{"id":7922,"date":"2024-06-30T18:01:01","date_gmt":"2024-06-30T10:01:01","guid":{"rendered":""},"modified":"2024-06-30T18:01:01","modified_gmt":"2024-06-30T10:01:01","slug":"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a","status":"publish","type":"post","link":"https:\/\/mushiming.com\/7922.html","title":{"rendered":"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a"},"content":{"rendered":"<p><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"display: none;\"> \n <path stroke-linecap=\"round\" d=\"M5,0 0,2.5 5,5z\" id=\"raphael-marker-block\" style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0);\"><\/path> \n<\/svg> <\/p>\n<div class=\"toc\">\n<h4>Mission-based Architecture for Swarm Composability<\/h4>\n<ul>\n<li>Mission-based Architecture for Swarm Composability (MASC)<\/li>\n<li>\n<ul>\n<li>\u6458\u8981<\/li>\n<li>1\uff0c\u80cc\u666f\u548c\u4ecb\u7ecd<\/li>\n<li>2\uff0c\u5f53\u524d\u7684\u65b9\u6cd5<\/li>\n<li>3 Mission-based Architecture for Swarm Composability (MASC)\u57fa\u4e8e\u4efb\u52a1\u7684\u6846\u67b6<\/li>\n<li>4 Conclusions and Future Work<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<h2>Mission-based Architecture for Swarm Composability (MASC)<\/h2>\n<p>\u57fa\u4e8e\u4efb\u52a1\u7684\u96c6\u7fa4\u7ec4\u5408\u6027\u6846\u67b6<\/p>\n<p>\u95f2\u7740\u6ca1\u4e8b\u501f\u52a9google\u7ffb\u8bd1\u4e86\u4e00\u7bc7\u96c6\u7fa4\u65b9\u9762\u7684\u6587\u7ae0<br \/> \u4e3b\u8981\u601d\u60f3\u662f\u7528\u81ea\u4e0a\u800c\u4e0b\u548c\u81ea\u4e0b\u800c\u4e0a\u5f62\u5f0f\u7ed3\u5408\u505a\u96c6\u7fa4\u3002\u628a\u8981\u5b8c\u6210\u7684\u4efb\u52a1\u5206\u6210\u4e0d\u540c\u7684\u9636\u6bb5\uff08\u98de\u884c\u524d\u51c6\u5907\u3001\u8fdb\u5165\u4efb\u52a1\u3001\u4efb\u52a1\u3001\u9000\u51fa\u4efb\u52a1\u3001\u4efb\u52a1\u540e\u6536\u5c3e\uff09\uff0c\u5728\u4e0d\u540c\u9636\u6bb5\u53c8\u5c06\u4efb\u52a1\u5206\u4e3a\u4e0d\u540c\u7684\u5c42\u6b21\u3002\u5728\u6838\u5fc3\u7684\u4efb\u52a1\u6bb5\uff08\u4f8b\u5982\u4fa6\u5bdf\u3001\u60c5\u62a5\u3001\u641c\u6551\uff09\uff0c\u63d0\u53d6\u4e00\u4e9b\u5178\u578b\u7684\u96c6\u7fa4\u7b56\u7565\uff08\u4f8b\u5982\u641c\u7d22\u3001\u8ddf\u8e2a\u3001\u653b\u51fb\uff09\uff0c\u5728\u7b56\u7565\u7684\u4e0b\u5c42\u4f7f\u7528\u96c6\u7fa4\u884c\u4e3a\uff08\u4f8b\u5982\u53d1\u5c04\u3001\u56de\u6536\u3001\u5230\u822a\u70b9\u3001\u7fa4\u4f53\u5206\u5f00\u3001\u7fa4\u4f53\u8fde\u63a5\u3001\u7fa4\u4f53\u907f\u969c\u7b49\uff09\uff0c\u518d\u96c6\u7fa4\u884c\u4e3a\u4e0b\u8bbe\u7f6e\u96c6\u7fa4\u7b97\u6cd5\u5c42\u3001\u6700\u4e0b\u5c42\u662f\u4e2a\u4f53\u65e0\u4eba\u673a\u5c42\u3002\u8be6\u89c1\u7b2c\u4e09\u7ae0\u3002<br \/> \u4e2a\u4eba\u89c9\u5f97\u6bcf\u4e00\u5c42\u90fd\u662f\u90fd\u505a\u6210\u5e93\u7684\u5f62\u5f0f\uff0c\u5c42\u4e0e\u5c42\u4e4b\u95f4\u53ef\u4ee5\u7531\u4e0a\u5230\u4e0b\u7684\u8c03\u7528\u3002\u505a\u597d\u4e86\u8fd9\u79cd\u6846\u67b6\uff0c\u53ef\u4ee5\u4e0a\u4e0b\u5e76\u884c\u5f00\u53d1\u7cfb\u7edf\u4e86\u3002<\/p>\n<h3>\u6458\u8981<\/h3>\n<p>Unmanned aerial vehicle (UAV) swarm design and mission application is a burgeoning area of research. The applications of swarm technology to unmanned systems are in the infancy of realization, although clear benefits from the enhanced capabilities can be easily envisioned for commercial and government missions including persistent search, long-term monitoring, sensor data collection, object retrieval, and offensive attack missions. Designing a UAV swarm architecture without considering the mission doctrine is imprudent, as is developing a mission doctrine without understanding the unique capabilities of swarm technology. This paper provides a literature review of current UAV swarm design architectures, most of which employ bottom-up methods with little consideration for an intended operational mission. The proposed top-down framework and modeling approach is focused on developing a modular UAV swarm playbook-type architecture by decomposing a subset of US Navy missions, and developing tactics that can be applied to those missions and support future missions. The framework proposed herein is derived from a consolidation of heuristics from the model-based systems engineering, robotics, human systems integration, biology, and computer science disciplines. The goal of this framework is to provide a standard methodology for designing and operating swarm unmanned systems that will meet the performance requirements for military missions. In this paper, an integrated framework for designing a UAV swarm system with swarm mission doctrine as a primary design factor is presented.<\/p>\n<p>\u65e0\u4eba\u673a\uff08UAV\uff09\u7fa4\u4f53\u8bbe\u8ba1\u548c\u4efb\u52a1\u5e94\u7528\u662f\u4e00\u4e2a\u65b0\u5174\u7684\u7814\u7a76\u9886\u57df\u3002\u7fa4\u4f53\u6280\u672f\u5728\u65e0\u4eba\u7cfb\u7edf\u4e2d\u7684\u5e94\u7528\u6b63\u5904\u4e8e\u5b9e\u73b0\u7684\u521d\u671f\u9636\u6bb5\uff0c\u5c3d\u7ba1\u53ef\u4ee5\u8f7b\u677e\u5730\u4e3a\u5546\u4e1a\u548c\u653f\u5e9c\u4efb\u52a1\u8bbe\u60f3\u589e\u5f3a\u529f\u80fd\u7684\u660e\u663e\u597d\u5904\uff0c\u5305\u62ec\u6301\u7eed\u641c\u7d22\uff0c\u957f\u671f\u76d1\u6d4b\uff0c\u4f20\u611f\u5668\u6570\u636e\u6536\u96c6\uff0c\u5bf9\u8c61\u68c0\u7d22\u548c\u653b\u51fb\u6027\u653b\u51fb\u4efb\u52a1\u3002\u5728\u4e0d\u8003\u8651\u4efb\u52a1\u539f\u5219\u7684\u60c5\u51b5\u4e0b\u8bbe\u8ba1\u65e0\u4eba\u673a\u7fa4\u4f53\u67b6\u6784\u662f\u4e0d\u8c28\u614e\u7684\uff0c\u6b63\u5982\u5728\u4e0d\u4e86\u89e3\u7fa4\u4f53\u6280\u672f\u7684\u72ec\u7279\u80fd\u529b\u7684\u60c5\u51b5\u4e0b\u5f00\u53d1\u4efb\u52a1\u539f\u5219\u4e00\u6837\u3002\u672c\u6587\u63d0\u4f9b\u4e86\u5f53\u524d\u65e0\u4eba\u673a\u7fa4\u4f53\u8bbe\u8ba1\u67b6\u6784\u7684\u6587\u732e\u7efc\u8ff0\uff0c\u5176\u4e2d\u5927\u591a\u6570\u91c7\u7528\u81ea\u4e0b\u800c\u4e0a\u7684\u65b9\u6cd5\uff0c\u5f88\u5c11\u8003\u8651\u9884\u671f\u7684\u64cd\u4f5c\u4efb\u52a1\u3002\u5efa\u8bae\u7684\u81ea\u4e0a\u800c\u4e0b\u7684\u6846\u67b6\u548c\u5efa\u6a21\u65b9\u6cd5\u4fa7\u91cd\u4e8e\u901a\u8fc7\u5206\u89e3\u7f8e\u56fd\u6d77\u519b\u4efb\u52a1\u7684\u4e00\u90e8\u5206\u6765\u5f00\u53d1\u6a21\u5757\u5316\u65e0\u4eba\u673a\u7fa4\u4f53\u6e38\u620f\u4e66\u578b\u67b6\u6784\uff0c\u5e76\u5236\u5b9a\u53ef\u5e94\u7528\u4e8e\u8fd9\u4e9b\u4efb\u52a1\u5e76\u652f\u6301\u672a\u6765\u4efb\u52a1\u7684\u7b56\u7565\u3002\u672c\u6587\u63d0\u51fa\u7684\u6846\u67b6\u6e90\u81ea\u57fa\u4e8e\u6a21\u578b\u7684\u7cfb\u7edf\u5de5\u7a0b\uff0c\u673a\u5668\u4eba\uff0c\u4eba\u4f53\u7cfb\u7edf\u96c6\u6210\uff0c\u751f\u7269\u5b66\u548c\u8ba1\u7b97\u673a\u79d1\u5b66\u5b66\u79d1\u7684\u542f\u53d1\u5f0f\u6574\u5408\u3002\u8be5\u6846\u67b6\u7684\u76ee\u6807\u662f\u4e3a\u8bbe\u8ba1\u548c\u8fd0\u884c\u6ee1\u8db3\u519b\u4e8b\u4efb\u52a1\u6027\u80fd\u8981\u6c42\u7684\u7fa4\u4f53\u65e0\u4eba\u7cfb\u7edf\u63d0\u4f9b\u6807\u51c6\u65b9\u6cd5\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u8bbe\u8ba1\u65e0\u4eba\u673a\u7fa4\u7cfb\u7edf\u7684\u96c6\u6210\u6846\u67b6\uff0c\u8be5\u7cfb\u7edf\u4ee5\u7fa4\u4f53\u4efb\u52a1\u539f\u5219\u4e3a\u4e3b\u8981\u8bbe\u8ba1\u56e0\u7d20\u3002<\/p>\n<h3>1\uff0c\u80cc\u666f\u548c\u4ecb\u7ecd<\/h3>\n<p>Interest in swarm technology has increased over the last two decades. Much of this interest can be attributed to the dynamic field of unmanned systems technology, which has been rapidly developing both in government and in the private sector. Unmanned system technology has expanded from physically hazardous, extended-endurance, highaltitude, military missions to agriculture, search and rescue, environmental research, and commercial missions1. Unmanned systems provide many advantages over manned systems. In the case of UAVs, they are less constrained by human factors such as crew rest, G-tolerance, environmental conditions, and comfort. Unmanned systems can be expendable and could have lower life-cycle costs than manned systems; however, low system reliability2 low technology readiness levels, large logistical footprints, and an ironically increased manpower requirement have marginalized cost advantages.<\/p>\n<p>\u5728\u8fc7\u53bb\u7684\u4e8c\u5341\u5e74\u4e2d\uff0c\u5bf9\u7fa4\u4f53\u6280\u672f\u7684\u5174\u8da3\u6709\u6240\u589e\u52a0\u3002 \u8fd9\u79cd\u5174\u8da3\u5f88\u5927\u7a0b\u5ea6\u4e0a\u5f52\u529f\u4e8e\u65e0\u4eba\u7cfb\u7edf\u6280\u672f\u7684\u52a8\u6001\u9886\u57df\uff0c\u65e0\u4eba\u7cfb\u7edf\u6280\u672f\u5728\u653f\u5e9c\u548c\u79c1\u8425\u90e8\u95e8\u90fd\u5728\u8fc5\u901f\u53d1\u5c55\u3002 \u65e0\u4eba\u7cfb\u7edf\u6280\u672f\u5df2\u4ece\u7269\u7406\u5371\u9669\uff0c\u5ef6\u957f\u8010\u529b\uff0c\u9ad8\u5ea6\uff0c\u519b\u4e8b\u4efb\u52a1\u6269\u5c55\u5230\u519c\u4e1a\uff0c\u641c\u7d22\u548c\u6551\u63f4\uff0c\u73af\u5883\u7814\u7a76\u548c\u5546\u4e1a\u4efb\u52a11\u3002 \u4e0e\u8f7d\u4eba\u7cfb\u7edf\u76f8\u6bd4\uff0c\u65e0\u4eba\u7cfb\u7edf\u5177\u6709\u8bb8\u591a\u4f18\u52bf\u3002 \u5728\u65e0\u4eba\u673a\u7684\u60c5\u51b5\u4e0b\uff0c\u5b83\u4eec\u53d7\u5230\u4eba\u5458\u56e0\u7d20\u7684\u9650\u5236\uff0c\u4f8b\u5982\u8239\u5458\u4f11\u606f\uff0cG\u8010\u53d7\u6027\uff0c\u73af\u5883\u6761\u4ef6\u548c\u8212\u9002\u6027\u3002 \u65e0\u4eba\u7cfb\u7edf\u53ef\u4ee5\u662f\u6d88\u8017\u6027\u7684\uff0c\u5e76\u4e14\u53ef\u4ee5\u6bd4\u6709\u4eba\u7cfb\u7edf\u5177\u6709\u66f4\u4f4e\u7684\u751f\u547d\u5468\u671f\u6210\u672c; \u7136\u800c\uff0c\u4f4e\u7cfb\u7edf\u53ef\u9760\u60272\uff0c\u4f4e\u6280\u672f\u51c6\u5907\u6c34\u5e73\uff0c\u5927\u91cf\u7684\u7269\u6d41\u8db3\u8ff9\u4ee5\u53ca\u5177\u6709\u8bbd\u523a\u610f\u5473\u7684\u589e\u52a0\u7684\u4eba\u529b\u9700\u6c42\u4f7f\u5f97\u6210\u672c\u4f18\u52bf\u8fb9\u7f18\u5316\u3002<\/p>\n<p>Swarm application to unmanned systems derives impetus from biology. Large numbers of individuals such as birds, fish, or insects may work together to accomplish useful tasks that cannot be completed by an individual or any group of non-cooperative individuals. Members of the swarm may be unintelligent and inefficient on an individual scale, yet inter-agent interactions produce emergent behavior that enables advantages such as robustness, flexibility, and scalability3. It is the local interactions among the agents and between the agents and their environment that may elicit beneficial collective behavior3,4. For this paper, a swarm is defined as a group of 10 or more individual, self-organized, homogeneous UAVs that perform a mission through local interactions under a decentralized control architecture5,6. The number 10 was chosen as a lower limit because it likely exceeds the number of UAVs that one human can directly control under operationally relevant workloads7,8. Decentralized control is stipulated because it permits scalability, robustness, and self-organization6.<\/p>\n<p>Swarm\u5e94\u7528\u4e8e\u65e0\u4eba\u7cfb\u7edf\u6e90\u4e8e\u751f\u7269\u5b66\u7684\u63a8\u52a8\u529b\u3002\u8bf8\u5982\u9e1f\u7c7b\uff0c\u9c7c\u7c7b\u6216\u6606\u866b\u4e4b\u7c7b\u7684\u5927\u91cf\u4e2a\u4f53\u53ef\u4ee5\u4e00\u8d77\u5de5\u4f5c\u4ee5\u5b8c\u6210\u65e0\u6cd5\u7531\u4e2a\u4eba\u6216\u4efb\u4f55\u975e\u5408\u4f5c\u4e2a\u4f53\u7ec4\u5b8c\u6210\u7684\u6709\u7528\u4efb\u52a1\u3002\u7fa4\u4f53\u6210\u5458\u5728\u4e2a\u4f53\u89c4\u6a21\u4e0a\u53ef\u80fd\u662f\u975e\u667a\u80fd\u548c\u4f4e\u6548\u7684\uff0c\u4f46\u4ee3\u7406\u95f4\u4ea4\u4e92\u4ea7\u751f\u7684\u7d27\u6025\u884c\u4e3a\u80fd\u591f\u5b9e\u73b0\u8bf8\u5982\u5065\u58ee\u6027\uff0c\u7075\u6d3b\u6027\u548c\u53ef\u6269\u5c55\u6027\u7b49\u4f18\u52bf3\u3002\u4ee3\u7406\u4eba\u4e4b\u95f4\u4ee5\u53ca\u4ee3\u7406\u4eba\u4e0e\u4ed6\u4eec\u7684\u73af\u5883\u4e4b\u95f4\u7684\u672c\u5730\u4e92\u52a8\u53ef\u80fd\u5f15\u53d1\u6709\u76ca\u7684\u96c6\u4f53\u884c\u4e3a3,4\u3002\u5bf9\u4e8e\u672c\u6587\uff0c\u7fa4\u4f53\u88ab\u5b9a\u4e49\u4e3a\u4e00\u7ec410\u4e2a\u6216\u66f4\u591a\u4e2a\u4f53\uff0c\u81ea\u7ec4\u7ec7\uff0c\u540c\u8d28\u7684\u65e0\u4eba\u673a\uff0c\u901a\u8fc7\u5206\u6563\u63a7\u5236\u67b6\u6784\u4e0b\u7684\u672c\u5730\u4ea4\u4e92\u6267\u884c\u4efb\u52a15,6\u3002\u6570\u5b5710\u88ab\u9009\u4e3a\u4e0b\u9650\uff0c\u56e0\u4e3a\u5b83\u53ef\u80fd\u8d85\u8fc7\u4e00\u4e2a\u4eba\u5728\u64cd\u4f5c\u76f8\u5173\u5de5\u4f5c\u8d1f\u8377\u4e0b\u53ef\u76f4\u63a5\u63a7\u5236\u7684\u65e0\u4eba\u673a\u6570\u91cf7,8\u3002\u89c4\u5b9a\u4e86\u5206\u6563\u63a7\u5236\uff0c\u56e0\u4e3a\u5b83\u5141\u8bb8\u53ef\u6269\u5c55\u6027\uff0c\u7a33\u5065\u6027\u548c\u81ea\u7ec4\u7ec76\u3002<\/p>\n<p>Swarm system behaviors are difficult to predict and control due to their complexity9. The number of possible behavioral interactions increases exponentially with the number of agents in the swarm9. Yet a key advantage of swarm technology is that the swarm is designed to be composed of simple, modular, identical components6. These homogeneous agents are not programmed for a specific role and do not operate under a centralized coordinating agent. Accordingly, the loss of an individual does not cause a significant decrease in system performance because another agent or agents can assume its duties. Thus, a homogeneous swarm can be adaptable, robust, and scalable3,6. Bachrach et al. eloquently underscored the need for a high-level language to orchestrate multi-agent operations by composing new behavior plans from existing lower-level programs9.<\/p>\n<p>\u7531\u4e8e\u5176\u590d\u6742\u6027\uff0c\u7fa4\u4f53\u7cfb\u7edf\u884c\u4e3a\u96be\u4ee5\u9884\u6d4b\u548c\u63a7\u52369\u3002 \u968f\u7740swarm9\u4e2d\u4ee3\u7406\u7684\u6570\u91cf\uff0c\u53ef\u80fd\u7684\u884c\u4e3a\u4ea4\u4e92\u7684\u6570\u91cf\u5448\u6307\u6570\u589e\u957f\u3002 \u7136\u800c\uff0c\u7fa4\u4f53\u6280\u672f\u7684\u4e00\u4e2a\u5173\u952e\u4f18\u52bf\u662f\u7fa4\u4f53\u8bbe\u8ba1\u7531\u7b80\u5355\uff0c\u6a21\u5757\u5316\uff0c\u76f8\u540c\u7684\u7ec4\u4ef6\u7ec4\u62106\u3002 \u8fd9\u4e9b\u540c\u8d28\u4ee3\u7406\u4e0d\u662f\u9488\u5bf9\u7279\u5b9a\u89d2\u8272\u7f16\u7a0b\u7684\uff0c\u4e0d\u5728\u96c6\u4e2d\u534f\u8c03\u4ee3\u7406\u4e0b\u8fd0\u884c\u3002 \u56e0\u6b64\uff0c\u4e2a\u4eba\u7684\u635f\u5931\u4e0d\u4f1a\u5bfc\u81f4\u7cfb\u7edf\u6027\u80fd\u7684\u663e\u7740\u964d\u4f4e\uff0c\u56e0\u4e3a\u53e6\u4e00\u4e2a\u6216\u591a\u4e2a\u4ee3\u7406\u53ef\u4ee5\u627f\u62c5\u5176\u804c\u8d23\u3002 \u56e0\u6b64\uff0c\u540c\u8d28\u7fa4\u53ef\u4ee5\u662f\u53ef\u9002\u5e94\u7684\uff0c\u7a33\u5065\u7684\u548c\u53ef\u6269\u5c55\u76843,6\u3002 Bachrach\u7b49\u3002 \u96c4\u8fa9\u5730\u5f3a\u8c03\u9700\u8981\u4e00\u79cd\u9ad8\u7ea7\u8bed\u8a00\u6765\u7f16\u6392\u591a\u4ee3\u7406\u64cd\u4f5c\uff0c\u65b9\u6cd5\u662f\u4ece\u73b0\u6709\u7684\u4f4e\u7ea7\u7a0b\u5e8f\u4e2d\u7f16\u5199\u65b0\u7684\u884c\u4e3a\u8ba1\u52129\u3002<\/p>\n<h3>2\uff0c\u5f53\u524d\u7684\u65b9\u6cd5<\/h3>\n<p>Bottom-up modeling approaches focus on assembling sub-components of systems to build more complex systems. Agent-based modeling (ABM), finite state machines and Petri Nets are bottom-up modeling methods frequently used in swarm system design. Bottom-up models are advantageous from a modularity and composability perspective, but they often risk failing to meet higher-level system requirements if design begins before a higher-level system architecture is established. Consequently, combining bottom-up and top-down models is a software development heuristic10.<br \/> \u81ea\u4e0b\u800c\u4e0a\u7684\u5efa\u6a21\u65b9\u6cd5\u4fa7\u91cd\u4e8e\u7ec4\u88c5\u7cfb\u7edf\u7684\u5b50\u7ec4\u4ef6\u4ee5\u6784\u5efa\u66f4\u590d\u6742\u7684\u7cfb\u7edf\u3002 \u57fa\u4e8e\u4ee3\u7406\u7684\u5efa\u6a21\uff08ABM\uff09\uff0c\u6709\u9650\u72b6\u6001\u673a\u548cPetri\u7f51\u662f\u5728\u7fa4\u7cfb\u7edf\u8bbe\u8ba1\u4e2d\u7ecf\u5e38\u4f7f\u7528\u7684\u81ea\u4e0b\u800c\u4e0a\u5efa\u6a21\u65b9\u6cd5\u3002 \u4ece\u6a21\u5757\u5316\u548c\u53ef\u7ec4\u5408\u6027\u7684\u89d2\u5ea6\u6765\u770b\uff0c\u81ea\u4e0b\u800c\u4e0a\u7684\u6a21\u578b\u662f\u6709\u5229\u7684\uff0c\u4f46\u5982\u679c\u5728\u5efa\u7acb\u66f4\u9ad8\u7ea7\u522b\u7684\u7cfb\u7edf\u67b6\u6784\u4e4b\u524d\u5f00\u59cb\u8bbe\u8ba1\uff0c\u5b83\u4eec\u901a\u5e38\u6709\u53ef\u80fd\u65e0\u6cd5\u6ee1\u8db3\u66f4\u9ad8\u7ea7\u522b\u7684\u7cfb\u7edf\u8981\u6c42\u3002 \u56e0\u6b64\uff0c\u81ea\u4e0b\u800c\u4e0a\u548c\u81ea\u4e0a\u800c\u4e0b\u6a21\u578b\u76f8\u7ed3\u5408\u662f\u4e00\u79cd\u8f6f\u4ef6\u5f00\u53d1\u542f\u53d1\u5f0f10\u3002<\/p>\n<p>Modeling agent-level interactions can provide valuable information regarding the emergent behavior inherent to the complex adaptive system (CAS). ABM is a commonly used approach for modeling a CAS as a group of autonomous agents who make decisions individually based on their assessment of the environment and in accordance with a rule set11,12. McCune et al.13 used ABM to investigate command and control of a UAV swarm, Bonabeau simulated human systems using ABM methods11, and Munoz14 studied defensive UAV swarm employment using ABM. The emergent behavior of a swarm system is a critical attribute to consider when designing a swarm system, developing swarm tactics, or devising an assessment methodology for a swarm system.<\/p>\n<p>728\/5000<br \/> \u5efa\u6a21\u4ee3\u7406\u7ea7\u522b\u7684\u4ea4\u4e92\u53ef\u4ee5\u63d0\u4f9b\u6709\u5173\u590d\u6742\u81ea\u9002\u5e94\u7cfb\u7edf\uff08CAS\uff09\u56fa\u6709\u7684\u7d27\u6025\u884c\u4e3a\u7684\u6709\u4ef7\u503c\u4fe1\u606f\u3002 ABM\u662f\u4e00\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u7528\u4e8e\u5c06CAS\u5efa\u6a21\u4e3a\u4e00\u7ec4\u81ea\u4e3b\u4ee3\u7406\uff0c\u8fd9\u4e9b\u4ee3\u7406\u6839\u636e\u5176\u5bf9\u73af\u5883\u7684\u8bc4\u4f30\u5e76\u6839\u636e\u89c4\u5219\u96c611,12\u5355\u72ec\u505a\u51fa\u51b3\u7b56\u3002 McCune\u7b49\u4eba13\u4f7f\u7528ABM\u6765\u7814\u7a76\u65e0\u4eba\u673a\u7fa4\u7684\u547d\u4ee4\u548c\u63a7\u5236\uff0cBonabeau\u4f7f\u7528ABM\u65b9\u6cd5\u6a21\u62df\u4eba\u7c7b\u7cfb\u7edf11\uff0cMunoz14\u4f7f\u7528ABM\u7814\u7a76\u9632\u5fa1\u6027\u65e0\u4eba\u673a\u7fa4\u4f53\u5c31\u4e1a\u3002 \u7fa4\u4f53\u7cfb\u7edf\u7684\u7d27\u6025\u884c\u4e3a\u662f\u8bbe\u8ba1\u7fa4\u4f53\u7cfb\u7edf\uff0c\u5f00\u53d1\u7fa4\u4f53\u7b56\u7565\u6216\u8bbe\u8ba1\u7fa4\u4f53\u7cfb\u7edf\u8bc4\u4f30\u65b9\u6cd5\u65f6\u9700\u8981\u8003\u8651\u7684\u5173\u952e\u5c5e\u6027\u3002<\/p>\n<p>Finite state machines (FSM) have been used to model multi-vehicle autonomous, unmanned system architectures15,16,17,18. Within an FSM architecture, each agent operates within one of several defined states at a given time with trigger events causing the agent to transition between states precipitated by environmental conditions it senses or events it encounters. This type of structure is applicable in developing military swarm systems as the states and triggers can be defined deterministically, which is necessary for high-risk mission events. Weiskopf et al. demonstrated a control architecture based on a library of basic tasks to support a search and track reference mission18. Conversely, there may be other mission events, such as covert searching, in which some bounded degree of unpredictability is desired. In those cases, probabilistic finite state machines (PFSM), can be used to allow for different behaviors within a state or by allowing multiple transitions between states17. Using this architecture, the transition<br \/> probabilities can remain fixed or change over time19. PFSM have been used to produce collective behaviors such as task allocation and aggregation15.<br \/> \u6709\u9650\u72b6\u6001\u673a\uff08FSM\uff09\u5df2\u88ab\u7528\u4e8e\u6a21\u62df\u591a\u8f66\u8f86\u81ea\u4e3b\uff0c\u65e0\u4eba\u7cfb\u7edf\u67b6\u678415,16,17,18\u3002\u5728FSM\u67b6\u6784\u5185\uff0c\u6bcf\u4e2a\u4ee3\u7406\u5728\u7ed9\u5b9a\u65f6\u95f4\u5185\u5728\u82e5\u5e72\u5b9a\u4e49\u72b6\u6001\u4e4b\u4e00\u5185\u64cd\u4f5c\uff0c\u89e6\u53d1\u4e8b\u4ef6\u4f7f\u4ee3\u7406\u5728\u5176\u611f\u77e5\u7684\u73af\u5883\u6761\u4ef6\u6216\u5176\u9047\u5230\u7684\u4e8b\u4ef6\u5f15\u53d1\u7684\u72b6\u6001\u4e4b\u95f4\u8f6c\u6362\u3002\u8fd9\u79cd\u7c7b\u578b\u7684\u7ed3\u6784\u9002\u7528\u4e8e\u5f00\u53d1\u519b\u4e8b\u7fa4\u4f53\u7cfb\u7edf\uff0c\u56e0\u4e3a\u53ef\u4ee5\u786e\u5b9a\u6027\u5730\u5b9a\u4e49\u72b6\u6001\u548c\u89e6\u53d1\u5668\uff0c\u8fd9\u5bf9\u4e8e\u9ad8\u98ce\u9669\u4efb\u52a1\u4e8b\u4ef6\u662f\u5fc5\u8981\u7684\u3002 Weiskopf\u7b49\u4eba\u3002\u5c55\u793a\u4e86\u57fa\u4e8e\u57fa\u672c\u4efb\u52a1\u5e93\u7684\u63a7\u5236\u67b6\u6784\uff0c\u4ee5\u652f\u6301\u641c\u7d22\u548c\u8ddf\u8e2a\u53c2\u8003\u4efb\u52a118\u3002\u76f8\u53cd\uff0c\u53ef\u80fd\u5b58\u5728\u5176\u4ed6\u4efb\u52a1\u4e8b\u4ef6\uff0c\u4f8b\u5982\u9690\u853d\u641c\u7d22\uff0c\u5176\u4e2d\u9700\u8981\u4e00\u4e9b\u6709\u9650\u7a0b\u5ea6\u7684\u4e0d\u53ef\u9884\u6d4b\u6027\u3002\u5728\u8fd9\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6982\u7387\u6709\u9650\u72b6\u6001\u673a\uff08PFSM\uff09\u53ef\u7528\u4e8e\u5141\u8bb8\u72b6\u6001\u5185\u7684\u4e0d\u540c\u884c\u4e3a\u6216\u5141\u8bb8\u72b6\u6001\u4e4b\u95f4\u7684\u591a\u4e2a\u8f6c\u636217\u3002\u4f7f\u7528\u8fd9\u79cd\u67b6\u6784\uff0c\u8f6c\u6362\u6982\u7387\u53ef\u4ee5\u4fdd\u6301\u56fa\u5b9a\u6216\u968f\u65f6\u95f4\u53d8\u531619\u3002 PFSM\u5df2\u88ab\u7528\u4e8e\u4ea7\u751f\u96c6\u4f53\u884c\u4e3a\uff0c\u5982\u4efb\u52a1\u5206\u914d\u548c\u805a\u540815\u3002<br \/> Behavior-based design, in which the individual behavior of each agent is developed iteratively until the desired swarm behavior is acquired, has been a typical design method in swarm robotics, in part due to the influence of biological swarms. The term behavior is commonly used in robotics literature to describe the actions being performed by robots, or agents. Behaviors apply to individuals and environments, as well as to groups, which are often called collective behaviors. Behaviors may also be categorized as higher-level abstract behaviors and lower-level primitive behaviors, or simply primitives19. The term primitives is borrowed from the computer science discipline and functions similarly in robotics literature; they act as building blocks for programming higher-level functions. A seminal behavior-based design is Brooks\u2019 subsumption architecture which uses a layering approach for controlling systems and incorporates augmented FSM processors for managing inputs and outputs20. A key contribution of Brooks\u2019 work was that he partitioned the robot control problem into behaviors rather than into functional modules. As a result, his hierarchy of layered behaviors allows higher levels of behaviors to subsume lower layers of less complex behavior. Nicolescu and Matari\u0107 presented a behavior-based hierarchical architecture for robots in which reusable primitive behaviors support a library of abstract behaviors21. The proposed MASC framework presents an approach similar to Nicolescu and Matari\u0107\u2019s, but inspired from a navy doctrinal perspective, that may provide applications across the gamut of prospective military swarm operations to enhance mission suitability of future swarm systems<br \/> 1706\/5000<br \/> \u57fa\u4e8e\u884c\u4e3a\u7684\u8bbe\u8ba1\uff0c\u5176\u4e2d\u6bcf\u4e2a\u4ee3\u7406\u7684\u4e2a\u4f53\u884c\u4e3a\u88ab\u8fed\u4ee3\u5730\u5f00\u53d1\u76f4\u5230\u83b7\u5f97\u671f\u671b\u7684\u7fa4\u4f53\u884c\u4e3a\uff0c\u5df2\u7ecf\u662f\u7fa4\u4f53\u673a\u5668\u4eba\u4e2d\u7684\u5178\u578b\u8bbe\u8ba1\u65b9\u6cd5\uff0c\u90e8\u5206\u5730\u7531\u4e8e\u751f\u7269\u7fa4\u7684\u5f71\u54cd\u3002\u672f\u8bed\u884c\u4e3a\u901a\u5e38\u7528\u4e8e\u673a\u5668\u4eba\u6587\u732e\u4e2d\uff0c\u4ee5\u63cf\u8ff0\u7531\u673a\u5668\u4eba\u6216\u4ee3\u7406\u6267\u884c\u7684\u52a8\u4f5c\u3002\u884c\u4e3a\u9002\u7528\u4e8e\u4e2a\u4eba\u548c\u73af\u5883\uff0c\u4ee5\u53ca\u901a\u5e38\u79f0\u4e3a\u96c6\u4f53\u884c\u4e3a\u7684\u7fa4\u4f53\u3002\u884c\u4e3a\u4e5f\u53ef\u4ee5\u5f52\u7c7b\u4e3a\u66f4\u9ad8\u7ea7\u522b\u7684\u62bd\u8c61\u884c\u4e3a\u548c\u66f4\u4f4e\u7ea7\u522b\u7684\u539f\u59cb\u884c\u4e3a\uff0c\u6216\u8005\u4ec5\u4ec5\u662f\u539f\u59cb\u884c\u4e3a19\u3002\u539f\u8bed\u8fd9\u4e2a\u672f\u8bed\u6765\u81ea\u8ba1\u7b97\u673a\u79d1\u5b66\u5b66\u79d1\uff0c\u5728\u673a\u5668\u4eba\u6587\u5b66\u4e2d\u4e5f\u6709\u7c7b\u4f3c\u7684\u529f\u80fd;\u5b83\u4eec\u5145\u5f53\u7f16\u7a0b\u66f4\u9ad8\u7ea7\u51fd\u6570\u7684\u6784\u5efa\u5757\u3002\u57fa\u4e8e\u884c\u4e3a\u7684\u5f00\u521b\u6027\u8bbe\u8ba1\u662fBrooks\u7684\u5305\u5bb9\u4f53\u7cfb\u7ed3\u6784\uff0c\u5b83\u4f7f\u7528\u5206\u5c42\u65b9\u6cd5\u6765\u63a7\u5236\u7cfb\u7edf\uff0c\u5e76\u91c7\u7528\u589e\u5f3a\u578bFSM\u5904\u7406\u5668\u6765\u7ba1\u7406\u8f93\u5165\u548c\u8f93\u51fa20\u3002\u5e03\u9c81\u514b\u65af\u7684\u5de5\u4f5c\u7684\u4e00\u4e2a\u5173\u952e\u8d21\u732e\u662f\u4ed6\u5c06\u673a\u5668\u4eba\u63a7\u5236\u95ee\u9898\u5212\u5206\u4e3a\u884c\u4e3a\u800c\u4e0d\u662f\u529f\u80fd\u6a21\u5757\u3002\u56e0\u6b64\uff0c\u4ed6\u7684\u5206\u5c42\u884c\u4e3a\u5c42\u6b21\u7ed3\u6784\u5141\u8bb8\u66f4\u9ad8\u7ea7\u522b\u7684\u884c\u4e3a\u5305\u542b\u66f4\u4f4e\u5c42\u6b21\u7684\u590d\u6742\u884c\u4e3a\u3002 Nicolescu\u548cMatari\u0107\u4e3a\u673a\u5668\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u884c\u4e3a\u7684\u5206\u5c42\u67b6\u6784\uff0c\u5176\u4e2d\u53ef\u91cd\u7528\u7684\u539f\u59cb\u884c\u4e3a\u652f\u6301\u62bd\u8c61\u884c\u4e3a\u5e9321\u3002\u62df\u8bae\u7684MASC\u6846\u67b6\u63d0\u51fa\u4e86\u7c7b\u4f3c\u4e8eNicolescu\u548cMatari\u0107\u7684\u65b9\u6cd5\uff0c\u4f46\u53d7\u5230\u6d77\u519b\u7406\u8bba\u89c2\u70b9\u7684\u542f\u53d1\uff0c\u53ef\u80fd\u63d0\u4f9b\u8de8\u8d8a\u672a\u6765\u519b\u4e8b\u7fa4\u4f53\u64cd\u4f5c\u7684\u5e94\u7528\uff0c\u4ee5\u589e\u5f3a\u672a\u6765\u7fa4\u4f53\u7cfb\u7edf\u7684\u4efb\u52a1\u9002\u7528\u6027<\/p>\n<p>Top-down methods are surfacing to augment the bottom-up models and expedite their progression from labs and simulations to operationally useful systems. Model-based composable architectures, such as playbooks, merge the benefits of top-down and bottom-up approaches and promote reusable designs. DeLoach et al. developed the MultiAgent Systems Engineering methodology for analyzing, designing, and producing heterogeneous multi-agent systems22. Their seven-step method uses graphically based models to define the agents and interfaces. Brambilla et al. proposed a four-phase, property-driven, top-down design method that formally describes the features of the system the designer wants to realize23.<br \/> \u81ea\u4e0a\u800c\u4e0b\u7684\u65b9\u6cd5\u6b63\u5728\u6d6e\u51fa\u6c34\u9762\uff0c\u4ee5\u589e\u5f3a\u81ea\u4e0b\u800c\u4e0a\u7684\u6a21\u578b\uff0c\u52a0\u5feb\u4ece\u5b9e\u9a8c\u5ba4\u548c\u6a21\u62df\u5230\u64cd\u4f5c\u4e0a\u6709\u7528\u7684\u7cfb\u7edf\u7684\u53d1\u5c55\u3002 \u57fa\u4e8e\u6a21\u578b\u7684\u53ef\u7ec4\u5408\u4f53\u7cfb\u7ed3\u6784\uff08\u5982playbooks\uff09\u878d\u5408\u4e86\u81ea\u4e0a\u800c\u4e0b\u548c\u81ea\u4e0b\u800c\u4e0a\u65b9\u6cd5\u7684\u4f18\u70b9\uff0c\u5e76\u4fc3\u8fdb\u4e86\u53ef\u91cd\u7528\u7684\u8bbe\u8ba1\u3002 DeLoach\u7b49 \u5f00\u53d1\u4e86MultiAgent\u7cfb\u7edf\u5de5\u7a0b\u65b9\u6cd5\uff0c\u7528\u4e8e\u5206\u6790\uff0c\u8bbe\u8ba1\u548c\u751f\u6210\u5f02\u6784\u591a\u4ee3\u7406\u7cfb\u7edf\u3002 \u4ed6\u4eec\u7684\u4e03\u6b65\u6cd5\u4f7f\u7528\u57fa\u4e8e\u56fe\u5f62\u7684\u6a21\u578b\u6765\u5b9a\u4e49\u4ee3\u7406\u548c\u63a5\u53e3\u3002 Brambilla\u7b49 \u63d0\u51fa\u4e86\u4e00\u79cd\u56db\u9636\u6bb5\uff0c\u5c5e\u6027\u9a71\u52a8\uff0c\u81ea\u4e0a\u800c\u4e0b\u7684\u8bbe\u8ba1\u65b9\u6cd5\uff0c\u6b63\u5f0f\u63cf\u8ff0\u4e86\u8bbe\u8ba1\u8005\u60f3\u8981\u5b9e\u73b0\u7684\u7cfb\u7edf\u7684\u7279\u5f8123\u3002<\/p>\n<p>2.2.1. Playbooks<br \/> The idea of using a playbook, or collection of pre-defined tactics or action plans for multi-robot unmanned systems is not new and has been used in both UAVs24 and UGVs25. A playbook can be described as a library of action plans that enable the operator to manage the agents\u2019 behavior at multiple levels and tailor the level of autonomy to the mission26. Rather than controlling each sequence of actions, the catalog of pre-defined behaviors simplifies user control, reduces necessary communications, and synchronizes agent tasking26. A good playbook should be constructed at a high enough level of abstraction such that action plans (tactics in the proposed MASC) are common to several missions, and can be tailored for a variety of uses by adjusting tune-able parameters. A benefit to using a playbook architecture for UAVs is the possibility to extend the pool of potential operators beyond specifically trained UAV operators.<br \/> \u5bf9\u591a\u673a\u5668\u4eba\u65e0\u4eba\u7cfb\u7edf\u4f7f\u7528\u5267\u672c\u6216\u9884\u5b9a\u4e49\u6218\u672f\u6216\u884c\u52a8\u8ba1\u5212\u7684\u96c6\u5408\u7684\u60f3\u6cd5\u5e76\u4e0d\u65b0\u9c9c\uff0c\u5e76\u4e14\u5df2\u7ecf\u5728UAV24\u548cUGVs25\u4e2d\u4f7f\u7528\u3002 \u5267\u672c\u53ef\u4ee5\u88ab\u63cf\u8ff0\u4e3a\u4e00\u4e2a\u884c\u52a8\u8ba1\u5212\u5e93\uff0c\u4f7f\u64cd\u4f5c\u8005\u80fd\u591f\u5728\u591a\u4e2a\u5c42\u9762\u7ba1\u7406\u4ee3\u7406\u4eba\u7684\u884c\u4e3a\uff0c\u5e76\u4e3a\u4efb\u52a126\u5b9a\u5236\u81ea\u6cbb\u6c34\u5e73\u3002 \u9884\u5b9a\u4e49\u884c\u4e3a\u7684\u76ee\u5f55\u4e0d\u662f\u63a7\u5236\u6bcf\u4e2a\u52a8\u4f5c\u5e8f\u5217\uff0c\u800c\u662f\u7b80\u5316\u7528\u6237\u63a7\u5236\uff0c\u51cf\u5c11\u5fc5\u8981\u7684\u901a\u4fe1\uff0c\u5e76\u540c\u6b65\u4ee3\u7406\u4efb\u52a126\u3002 \u5e94\u8be5\u5728\u8db3\u591f\u9ad8\u7684\u62bd\u8c61\u5c42\u6b21\u4e0a\u6784\u5efa\u4e00\u4e2a\u597d\u7684\u5267\u672c\uff0c\u4ee5\u4fbf\u884c\u52a8\u8ba1\u5212\uff08\u62df\u8bae\u7684MASC\u4e2d\u7684\u7b56\u7565\uff09\u5bf9\u4e8e\u51e0\u4e2a\u4efb\u52a1\u662f\u901a\u7528\u7684\uff0c\u5e76\u4e14\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u53ef\u8c03\u53c2\u6570\u6765\u9488\u5bf9\u5404\u79cd\u7528\u9014\u8fdb\u884c\u5b9a\u5236\u3002 \u4f7f\u7528\u65e0\u4eba\u673a\u7684\u5267\u672c\u67b6\u6784\u7684\u597d\u5904\u662f\u53ef\u4ee5\u5c06\u6f5c\u5728\u64cd\u4f5c\u8005\u7684\u8303\u56f4\u6269\u5c55\u5230\u7ecf\u8fc7\u4e13\u95e8\u8bad\u7ec3\u7684\u65e0\u4eba\u673a\u64cd\u4f5c\u8005\u4e4b\u5916\u3002<\/p>\n<p>Playbooks may provide a means to improve human-machine teaming, an important factor for operating swarm systems. Squire et al. studied the effects of various multi-vehicle control architectures on human performance27. This work was built upon Parasuraman et al.'s experiments that used a simplified version of the \u201cPlaybook\u201d interface in the RoboFlag multi-vehicle simulation environment28. The simplified RoboFlag Playbook used a hierarchical task model to task robots to perform various functions under temporal and conditional constraints, using three architectures of different automation levels. The authors evaluated the effects of these three architectures on human performance under defensive, offensive, and mixed conditions. The results of this study suggest that a playbook type architecture provides operators with the requisite flexibility of balancing manual control and cognitive workload when managing unforeseeable conditions28. Furthermore, the playbook system architecture looks to be promising for overcoming the \u201csingle robot parenting\u201d paradigm.<br \/> Playbooks\u53ef\u4ee5\u63d0\u4f9b\u4e00\u79cd\u6539\u8fdb\u4eba\u673a\u7ec4\u5408\u7684\u65b9\u6cd5\uff0c\u8fd9\u662f\u64cd\u4f5c\u7fa4\u7cfb\u7edf\u7684\u4e00\u4e2a\u91cd\u8981\u56e0\u7d20\u3002 Squire\u7b49\u4eba\u3002 \u7814\u7a76\u4e86\u5404\u79cd\u591a\u8f66\u8f86\u63a7\u5236\u67b6\u6784\u5bf9\u4eba\u4f53\u8868\u73b0\u7684\u5f71\u54cd27\u3002 \u8fd9\u9879\u5de5\u4f5c\u5efa\u7acb\u5728Parasuraman\u7b49\u4eba\u7684\u5b9e\u9a8c\u57fa\u7840\u4e4b\u4e0a\uff0c\u8be5\u5b9e\u9a8c\u5728RoboFlag\u591a\u8f66\u8f86\u4eff\u771f\u73af\u5883\u4e2d\u4f7f\u7528\u4e86\u201cPlaybook\u201d\u754c\u9762\u7684\u7b80\u5316\u7248\u672c28\u3002 \u7b80\u5316\u7684RoboFlag Playbook\u4f7f\u7528\u5206\u5c42\u4efb\u52a1\u6a21\u578b\uff0c\u4f7f\u4efb\u52a1\u673a\u5668\u4eba\u5728\u65f6\u95f4\u548c\u6761\u4ef6\u7ea6\u675f\u4e0b\u4f7f\u7528\u4e09\u79cd\u4e0d\u540c\u81ea\u52a8\u5316\u7ea7\u522b\u7684\u4f53\u7cfb\u7ed3\u6784\u6267\u884c\u5404\u79cd\u529f\u80fd\u3002 \u4f5c\u8005\u8bc4\u4f30\u4e86\u8fd9\u4e09\u79cd\u67b6\u6784\u5728\u9632\u5fa1\uff0c\u8fdb\u653b\u548c\u6df7\u5408\u6761\u4ef6\u4e0b\u5bf9\u4eba\u7c7b\u8868\u73b0\u7684\u5f71\u54cd\u3002\u8fd9\u9879\u7814\u7a76\u7684\u7ed3\u679c\u8868\u660e\uff0c\u5728\u7ba1\u7406\u4e0d\u53ef\u9884\u89c1\u7684\u6761\u4ef6\u65f6\uff0cPlaybook\u7c7b\u578b\u7684\u4f53\u7cfb\u7ed3\u6784\u4e3a\u64cd\u4f5c\u5458\u63d0\u4f9b\u4e86\u5e73\u8861\u624b\u52a8\u63a7\u5236\u548c\u8ba4\u77e5\u5de5\u4f5c\u8d1f\u8f7d\u7684\u5fc5\u8981\u7075\u6d3b\u602728\u3002 \u6b64\u5916\uff0c\u5267\u672c\u7cfb\u7edf\u67b6\u6784\u4f3c\u4e4e\u6709\u671b\u514b\u670d\u201c\u5355\u4e00\u673a\u5668\u4eba\u80b2\u513f\u201d\u8303\u5f0f\u3002<\/p>\n<p>Browning et al.'s \u201cSTP\u201d (skills, tactics, and plays) multi-robot architecture is particularly applicable to this research because it was designed to control a team of autonomous robots in an adversarial environment: RoboCup robot soccer29. In the STP architecture, skills are the low-level control algorithms that support the tactics, which in turn compose the higher-level plays. In this architecture, tactics and skills can be detached from plays to support a hierarchical control structure for operating individual heterogeneous robots that perform different roles.<br \/> Goldman et al. addressed the problem of over-constrained missions in which the system cannot meet the requests of the operator, due to temporal, geographic or other constraints24. They improved the Smart Information Flow Technology (SIFT) Playbook-enhanced Variable Autonomy Control System (PVACS) project by adding a \u201cbest effort\u201d planning mode, using cost-based optimization, to relax constraints and provide viable alternative play options24. The capability to automate modifications to plays extends the potential for multi-vehicle unmanned systems to be operated by users who may not be specifically trained for the UAV\u2019s capabilities.<br \/> In 2017, DARPA announced a program called OFFSET in order to advance swarm technology by focusing on human-swarm teaming and swarm autonomy within a realistic gaming environment30. Their method takes a hierarchical approach to the swarm framework which is composed of a mission, tactics, primitives, and supporting algorithms30. This program focuses exclusively on the urban operational environment, with a goal of building a playbook of tactics to support the framework.<br \/> Browning\u7b49\u4eba\u7684\u201cSTP\u201d\uff08\u6280\u80fd\uff0c\u6218\u672f\u548c\u6e38\u620f\uff09\u591a\u673a\u5668\u4eba\u4f53\u7cfb\u7ed3\u6784\u7279\u522b\u9002\u7528\u4e8e\u8fd9\u9879\u7814\u7a76\uff0c\u56e0\u4e3a\u5b83\u65e8\u5728\u63a7\u5236\u5bf9\u6297\u73af\u5883\u4e2d\u7684\u81ea\u4e3b\u673a\u5668\u4eba\u56e2\u961f\uff1aRoboCup\u673a\u5668\u4eba\u8db3\u740329\u3002\u5728STP\u67b6\u6784\u4e2d\uff0c\u6280\u80fd\u662f\u652f\u6301\u6218\u672f\u7684\u4f4e\u7ea7\u63a7\u5236\u7b97\u6cd5\uff0c\u800c\u540e\u8005\u53c8\u6784\u6210\u4e86\u66f4\u9ad8\u7ea7\u522b\u7684\u6e38\u620f\u3002\u5728\u8fd9\u79cd\u4f53\u7cfb\u7ed3\u6784\u4e2d\uff0c\u7b56\u7565\u548c\u6280\u80fd\u53ef\u4ee5\u4e0e\u6e38\u620f\u5206\u79bb\uff0c\u4ee5\u652f\u6301\u7528\u4e8e\u64cd\u4f5c\u6267\u884c\u4e0d\u540c\u89d2\u8272\u7684\u5404\u4e2a\u5f02\u6784\u673a\u5668\u4eba\u7684\u5206\u5c42\u63a7\u5236\u7ed3\u6784\u3002<br \/> Goldman\u7b49\u4eba\u3002\u89e3\u51b3\u4e86\u8fc7\u5ea6\u7ea6\u675f\u7684\u4efb\u52a1\u95ee\u9898\uff0c\u7531\u4e8e\u65f6\u95f4\uff0c\u5730\u7406\u6216\u5176\u4ed6\u9650\u5236\uff0c\u7cfb\u7edf\u65e0\u6cd5\u6ee1\u8db3\u8fd0\u8425\u5546\u7684\u8981\u6c4224\u3002\u4ed6\u4eec\u901a\u8fc7\u6dfb\u52a0\u201c\u5c3d\u529b\u800c\u4e3a\u201d\u8ba1\u5212\u6a21\u5f0f\uff0c\u4f7f\u7528\u57fa\u4e8e\u6210\u672c\u7684\u4f18\u5316\uff0c\u653e\u5bbd\u7ea6\u675f\u5e76\u63d0\u4f9b\u53ef\u884c\u7684\u66ff\u4ee3\u6e38\u620f\u9009\u987924\uff0c\u6539\u8fdb\u4e86\u667a\u80fd\u4fe1\u606f\u6d41\u6280\u672f\uff08SIFT\uff09Playbook\u589e\u5f3a\u578b\u53ef\u53d8\u81ea\u6cbb\u63a7\u5236\u7cfb\u7edf\uff08PVACS\uff09\u9879\u76ee\u3002\u81ea\u52a8\u4fee\u6539\u6e38\u620f\u7684\u80fd\u529b\u6269\u5c55\u4e86\u591a\u8f66\u8f86\u65e0\u4eba\u7cfb\u7edf\u7531\u672a\u7ecf\u8fc7UAV\u4e13\u95e8\u8bad\u7ec3\u7684\u7528\u6237\u64cd\u4f5c\u7684\u53ef\u80fd\u6027\u3002<br \/> 2017\u5e74\uff0cDARPA\u5ba3\u5e03\u4e86\u4e00\u9879\u540d\u4e3aOFFSET\u7684\u8ba1\u5212\uff0c\u65e8\u5728\u901a\u8fc7\u5728\u771f\u5b9e\u7684\u6e38\u620f\u73af\u5883\u4e2d\u4e13\u6ce8\u4e8e\u4eba\u7fa4\u805a\u96c6\u548c\u7fa4\u4f53\u81ea\u6cbb\u6765\u63a8\u8fdb\u7fa4\u4f53\u6280\u672f30\u3002\u4ed6\u4eec\u7684\u65b9\u6cd5\u91c7\u7528\u5206\u5c42\u65b9\u6cd5\u6765\u5904\u7406\u7fa4\u4f53\u6846\u67b6\uff0c\u8be5\u6846\u67b6\u7531\u4efb\u52a1\uff0c\u7b56\u7565\uff0c\u539f\u8bed\u548c\u652f\u6301\u7b97\u6cd5\u7ec4\u621030\u3002\u8be5\u8ba1\u5212\u4e13\u6ce8\u4e8e\u57ce\u5e02\u8fd0\u8425\u73af\u5883\uff0c\u76ee\u6807\u662f\u5efa\u7acb\u4e00\u4e2a\u652f\u6301\u6846\u67b6\u7684\u6218\u672f\u624b\u518c\u3002<\/p>\n<h3>3 Mission-based Architecture for Swarm Composability (MASC)\u57fa\u4e8e\u4efb\u52a1\u7684\u6846\u67b6<\/h3>\n<ol start=\"3\">\n<li>Mission-based Architecture for Swarm Composability (MASC)<br \/> Terminology for swarm system functions varies depending on the academic field. A standard taxonomy for swarm system functions that is relatable across academic disciplines would facilitate architecture design. This research proposes a top-down methodology to integrate mission doctrine and system architecture design for a UAV swarm, and aims to identify a taxonomy for swarm unmanned system architecture design. MASC is focused on developing a swarm playbook-type architecture by decomposing a subset of US Navy missions, and developing tactics that can be applied to those missions and support future missions. Fig. 1 shows the decomposition of the proposed swarm operational framework, MASC. The focus of this research is on the tactic and play levels of the architecture.<br \/> \u7fa4\u4f53\u7cfb\u7edf\u529f\u80fd\u7684\u672f\u8bed\u56e0\u5b66\u672f\u9886\u57df\u800c\u5f02\u3002 \u9488\u5bf9\u7fa4\u4f53\u7cfb\u7edf\u529f\u80fd\u7684\u6807\u51c6\u5206\u7c7b\u6cd5\u53ef\u4ee5\u8de8\u5b66\u79d1\u8fdb\u884c\u5173\u8054\uff0c\u8fd9\u5c06\u6709\u52a9\u4e8e\u67b6\u6784\u8bbe\u8ba1\u3002 \u672c\u7814\u7a76\u63d0\u51fa\u4e86\u4e00\u79cd\u81ea\u4e0a\u800c\u4e0b\u7684\u65b9\u6cd5\u6765\u6574\u5408\u65e0\u4eba\u673a\u7fa4\u7684\u4efb\u52a1\u539f\u5219\u548c\u7cfb\u7edf\u67b6\u6784\u8bbe\u8ba1\uff0c\u65e8\u5728\u786e\u5b9a\u7fa4\u4f53\u65e0\u4eba\u7cfb\u7edf\u67b6\u6784\u8bbe\u8ba1\u7684\u5206\u7c7b\u3002 MASC\u81f4\u529b\u4e8e\u901a\u8fc7\u5206\u89e3\u7f8e\u56fd\u6d77\u519b\u4efb\u52a1\u7684\u5b50\u96c6\uff0c\u5f00\u53d1\u53ef\u7528\u4e8e\u8fd9\u4e9b\u4efb\u52a1\u5e76\u652f\u6301\u672a\u6765\u4efb\u52a1\u7684\u6218\u672f\u6765\u5f00\u53d1\u7fa4\u4f53\u6e38\u620f\u5f0f\u67b6\u6784\u3002 \u56fe1\u663e\u793a\u4e86\u6240\u63d0\u51fa\u7684\u7fa4\u4f53\u64cd\u4f5c\u6846\u67b6MASC\u7684\u5206\u89e3\u3002 \u8fd9\u9879\u7814\u7a76\u7684\u91cd\u70b9\u662f\u5efa\u7b51\u7684\u7b56\u7565\u548c\u6e38\u620f\u6c34\u5e73\u3002<\/li>\n<\/ol>\n<p>The swarm operational team is based on the UAV swarm field experimentation conducted by the Advanced Robotic Systems Engineering Laboratory (ARSENL) at the Naval Postgraduate School. The team is comprised of a Swarm Commander, Swarm Monitor, and Ground Crew. The Swarm Commander selects the swarm tactics and is responsible for the overall execution of the mission. The Swarm Monitor oversees the health and function of the swarm, and separates errant individual UAVs from the swarm33. The Ground Crew is responsible for UAV preflight, launch, and postflight dutie<br \/> \u7fa4\u4f53\u8fd0\u884c\u56e2\u961f\u57fa\u4e8e\u7531\u6d77\u519b\u7814\u7a76\u751f\u9662\u7684\u9ad8\u7ea7\u673a\u5668\u4eba\u7cfb\u7edf\u5de5\u7a0b\u5b9e\u9a8c\u5ba4\uff08ARSENL\uff09\u8fdb\u884c\u7684\u65e0\u4eba\u673a\u7fa4\u4f53\u5b9e\u5730\u8bd5\u9a8c\u3002 \u8be5\u56e2\u961f\u7531Swarm Commander\uff0cSwarm Monitor\u548cGround Crew\u7ec4\u6210\u3002 \u7fa4\u4f53\u6307\u6325\u5b98\u9009\u62e9\u7fa4\u4f53\u6218\u672f\u5e76\u8d1f\u8d23\u4efb\u52a1\u7684\u6574\u4f53\u6267\u884c\u3002 \u7fa4\u4f53\u76d1\u89c6\u5668\u76d1\u89c6\u7fa4\u4f53\u7684\u5065\u5eb7\u548c\u529f\u80fd\uff0c\u5e76\u5c06\u9519\u8bef\u7684\u5355\u4e2a\u65e0\u4eba\u673a\u4e0e\u7fa4\u4f5333\u5206\u5f00\u3002 Ground Crew\u8d1f\u8d23\u65e0\u4eba\u673a\u9884\u68c0\uff0c\u53d1\u5c04\u548c\u540e\u98de\u884c<\/p>\n<p>The following UAV swarm mission taxonomy is proposed to describe the overall mission architecture, and includes the following terms:<br \/> A swarm mission describes the overall task and purpose delineating the action assigned to the swarm. Example UAV swarm missions include: intelligence, surveillance, and reconnaissance (ISR), maritime interdiction operations (MIO), humanitarian assistance and disaster relief (HADR), and search and rescue (SAR). Each swarm mission is the parent of several swarm mission phases.<br \/> A swarm mission phase describes a distinct time period within the mission. There are five operational phases in a swarm mission: Preflight, Ingress, OnStation, Egress, and Postflight. The three phases that cover the in-flight portion of the mission\u2014 Ingress, OnStation, and Egress\u2014are the focus of this research. A swarm mission phase is composed of one or more swarm tactics.<br \/> \u63d0\u51fa\u4ee5\u4e0b\u65e0\u4eba\u673a\u7fa4\u4f53\u4efb\u52a1\u5206\u7c7b\u6765\u63cf\u8ff0\u6574\u4f53\u4efb\u52a1\u67b6\u6784\uff0c\u5e76\u5305\u62ec\u4ee5\u4e0b\u672f\u8bed\uff1a<br \/> \u7fa4\u4f53\u4efb\u52a1\u63cf\u8ff0\u4e86\u63cf\u8ff0\u5206\u914d\u7ed9\u7fa4\u4f53\u7684\u52a8\u4f5c\u7684\u603b\u4f53\u4efb\u52a1\u548c\u76ee\u7684\u3002 \u65e0\u4eba\u673a\u7fa4\u4f53\u4efb\u52a1\u5305\u62ec\uff1a\u60c5\u62a5\uff0c\u76d1\u89c6\u548c\u4fa6\u5bdf\uff08ISR\uff09\uff0c\u6d77\u4e0a\u62e6\u622a\u884c\u52a8\uff08MIO\uff09\uff0c\u4eba\u9053\u4e3b\u4e49\u63f4\u52a9\u548c\u6551\u707e\uff08HADR\uff09\u4ee5\u53ca\u641c\u6551\uff08SAR\uff09\u3002 \u6bcf\u4e2a\u7fa4\u4f53\u4efb\u52a1\u90fd\u662f\u51e0\u4e2a\u7fa4\u4f53\u4efb\u52a1\u9636\u6bb5\u7684\u7236\u6bcd\u3002<br \/> \u7fa4\u4f53\u4efb\u52a1\u9636\u6bb5\u63cf\u8ff0\u4e86\u4efb\u52a1\u4e2d\u7684\u4e0d\u540c\u65f6\u95f4\u6bb5\u3002 \u7fa4\u4f53\u4efb\u52a1\u6709\u4e94\u4e2a\u64cd\u4f5c\u9636\u6bb5\uff1aPreflight\uff0cIngress\uff0cOnStation\uff0cEgress\u548cPostflight\u3002 \u8fd9\u9879\u7814\u7a76\u7684\u91cd\u70b9\u662f\u8986\u76d6\u98de\u884c\u4efb\u52a1\u90e8\u5206Ingress\uff0cOnStation\u548cEgress\u7684\u4e09\u4e2a\u9636\u6bb5\u3002 \u7fa4\u4f53\u4efb\u52a1\u9636\u6bb5\u7531\u4e00\u4e2a\u6216\u591a\u4e2a\u7fa4\u4f53\u6218\u672f\u7ec4\u6210\u3002<\/p>\n<p>A swarm tactic is the employment and ordered arrangement of agents in relation to one another for the purpose of performing a specific task31. Swarm tactics include searching, tracking, monitoring, evading, and attacking. A swarm tactic is composed of one or more swarm plays. For example, the Efficient Search tactic is composed of four different search pattern options (plays). Swarm tactics are designed to be used in multiple missions.<br \/> A swarm play describes the maneuvers and behaviors of the swarm as a collective of agents31. The artificial intelligence and robotics communities use the term \u201cbehavior\u201d to describe the interaction of the agent with the environment32. Swarm plays can be described as behaviors with specific triggers and temporal constraints, and are the building blocks for swarm tactics. Example swarm plays include launch, transit to waypoint, split, join, and orbit. Swarm play parameters are tunable characteristics of a play\u2014such as altitude blocks or geographic boundaries\u2014that can be changed based on the mission or rules of engagement (ROE). A swarm play is composed of one or more swarm algorithms. Swarm plays are re-used within multiple different swarm tactics in the MASC framework.<br \/> Swarm algorithms are the step-by-step procedures used by the controlling software to solve a recurrent task such as sorting, path planning or foraging. Swarm algorithms are the building blocks of swarm plays. A swarm play is composed of one or more swarm algorithms31. Swarm algorithms use data from the individual UAVs such as position, heading, velocity, altitude, attitude, health status, and state31.<br \/> \u7fa4\u4f53\u7b56\u7565\u662f\u4ee3\u7406\u4eba\u4e3a\u4e86\u6267\u884c\u7279\u5b9a\u4efb\u52a1\u800c\u76f8\u4e92\u5173\u8054\u7684\u5c31\u4e1a\u548c\u6709\u5e8f\u5b89\u639231\u3002\u7fa4\u4f53\u7b56\u7565\u5305\u62ec\u641c\u7d22\uff0c\u8ddf\u8e2a\uff0c\u76d1\u89c6\uff0c\u56de\u907f\u548c\u653b\u51fb\u3002\u7fa4\u4f53\u6218\u672f\u7531\u4e00\u4e2a\u6216\u591a\u4e2a\u7fa4\u4f53\u6e38\u620f\u811a\u672c\u7ec4\u6210\u3002\u4f8b\u5982\uff0c\u9ad8\u6548\u641c\u7d22\u7b56\u7565\u7531\u56db\u79cd\u4e0d\u540c\u7684\u641c\u7d22\u6a21\u5f0f\u9009\u9879\uff08\u811a\u672c\uff09\u7ec4\u6210\u3002\u7fa4\u4f53\u6218\u672f\u65e8\u5728\u7528\u4e8e\u591a\u4e2a\u4efb\u52a1\u3002<br \/> \u7fa4\u4f53\u811a\u672c\u63cf\u8ff0\u4e86\u7fa4\u4f53\u4f5c\u4e3a\u4ee3\u7406\u4eba\u96c6\u4f53\u7684\u52a8\u4f5c\u548c\u884c\u4e3a31\u3002\u4eba\u5de5\u667a\u80fd\u548c\u673a\u5668\u4eba\u793e\u533a\u4f7f\u7528\u672f\u8bed\u201c\u884c\u4e3a\u201d\u6765\u63cf\u8ff0\u4ee3\u7406\u4e0e\u73af\u5883\u7684\u4ea4\u4e9232\u3002\u7fa4\u4f53\u811a\u672c\u53ef\u4ee5\u88ab\u63cf\u8ff0\u4e3a\u5177\u6709\u7279\u5b9a\u89e6\u53d1\u548c\u65f6\u95f4\u7ea6\u675f\u7684\u884c\u4e3a\uff0c\u5e76\u4e14\u662f\u7fa4\u4f53\u6218\u672f\u7684\u6784\u5efa\u5757\u3002\u793a\u4f8b\u7fa4\u4f53\u811a\u672c\u5305\u62ec\u53d1\u5c04\uff0c\u8f6c\u79fb\u5230\u822a\u70b9\uff0c\u5206\u88c2\uff0c\u8fde\u63a5\u548c\u8f68\u9053\u3002\u7fa4\u4f53\u811a\u672c\u53c2\u6570\u662f\u811a\u672c\u7684\u53ef\u8c03\u7279\u6027 - \u4f8b\u5982\u9ad8\u5ea6\u5757\u6216\u5730\u7406\u8fb9\u754c - \u53ef\u4ee5\u6839\u636e\u4efb\u52a1\u6216\u53c2\u4e0e\u89c4\u5219\uff08ROE\uff09\u8fdb\u884c\u66f4\u6539\u3002\u7fa4\u811a\u672c\u7531\u4e00\u4e2a\u6216\u591a\u4e2a\u7fa4\u7b97\u6cd5\u7ec4\u6210\u3002 Swarm\u811a\u672c\u5728MASC\u6846\u67b6\u4e2d\u7684\u591a\u4e2a\u4e0d\u540c\u7fa4\u4f53\u7b56\u7565\u4e2d\u91cd\u590d\u4f7f\u7528\u3002<br \/> Swarm\u7b97\u6cd5\u662f\u63a7\u5236\u8f6f\u4ef6\u7528\u4e8e\u89e3\u51b3\u8bf8\u5982\u6392\u5e8f\uff0c\u8def\u5f84\u89c4\u5212\u6216\u89c5\u98df\u7b49\u5468\u671f\u6027\u4efb\u52a1\u7684\u9010\u6b65\u8fc7\u7a0b\u3002 Swarm\u7b97\u6cd5\u662f\u7fa4\u4f53\u811a\u672c\u7684\u6784\u5efa\u5757\u3002\u7fa4\u811a\u672c\u7531\u4e00\u4e2a\u6216\u591a\u4e2a\u7fa4\u7b97\u6cd531\u7ec4\u6210\u3002\u7fa4\u4f53\u7b97\u6cd5\u4f7f\u7528\u6765\u81ea\u5404\u4e2a\u65e0\u4eba\u673a\u7684\u6570\u636e\uff0c\u4f8b\u5982\u4f4d\u7f6e\uff0c\u822a\u5411\uff0c\u901f\u5ea6\uff0c\u9ad8\u5ea6\uff0c\u59ff\u6001\uff0c\u5065\u5eb7\u72b6\u6001\u548c\u72b6\u600131\u3002<\/p>\n<p>3.1. MASC Applied to a Notional Mission Scenario<br \/> The MASC framework was applied to a fictional maritime interdiction operation (MIO) in which the US Navy is supporting the Indonesian Navy in combatting illegal shipping in the Sunda Strait. MIO is a US Navy mission, typically executed with maritime air support that involves surveillance and interception of private or commercial vessels, boarding and searching of suspect vessels, and detaining, diverting or seizing vessels found in violation of United Nations sanctions or other international laws34.<br \/> MASC\u6846\u67b6\u9002\u7528\u4e8e\u865a\u6784\u7684\u6d77\u4e0a\u62e6\u622a\u884c\u52a8\uff08MIO\uff09\uff0c\u5176\u4e2d\u7f8e\u56fd\u6d77\u519b\u6b63\u5728\u652f\u6301\u5370\u5ea6\u5c3c\u897f\u4e9a\u6d77\u519b\u6253\u51fbSu\u4ed6\u6d77\u5ce1\u7684\u975e\u6cd5\u822a\u8fd0\u3002 MIO\u662f\u7f8e\u56fd\u6d77\u519b\u7684\u4efb\u52a1\uff0c\u901a\u5e38\u7531\u6d77\u4e0a\u7a7a\u4e2d\u652f\u63f4\u6267\u884c\uff0c\u6d89\u53ca\u76d1\u89c6\u548c\u62e6\u622a\u79c1\u4eba\u6216\u5546\u4e1a\u8239\u53ea\uff0c\u767b\u8239\u548c\u641c\u67e5\u53ef\u7591\u8239\u53ea\uff0c\u4ee5\u53ca\u6263\u7559\uff0c\u8f6c\u79fb\u6216\u6263\u62bc\u8fdd\u53cd\u8054\u5408\u56fd\u5236\u88c1\u6216\u5176\u4ed6\u56fd\u9645\u6cd5\u5f8b\u7684\u8239\u53ea34\u3002<\/p>\n<p>The mission is composed of the five phases introduced earlier: Preflight, Ingress, OnStation, Egress, and Postflight. The Preflight phase begins when the swarm is powered on and concludes when the swarm is in a flight ready status. The Ingress phase commences when the first UAV of the swarm has been loaded onto the launcher and concludes when the swarm has arrived at the on-station waypoint. The OnStation phase begins when the entire swarm reaches the assigned on-station area and ends when the first UAV in the swarm reaches low fuel or the on-station tasking ends. The Egress phase starts when the swarm is on a flight path to return to the ship and concludes when the entire swarm has landed. The Postflight phase begins when the swarm has landed and ends when the mission has been debriefed. The inflight phases of the mission (Ingress, OnStation, and Egress) are the focus of the MASC framework. The Preflight, Ingress, Egress, and Postlight phases are similar amongst different mission scenarios, with the greatest variety between missions occurring in the OnStation phase. The flow of mission phases for the MIO mission is shown in the activity diagram in Fig. 2.<br \/> <img decoding=\"async\" src=\"https:\/\/img.mushiming.top\/app\/mushiming_com\/38a30756619640b2adfc8d24f15572e5.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a (https:\/\/mushiming.com\/)  \u7b2c1\u5f20\" title=\"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a  \u7b2c1\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><br \/> \u4efb\u52a1\u7531\u524d\u9762\u4ecb\u7ecd\u7684\u4e94\u4e2a\u9636\u6bb5\u7ec4\u6210\uff1aPreflight\uff0cIngress\uff0cOnStation\uff0cEgress\u548cPostflight\u3002\u9884\u68c0\u9636\u6bb5\u5728\u7fa4\u4f53\u5f00\u542f\u65f6\u5f00\u59cb\uff0c\u5e76\u5728\u7fa4\u4f53\u5904\u4e8e\u51c6\u5907\u5c31\u7eea\u72b6\u6001\u65f6\u7ed3\u675f\u3002\u5f53\u7fa4\u4f53\u7684\u7b2c\u4e00\u4e2a\u65e0\u4eba\u673a\u88ab\u88c5\u8f7d\u5230\u53d1\u5c04\u5668\u4e0a\u65f6\uff0c\u5165\u53e3\u9636\u6bb5\u5f00\u59cb\uff0c\u5e76\u4e14\u5f53\u7fa4\u4f53\u5230\u8fbe\u7ad9\u4e0a\u822a\u8def\u70b9\u65f6\u7ed3\u675f\u3002 OnStation\u9636\u6bb5\u5728\u6574\u4e2a\u7fa4\u4f53\u5230\u8fbe\u6307\u5b9a\u7684\u7ad9\u5185\u533a\u57df\u65f6\u5f00\u59cb\uff0c\u5e76\u5728\u7fa4\u4e2d\u7684\u7b2c\u4e00\u4e2aUAV\u8fbe\u5230\u4f4e\u71c3\u6599\u6216\u7ad9\u4e0a\u4efb\u52a1\u7ed3\u675f\u65f6\u7ed3\u675f\u3002\u5f53\u7fa4\u4f53\u5728\u98de\u884c\u8def\u5f84\u4e0a\u8fd4\u56de\u8239\u8236\u65f6\uff0c\u51fa\u53e3\u9636\u6bb5\u5f00\u59cb\uff0c\u5e76\u5728\u6574\u4e2a\u7fa4\u843d\u843d\u5730\u65f6\u7ed3\u675f\u3002 Postflight\u9636\u6bb5\u5728\u7fa4\u4f53\u7740\u9646\u65f6\u5f00\u59cb\uff0c\u5728\u4efb\u52a1\u88ab\u6c47\u62a5\u540e\u7ed3\u675f\u3002\u4efb\u52a1\u7684\u98de\u884c\u9636\u6bb5\uff08Ingress\uff0cOnStation\u548cEgress\uff09\u662fMASC\u6846\u67b6\u7684\u91cd\u70b9\u3002 Preflight\uff0cIngress\uff0cEgress\u548cPostlight\u9636\u6bb5\u5728\u4e0d\u540c\u7684\u4efb\u52a1\u573a\u666f\u4e2d\u662f\u76f8\u4f3c\u7684\uff0c\u5728OnStation\u9636\u6bb5\u53d1\u751f\u7684\u4efb\u52a1\u4e4b\u95f4\u7684\u5dee\u5f02\u6700\u5927\u3002 MIO\u4efb\u52a1\u7684\u4efb\u52a1\u9636\u6bb5\u6d41\u7a0b\u5982\u56fe2\u4e2d\u7684\u6d3b\u52a8\u56fe\u6240\u793a\u3002<\/p>\n<p>Each of the phases contains sub-activity diagrams depicting lower-level activities of the swarm, swarm operations team, and external actors, as signified by the \u201cdecomposed\u201d notation in the lower part of the block. Fig. 3 shows the breakdown of on-station MIO activities by participating unit. The focus of this research is on the swarm\u2019s activities in the top branch of Fig. 3, which are depicted at the tactics level in Fig. 4<br \/> \u6bcf\u4e2a\u9636\u6bb5\u90fd\u5305\u542b\u5b50\u6d3b\u52a8\u56fe\uff0c\u63cf\u8ff0\u4e86\u7fa4\u4f53\uff0c\u7fa4\u4f53\u8fd0\u8425\u56e2\u961f\u548c\u5916\u90e8\u53c2\u4e0e\u8005\u7684\u4f4e\u7ea7\u6d3b\u52a8\uff0c\u5982\u5757\u4e0b\u90e8\u7684\u201c\u5206\u89e3\u201d\u7b26\u53f7\u6240\u793a\u3002 \u56fe3\u663e\u793a\u4e86\u53c2\u4e0e\u5355\u5143\u5bf9\u7ad9\u5185MIO\u6d3b\u52a8\u7684\u7ec6\u5206\u3002 \u672c\u7814\u7a76\u7684\u91cd\u70b9\u662f\u56fe3\u9876\u90e8\u5206\u652f\u4e2d\u7684\u7fa4\u4f53\u6d3b\u52a8\uff0c\u5982\u56fe4\u4e2d\u7684\u6218\u672f\u5c42\u9762\u6240\u793a\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/img.mushiming.top\/app\/mushiming_com\/1161ddcec5be4c6fb2468dffe455d807.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a (https:\/\/mushiming.com\/)  \u7b2c2\u5f20\" title=\"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a  \u7b2c2\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/img.mushiming.top\/app\/mushiming_com\/7141a1d1d3b440dca0354b52ad2070c2.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a (https:\/\/mushiming.com\/)  \u7b2c3\u5f20\" title=\"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a  \u7b2c3\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<p>The swarm\u2019s activities during the MIO mission OnStation phase are decomposed and shown in Fig. 4. The light green blocks with labels beginning with \u201ct\u201d (i.e. t3, t4, t5, t8, t9, t13) are tactics the swarm executes. In this mission, the swarm is divided into two sub-swarms that concurrently execute different tactics. Based on the type of cueing information it receives, Sub-swarm 1 monitors or searches the assigned area, tracks the target, and evades the target if it is threatened. Meanwhile, Sub-swarm 2 operates at a higher altitude and relays the communication and sensor data from Sub-swarm 1 to the command and control and other participating units. Concurrently, the swarm is consuming power that is being monitored by the Swarm Monitor in a separate swarm team activity diagram. Finally, the swarm receives the off-station decision from the Swarm Commander, coordinated by the trigger (green parallelogram), and proceeds to the Egress phase.<br \/> MIO\u4efb\u52a1OnStation\u9636\u6bb5\u671f\u95f4\u7fa4\u4f53\u7684\u6d3b\u52a8\u88ab\u5206\u89e3\u5e76\u663e\u793a\u5728\u56fe4\u4e2d\u3002\u6807\u6709\u201ct\u201d\u5f00\u5934\u7684\u6d45\u7eff\u8272\u5757\uff08\u5373t3\uff0ct4\uff0ct5\uff0ct8\uff0ct9\uff0ct13\uff09\u662f\u7fa4\u4f53\u6267\u884c\u7684\u7b56\u7565\u3002 \u5728\u8fd9\u4e2a\u4efb\u52a1\u4e2d\uff0c\u7fa4\u4f53\u88ab\u5206\u6210\u4e24\u4e2a\u5b50\u7fa4\uff0c\u540c\u65f6\u6267\u884c\u4e0d\u540c\u7684\u7b56\u7565\u3002 \u6839\u636e\u5b83\u63a5\u6536\u7684\u63d0\u793a\u4fe1\u606f\u7684\u7c7b\u578b\uff0c\u5b50\u7fa41\u76d1\u89c6\u6216\u641c\u7d22\u6307\u5b9a\u533a\u57df\uff0c\u8ddf\u8e2a\u76ee\u6807\uff0c\u5e76\u5728\u76ee\u6807\u53d7\u5230\u5a01\u80c1\u65f6\u56de\u907f\u76ee\u6807\u3002 \u540c\u65f6\uff0c\u5b50\u7fa42\u5728\u66f4\u9ad8\u7684\u9ad8\u5ea6\u64cd\u4f5c\u5e76\u4e14\u5c06\u6765\u81ea\u5b50\u7fa41\u7684\u901a\u4fe1\u548c\u4f20\u611f\u5668\u6570\u636e\u4e2d\u7ee7\u5230\u547d\u4ee4\u548c\u63a7\u5236\u4ee5\u53ca\u5176\u4ed6\u53c2\u4e0e\u5355\u5143\u3002 \u540c\u65f6\uff0c\u7fa4\u4f53\u6b63\u5728\u6d88\u8017\u7531Swarm Monitor\u5728\u5355\u72ec\u7684\u7fa4\u7ec4\u6d3b\u52a8\u56fe\u4e2d\u76d1\u89c6\u7684\u529f\u7387\u3002 \u6700\u540e\uff0c\u7fa4\u4f53\u63a5\u6536\u6765\u81eaSwarm Commander\u7684\u79bb\u7ad9\u51b3\u5b9a\uff0c\u7531\u89e6\u53d1\u5668\u534f\u8c03\uff08\u7eff\u8272\u5e73\u884c\u56db\u8fb9\u5f62\uff09\uff0c\u5e76\u8fdb\u5165\u51fa\u53e3\u9636\u6bb5\u3002<br \/> Underlying each tactic is a sequence of plays. Each tactic is composed of one or more sensor plays and one or more maneuver plays. Modifiable play parameters such as altitude, velocity, execution times, offset distances, geographic constraints, and frequency settings enable the plays to be tailored to mission specific conditions. Fig 5. is an example of the sequence of plays that compose the Evade tactic. The Evade tactic is triggered by a perceived threat that precipitates a concurrent sensor and maneuver loop. The sensors can be set to a passive emissions control (EMCON) mode or an active jamming mode based on ROE or the commander\u2019s intent. The maneuver part of the Evade tactic disperses the individual UAVs away from the threat and joins them back together to confuse the threat. The Evade tactic can be executed until the threat is gone, or for a specified time period as directed by the Swarm Commander through repetition of the Disperse play<br \/> \u6bcf\u79cd\u7b56\u7565\u7684\u57fa\u7840\u90fd\u662f\u4e00\u7cfb\u5217\u620f\u5267\u3002 \u6bcf\u4e2a\u6218\u672f\u7531\u4e00\u4e2a\u6216\u591a\u4e2a\u4f20\u611f\u5668\u811a\u672c\u548c\u4e00\u4e2a\u6216\u591a\u4e2a\u673a\u52a8\u811a\u672c\u7ec4\u6210\u3002 \u53ef\u4fee\u6539\u7684\u811a\u672c\u53c2\u6570\uff08\u4f8b\u5982\u9ad8\u5ea6\uff0c\u901f\u5ea6\uff0c\u6267\u884c\u65f6\u95f4\uff0c\u504f\u79fb\u8ddd\u79bb\uff0c\u5730\u7406\u9650\u5236\u548c\u9891\u7387\u8bbe\u7f6e\uff09\u4f7f\u5f97\u811a\u672c\u80fd\u591f\u6839\u636e\u4efb\u52a1\u7279\u5b9a\u6761\u4ef6\u8fdb\u884c\u5b9a\u5236\u3002 \u56fe5\u662f\u7ec4\u6210Evade\u6218\u672f\u7684\u811a\u672c\u5e8f\u5217\u7684\u4e00\u4e2a\u4f8b\u5b50\u3002 Evade\u6218\u672f\u662f\u7531\u611f\u77e5\u5230\u7684\u5a01\u80c1\u5f15\u53d1\u7684\uff0c\u8fd9\u79cd\u5a01\u80c1\u4fc3\u6210\u4e86\u540c\u65f6\u53d1\u751f\u7684\u4f20\u611f\u5668\u548c\u673a\u52a8\u56de\u8def\u3002 \u53ef\u4ee5\u6839\u636eROE\u6216\u6307\u6325\u5b98\u7684\u610f\u56fe\u5c06\u4f20\u611f\u5668\u8bbe\u7f6e\u4e3a\u65e0\u6e90\u53d1\u5c04\u63a7\u5236\uff08EMCON\uff09\u6a21\u5f0f\u6216\u4e3b\u52a8\u5e72\u6270\u6a21\u5f0f\u3002 Evade\u6218\u672f\u7684\u673a\u52a8\u90e8\u5206\u4f7f\u5404\u4e2a\u65e0\u4eba\u673a\u8fdc\u79bb\u5a01\u80c1\u5e76\u5c06\u5b83\u4eec\u8fde\u63a5\u5728\u4e00\u8d77\u4ee5\u6df7\u6dc6\u5a01\u80c1\u3002 Evade\u6218\u672f\u53ef\u4ee5\u5728\u5a01\u80c1\u6d88\u5931\u4e4b\u524d\u6267\u884c\uff0c\u6216\u8005\u5728Swarm Commander\u6307\u793a\u7684\u6307\u5b9a\u65f6\u95f4\u6bb5\u5185\u901a\u8fc7\u91cd\u590d\u5206\u6563\u811a\u672c\u6765\u6267\u884c<br \/> <img decoding=\"async\" src=\"https:\/\/img.mushiming.top\/app\/mushiming_com\/2ba7d76fbd1a439da5faf493f2148039.png\" =\"\" =\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" alt=\"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a (https:\/\/mushiming.com\/)  \u7b2c4\u5f20\" title=\"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544a  \u7b2c4\u5f20-\u7a46\u4e16\u660e\u535a\u5ba2\" ><\/p>\n<p>Each play is composed of one or more swarm algorithms, which are the self-contained sequence of procedures to be performed by the swarm control software in order to execute a specific task. Within the context of the MASC framework, the swarm algorithms show the boundary wherein the operational architecture ends and the solution architecture begins. There is a considerable body of research in robotics algorithms, especially with regard to search and tracking functions. Typically, these optimization problems are treated as local, small-scale sub-spaces of the larger, global (NP-complete) problem in order to satisfy practical applications35. Many algorithms applicable for swarm systems, such as Boid\u2019s flocking, ant colony optimization, and particle swarm optimization can be characterized as biologically-inspired or evolutionary36,37,38.<br \/> \u6bcf\u4e2a\u811a\u672c\u7531\u4e00\u4e2a\u6216\u591a\u4e2a\u7fa4\u7b97\u6cd5\u7ec4\u6210\uff0c\u8fd9\u4e9b\u7b97\u6cd5\u662f\u7531\u7fa4\u63a7\u5236\u8f6f\u4ef6\u6267\u884c\u4ee5\u6267\u884c\u7279\u5b9a\u4efb\u52a1\u7684\u72ec\u7acb\u8fc7\u7a0b\u5e8f\u5217\u3002 \u5728MASC\u6846\u67b6\u7684\u4e0a\u4e0b\u6587\u4e2d\uff0c\u7fa4\u4f53\u7b97\u6cd5\u663e\u793a\u4e86\u64cd\u4f5c\u4f53\u7cfb\u7ed3\u6784\u7ec8\u6b62\u4e14\u89e3\u51b3\u65b9\u6848\u4f53\u7cfb\u7ed3\u6784\u5f00\u59cb\u7684\u8fb9\u754c\u3002 \u673a\u5668\u4eba\u7b97\u6cd5\u4e2d\u6709\u5927\u91cf\u7814\u7a76\uff0c\u7279\u522b\u662f\u5728\u641c\u7d22\u548c\u8ddf\u8e2a\u529f\u80fd\u65b9\u9762\u3002 \u901a\u5e38\uff0c\u8fd9\u4e9b\u4f18\u5316\u95ee\u9898\u88ab\u89c6\u4e3a\u8f83\u5927\u7684\u5168\u5c40\uff08NP\u5b8c\u5168\uff09\u95ee\u9898\u7684\u5c40\u90e8\u5c0f\u5c3a\u5ea6\u5b50\u7a7a\u95f4\uff0c\u4ee5\u6ee1\u8db3\u5b9e\u9645\u5e94\u752835\u3002 \u9002\u7528\u4e8e\u7fa4\u4f53\u7cfb\u7edf\u7684\u8bb8\u591a\u7b97\u6cd5\uff0c\u4f8b\u5982Boid\u7684\u690d\u7ed2\uff0c\u8681\u7fa4\u4f18\u5316\u548c\u7c92\u5b50\u7fa4\u4f18\u5316\uff0c\u53ef\u4ee5\u88ab\u63cf\u8ff0\u4e3a\u751f\u7269\u542f\u53d1\u6216\u8fdb\u531636,37,38\u3002<\/p>\n<h3>4 Conclusions and Future Work<\/h3>\n<p>MASC provides a mission-based, top-down<br \/> framework and modeling approach focused on developing a modular UAV swarm playbook-type architecture by decomposing a subset of US Navy missions, and developing tactics that can be applied to those missions. The framework is derived from a consolidation of heuristics from multiple academic disciplines and extends the swarm UAV field experimentation research conducted by ARSENL. This framework provides a standard methodology for designing swarm unmanned systems that will operate within military missions. To progress from remotely piloted single UAVs to remotely supervised UAV swarms, automation must be incorporated into the system architecture. By operating at the tactics level rather than the play level, Swarm Commanders will be aided with mission-based automation tools designed to facilitate scaling up the UAV-to-operator ratio to achieve large swarm (greater than 50) capability. Due to the common task patterns across missions (i.e. searching, tracking, communication relay), this architecture has the potential to be re-used across multiple mission types. Future work in this research area includes employing this framework into a prototype virtual environment, collecting feedback from prospective users, expanding the mission set, and defining specific algorithms for each play<br \/> 4.\u7ed3\u8bba\u548c\u672a\u6765\u5de5\u4f5c<br \/> MASC\u63d0\u4f9b\u57fa\u4e8e\u4efb\u52a1\u7684\u81ea\u4e0a\u800c\u4e0b\u7684\u529f\u80fd<br \/> \u6846\u67b6\u548c\u5efa\u6a21\u65b9\u6cd5\u4fa7\u91cd\u4e8e\u901a\u8fc7\u5206\u89e3\u7f8e\u56fd\u6d77\u519b\u4efb\u52a1\u7684\u4e00\u90e8\u5206\u6765\u5f00\u53d1\u6a21\u5757\u5316\u65e0\u4eba\u673a\u7fa4\u4f53\u5267\u672c\u7c7b\u578b\u7684\u67b6\u6784\uff0c\u5e76\u5f00\u53d1\u53ef\u5e94\u7528\u4e8e\u8fd9\u4e9b\u4efb\u52a1\u7684\u7b56\u7565\u3002\u8be5\u6846\u67b6\u6e90\u81ea\u591a\u4e2a\u5b66\u79d1\u7684\u542f\u53d1\u5f0f\u6574\u5408\uff0c\u5e76\u6269\u5c55\u4e86ARSENL\u8fdb\u884c\u7684\u7fa4\u4f53\u65e0\u4eba\u673a\u73b0\u573a\u5b9e\u9a8c\u7814\u7a76\u3002\u8be5\u6846\u67b6\u63d0\u4f9b\u4e86\u4e00\u79cd\u6807\u51c6\u65b9\u6cd5\uff0c\u7528\u4e8e\u8bbe\u8ba1\u5c06\u5728\u519b\u4e8b\u4efb\u52a1\u4e2d\u8fd0\u884c\u7684\u7fa4\u4f53\u65e0\u4eba\u7cfb\u7edf\u3002\u4e3a\u4e86\u4ece\u9065\u63a7\u7684\u5355\u4e2a\u65e0\u4eba\u673a\u53d1\u5c55\u5230\u8fdc\u7a0b\u76d1\u7763\u7684\u65e0\u4eba\u673a\u7fa4\uff0c\u5fc5\u987b\u5c06\u81ea\u52a8\u5316\u6574\u5408\u5230\u7cfb\u7edf\u67b6\u6784\u4e2d\u3002\u901a\u8fc7\u5728\u6218\u672f\u5c42\u9762\u800c\u4e0d\u662f\u6e38\u620f\u5c42\u9762\u8fdb\u884c\u64cd\u4f5c\uff0cSwarm Commanders\u5c06\u914d\u5907\u57fa\u4e8e\u4efb\u52a1\u7684\u81ea\u52a8\u5316\u5de5\u5177\uff0c\u65e8\u5728\u5e2e\u52a9\u6269\u5927\u65e0\u4eba\u673a\u4e0e\u64cd\u4f5c\u5458\u7684\u6bd4\u4f8b\uff0c\u4ee5\u5b9e\u73b0\u5927\u7fa4\uff08\u8d85\u8fc750\uff09\u7684\u80fd\u529b\u3002\u7531\u4e8e\u5404\u4efb\u52a1\u7684\u5171\u540c\u4efb\u52a1\u6a21\u5f0f\uff08\u5373\u641c\u7d22\uff0c\u8ddf\u8e2a\uff0c\u901a\u4fe1\u4e2d\u7ee7\uff09\uff0c\u8be5\u67b6\u6784\u6709\u53ef\u80fd\u5728\u591a\u79cd\u4efb\u52a1\u7c7b\u578b\u4e2d\u91cd\u590d\u4f7f\u7528\u3002\u8be5\u7814\u7a76\u9886\u57df\u7684\u672a\u6765\u5de5\u4f5c\u5305\u62ec\u5c06\u8be5\u6846\u67b6\u5e94\u7528\u4e8e\u539f\u578b\u865a\u62df\u73af\u5883\uff0c\u6536\u96c6\u6f5c\u5728\u7528\u6237\u7684\u53cd\u9988\uff0c\u6269\u5c55\u4efb\u52a1\u96c6\uff0c\u5e76\u4e3a\u6bcf\u4e2a\u6e38\u620f\u5b9a\u4e49\u7279\u5b9a\u7b97\u6cd5<\/p>\n","protected":false},"excerpt":{"rendered":"elasticsearch\u96c6\u7fa4\u539f\u7406_hadoop\u96c6\u7fa4\u642d\u5efa\u5b9e\u9a8c\u62a5\u544aMission-basedArchitectureforSwarmComposabilityMission-basedArchitecturefo...","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"_links":{"self":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts\/7922"}],"collection":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/comments?post=7922"}],"version-history":[{"count":0,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts\/7922\/revisions"}],"wp:attachment":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/media?parent=7922"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/categories?post=7922"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/tags?post=7922"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}