The multi-agent system framework consists of a team of autonomous agents cooperating to carry out complex tasks within a given environment that is potentially highly dynamic, hazardous, and even adversarial. In general, these tasks entail an exploration of the environment to discover or detect various \u201cpoints of interest.\u201d<\/p>\n
The areas in which our lab is doing influential research on this topic are:<\/p>\n
Read more …<\/a><\/div>\n<\/div>\n We are interested in the decision-making process in cooperative control applications. We currently concentrate on the cooperative mission control of multiple Uninhabitated Autonomous Vehicles (UAVs) in a battlefield environment. The main objectives include developing the methodology and software that enable<\/p>\n read more<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n In contrast to a\u00a0patrolling problem\u00a0where “every” point in a\u00a0mission space must be monitored, the problem we address here involves a “finite number” of targets (typically larger than the number of agents)\u00a0which the agents must cooperatively monitor through periodic visits. We model\u00a0each target as a queue which value increases if\u00a0not being covered\u00a0and decreases if\u00a0being covered by agents. Our objective is to minimize the accumulated target values over all targets within a given time horizon. Read more …<\/a>\n<\/ul>\n<\/div>\n<\/div>\n <\/p>\n Once detected, these points of interest become \u201ctargets\u201d or \u201cdata sources\u201d which need to be monitored. If the targets have dynamics and are mobile, then they also need to be tracked by the agents. Thus, the overall objective of the system may be time-varying and combines exploration, data collection, and tracking to define a \u201cmission\u201d, all in the presence of uncertainties in the processes involved and usually with far more targets than agents. This setting typically arises in mobile robotic applications and sensor networks, but it is surprisingly rich and encompasses a number of other, much less obvious, application domains.<\/p>\n The control and coordination of agents, whether they be autonomous robots, or sensor platforms, in dynamic, hazardous, and possibly adversarial environments is highly challenging since it involves multiple objectives and a considerable amount of information exchange with often severe communication limitations (e.g., in a wireless network, the agents must operate with limited energy resources). Experience has shown that, even in relatively simple problems, the use of ad hoc control policies frequently leads to poorly performing systems. This motivates the use of optimization methods to ensure that well-designed, rational policies are developed that can guarantee satisfactory, if not optimal, behavior. Naturally, such optimization problems rapidly get computationally intractable and their solution is rarely amenable to on-line scalable, distributed implementations.<\/p>\n The types of tasks performed by multi-agent systems include consensus, coverage control, and persistent monitoring. The persistent monitoring problem arises when agents must monitor a dynamically changing environment which cannot be fully covered by a stationary team of agents. Thus, persistent monitoring differs from traditional coverage tasks due to the perpetual need to cover a changing environment.<\/p>\n<\/h4>\n
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Coverage Control<\/h4>\n
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The coverage control problem aims to find the optimal arrangement for a given set of agents inside a mission space so as to maximize the probability of detecting randomly occurring events. An agent can be thought of as a sensor node, a robot, a lighting device, an antenna or any other similar resource providing device. The importance of the coverage control problem as well as the applicability of the solving techniques are evident. In our research, we develop techniques for solving the coverage control problem under a diverse set of different conditions See platforms<\/a>. These techniques can be easily adapted to numerous applications. Moreover, the generalizability of the coverage control problems enables us to develop optimization techniques which can be applied to a much broader class of problems, such as for the class of general co-operative multi-agent optimization. <\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
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Cooperative Control<\/h4>\n
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Cooperative control deals with the problem of controlling a multi-agent robotic system to fulfill a common goal. The tasks associated with these robotic systems include search, exploration, surveillance, rescue operations and mapping unknown or partially known environments. In real-world applications, the control of multiple robots is often complicated by the following factors<\/p>\n
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Persistent Monitoring<\/h4>\n
\nSystems consisting of cooperating mobile agents<\/strong> are often used to perform tasks such as coverage control, surveillance, and environmental\u00a0sampling. The persistent monitoring problem arises when agents must monitor a dynamically changing environment<\/strong> which cannot be fully covered by a\u00a0stationary team of agents (as in the coverage control). A result of the\u00a0exploration process is the eventual discovery of various “points of interest”\u00a0which, once detected, become “targets” or “data sources” which need to be monitored. This setting arises in\u00a0multiple application domains ranging from surveillance, environmental monitoring, and energy management\u00a0down to nano-scale systems tasked to track fluorescent or\u00a0magnetic particles for the study of dynamic processes in bio-molecular systems\u00a0and in nano-medical research.<\/p>\n