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 exploration of the environment to discover or detect various “points of interest.”
Once detected, these points of interest become “targets” or “data sources” 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 “mission”, 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.
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.
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.
There are several projects in our research group which address issues in multi-agent systems:
Smart Adaptive Reliable Teams for Persistent Surveillance
Real-Time Optimization in Complex Stochastic Environments
Detection and tracking of multiple dynamic targets using cooperating networked agents
New Driving Models and Controllers for Connected Autonomous Vehicles
Simultaneous Optimization of Vehicle and Powertrain Operation Using Connectivity and Automation
A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments
Real-Time Distributed Optimization in Networked Multi-Agent Systems
Decentralized optimal control of cooperative networked multi-agent systems
Improving Highway Traffic Mobility with a Safe Swarm of Smart Vehicles