Multi-Aerial-Robot Planning

\abstract{Multi-robot systems are a major research topic in robotics. Designing, testing and deploying in the real world a large number of aerial robots is a concrete possibility due to the recent technological advances. The first section of this chapter treats the different aspects of cooperation in multi-agent systems.

A cooperative control should be designed in terms of the available feedback information. A cascade-type guidance law is proposed, followed by consensus approach and flocking behavior. Since information flow over the network changes over time, cooperative control must react accordingly but ensure group cooperative behavior which is the major issue in analysis and synthesis. Connectivity and convergence of formations are also studied. Team approach is followed by deterministic decision-making. Plans may be required for a team of aerial robots to plan for sensing, plan for action or plan for communication. Distributed receding horizon control as well as conflict resolution, artificial potentials and symbolic planning are thus analyzed.

Then, association with limited communications is studied, followed by genetic algorithms and game theory reasoning. Next, multi-agent decision-making under uncertainty is considered, formulating the Bayesian decentralized team decision problem, with and without explicit communication. Algorithms are then introduced for optimal planning as well as for task allocation and distributed chance constrained task allocation. Finally, some case studies are presented such as reconnaissance mission that can be defined as the road search problem or the general vehicle routing problem. Then, an approach is considered to coordinate a group of aerial robots without a central supervision, by using only local interactions between the robots. The third case is the optimization of perimeter patrol operation. If an aerial robot must be close toa location to monitor it correctly and the number of aerial robots does not allow covering each site simultaneously, a path planning problem arises. Finally stochastic strategies for surveillance are presented.}