Distributed Control of Heterogeneous Mobile Robotic Agents in the Presence of Uncertainties

Swarm robotics and distributed control offer the promise of enhanced performance and robustness relative to that of individual and centrally-controlled robots, with decreased cost or time-to-completion for certain tasks. Having many degrees of freedom, swarm-related control and estimation problems are challenging specifically when the solutions depend on a great amount of communication among the robots. Swarm controllers minimizing communication requirements are quite desirable.

Swarms are inherently more robust to uncertainties and failures, including complete loss of individual agents, due to the averaging inherent in convergence and agreement problems. Exploitation of this robustness to minimize processing and communication complexity is desirable.

This research focuses on simple but robust controllers for swarming problems, maximizing the likelihood of objective success while minimizing controller complexity and specialized communication or sensing requirements.

In addition, it develops distributed solutions for swarm control by examining and exploiting graph theoretic constructs. Details of specific implementations, such as nonholonomic motion and and numerosity constraints, were explored with some unexpectedly positive results.

In summary, this research focused on the development of control strategies for the distributed control of a swarm of robots, and graph-theoretic analysis of these controllers. These control strategies specifically consider probabilistic connectivity functions, based on requirements for sensing or communication. The developed control strategies are validated in both simulation and experiment.