"Abstractions, Analysis Techniques, and Synthesis of
Scalable Control Strategies for Robot Swarms”

Spring Berman
Ph.D. Candidate
Advisor: Vijay Kumar
Department of Mechanical Engineering and Applied Mechanics
University of Pennsylvania

Abstract
Tasks that require parallelism, redundancy, and adaptation to dynamic, possibly hazardous environments can potentially be performed very efficiently and robustly by a swarm robotic system.  Such a system would consist of hundreds or thousands of anonymous, resource-constrained robots that operate autonomously, with little to no direct human supervision.  The massive parallelism of a swarm would allow it to perform effectively in the event of robot failures, and the simplicity of individual robots facilitates a low unit cost.

Key challenges in the development of swarm robotic systems include the accurate prediction of swarm behavior and the design of robot controllers that can be proven to produce a desired macroscopic outcome.  The controllers should be scalable, meaning that they ensure system operation regardless of the swarm size. In this talk, I will present a comprehensive approach to modeling a swarm robotic system, analyzing its performance, and synthesizing scalable stochastic control policies that cause the swarm members to collectively achieve a target objective.  The control policies are decentralized, computed a priori, implementable on robots with limited sensing and communication capabilities, and have theoretical guarantees on performance. I will demonstrate the application of this approach to the design of a swarm task allocation strategy that does not rely on inter-robot communication and a reconfigurable manufacturing system.  My approach is inspired by the self-organized behavior of natural swarms such as ant colonies, which achieve complex tasks through the local interactions of many simple individuals.