Synthesis and analysis of distributed ensemble control strategies for allocation to multiple tasks

We present the synthesis and analysis of distributed ensemble control policies to enable a team of robots to control their distribution across a collection of tasks. We assume that individual robot controllers are modeled as a sequential composition of individual task controllers. A macroscopic description of the team dynamics is then used to synthesize ensemble feedback control strategies that maintain the desired distribution of robots across the tasks. We present a distributed implementation of the ensemble feedback strategy that can be implemented with minimal communication requirements. Different from existing strategies, the approach results in individual robot control policies that maintain the desired mean and the variance of the robot populations at each task. We present the stability properties of the ensemble feedback strategy, verify the feasibility of the distributed ensemble controller through high-fidelity simulations, and examine the robustness of the strategy to sensing and/or actuation failures. Specifically, we consider the case when robots are subject to estimation and navigation errors resulting from lossy inter-agent wireless communication links and localization errors.