Reliable distribution of computational load in robot teams

Abstract

Abstract Modern multi-robot systems often need to solve computationally intensive tasks but operate with limited compute resources and in the presence of failures. Cooperating to share computational tasks between robots at the edge reduces execution time. We introduce and evaluate a new computation load management technology for teams of robots: Reliable Autonomous Mobile Programs (RAMPs). RAMPs use information about the computational resources available in the team and a cost model to decide where to execute. RAMPs are implemented in ROS on a collection of Raspberry Pi-based robots. The performance of RAMPs is evaluated using route planning, a typical computationally-intensive robotics application. A systematic study of RAMPs demonstrates a high likelihood of optimal or near-optimal distribution and hence efficient resource utilisation. RAMPs successfully complete in the presence of simultaneous, or successive, robot failures and network failures, while preserving near-optimal distribution.