Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams

Abstract

This paper considers the problem of safely coordinating a team of sensor-equipped robots to reduce uncertainty about a dynamical process, where the objective trades off information gain and energy cost. Optimizing this trade-off is desirable, but leads to a non-monotone objective function in the set of robot trajectories. Therefore, common multi-robot planners based on coordinate descent lose their performance guarantees. Furthermore, methods that handle non-monotonicity lose their performance guarantees when subject to inter-robot collision avoidance constraints. As it is desirable to retain both the performance guarantee and safety guarantee, this work proposes a hierarchical approach with a distributed planner that uses local search with a worst-case performance guarantees and a decentralized controller based on control barrier functions that ensures safety and encourages timely arrival at sensing locations. Via extensive simulations, hardware-in-the-loop tests and hardware experiments, we demonstrate that the proposed approach achieves a better trade-off between sensing and energy cost than coordinate descent based algorithms.

A Distributed Algorithm for Multi-Robot Task Allocation via Weighted Buffered Voronoi Partition

“…A common approach to quantify the performance of robots is to calculate task completion rewards, where the marginal improvements decrease as additional targets are visited (submodularity) and visiting new targets does not impact the objective negatively (monotonicity) [12], [13]. These two properties allow for nearoptimal approximation algorithms while providing worst-case guarantees [14].…”