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

TL;DR

This paper introduces a distributed local search algorithm for energy-aware information gathering by heterogeneous robot teams, effectively balancing information gain and energy costs while reducing communication and computation.

Contribution

It presents a novel distributed planning method using lazy/greedy local search for non-monotone submodular optimization in multi-robot systems.

Findings

Reduces communication by up to 60%

Cuts computation by 80-92%

Outperforms coordinate descent algorithms

Abstract

This paper considers the problem of planning trajectories for a team of sensor-equipped robots to reduce uncertainty about a dynamical process. Optimizing the trade-off between information gain and energy cost (e.g., control effort, distance travelled) is desirable but leads to a non-monotone objective function in the set of robot trajectories. Therefore, common multi-robot planning algorithms based on techniques such as coordinate descent lose their performance guarantees. Methods based on local search provide performance guarantees for optimizing a non-monotone submodular function, but require access to all robots' trajectories, making it not suitable for distributed execution. This work proposes a distributed planning approach based on local search and shows how lazy/greedy methods can be adopted to reduce the computation and communication of the approach. We demonstrate the efficacy of the proposed method by coordinating robot teams composed of both ground and aerial vehicles with different sensing/control profiles and evaluate the algorithm's performance in two target tracking scenarios. Compared to the naive distributed execution of local search, our approach saves up to 60% communication and 80--92% computation on average when coordinating up to 10 robots, while outperforming the coordinate descent based algorithm in achieving a desirable trade-off between sensing and energy cost.