Energy-Aware, Collision-Free Information Gathering for Heterogeneous Robot Teams

TL;DR

This paper introduces a hierarchical, energy-aware, collision-free planning method for heterogeneous robot teams that balances information gathering and energy use while ensuring safety and performance guarantees.

Contribution

It proposes a novel distributed planning framework combining local search with control barrier functions to handle non-monotonic objectives and collision avoidance.

Findings

Outperforms coordinate descent algorithms in energy and sensing trade-offs.

Ensures safety with decentralized control using control barrier functions.

Validated through extensive simulations and real hardware experiments.

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.