Safety-Critical Ergodic Exploration in Cluttered Environments via Control Barrier Functions

TL;DR

This paper presents a novel method combining control barrier functions with ergodic trajectory optimization to enable safe, complete exploration in cluttered environments, demonstrated on drone platforms.

Contribution

It introduces a new approach that guarantees safety-critical, collision-free exploration trajectories by integrating control barrier functions with ergodic planning.

Findings

Successfully generated safe exploration trajectories in cluttered environments.

Validated approach on both simulated and real drone platforms.

Achieved complete space coverage while ensuring safety constraints.

Abstract

In this paper, we address the problem of safe trajectory planning for autonomous search and exploration in constrained, cluttered environments. Guaranteeing safe (collision-free) trajectories is a challenging problem that has garnered significant due to its importance in the successful utilization of robots in search and exploration tasks. This work contributes a method that generates guaranteed safety-critical search trajectories in a cluttered environment. Our approach integrates safety-critical constraints using discrete control barrier functions (DCBFs) with ergodic trajectory optimization to enable safe exploration. Ergodic trajectory optimization plans continuous exploratory trajectories that guarantee complete coverage of a space. We demonstrate through simulated and experimental results on a drone that our approach is able to generate trajectories that enable safe and effective exploration. Furthermore, we show the efficacy of our approach for safe exploration using real-world single- and multi- drone platforms.