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

Cameron Lerch, Ethan Dong, Ian Abraham

Submitted on 8 November 2022


In this paper, we address the problem of safe trajectory planning for autonomous search and exploration in constrained, cluttered environments. Guaranteeing safe navigation is a challenging problem that has garnered significant attention. 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 full 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 of real-world single- and multi- drone platforms.


Subject: Computer Science - Robotics