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.

翻译：本文针对受限且杂乱环境中的自主搜索与探索轨迹规划安全问题展开研究。保证安全（无碰撞）轨迹是实现机器人成功执行搜索与探索任务的关键挑战，这一问题因其重要性而备受关注。本文提出了一种能在杂乱环境中生成具有安全关键性保证搜索轨迹的方法。该方法通过将离散控制障碍函数（DCBFs）与遍历轨迹优化相结合，引入安全关键约束，从而在安全约束下实现高效探索。遍历轨迹优化可规划出能保证空间完全覆盖的连续探索轨迹。通过无人机仿真与实验结果，我们验证了该方法能够生成兼具安全性与有效性的探索轨迹。此外，基于单无人机与多无人机真实平台的安全探索实验进一步证明了该方法的有效性。