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，提出了安全轨迹规划问题。确保无碰撞的安全轨迹是一项具有挑战性的课题，因其对搜索与探索任务中机器人成功应用的重要性而受到广泛关注。本研究提出一种方法，可在杂乱环境中生成具有安全关键保证的搜索轨迹。该方法通过离散控制障碍函数（DCBFs）集成安全关键约束，并结合遍历轨迹优化以实现安全探索。遍历轨迹优化可规划连续探索性轨迹，确保对空间的完全覆盖。我们通过无人机仿真与实验结果证明，该方法能够生成实现安全有效探索的轨迹。此外，我们利用真实世界的单无人机与多无人机平台，验证了该方法在安全探索中的有效性。