论文标题
通过控制屏障功能，在混乱环境中安全至关重要的厄贡探索
Safety-Critical Ergodic Exploration in Cluttered Environments via Control Barrier Functions
论文作者
论文摘要
在本文中，我们解决了在受约束，混乱的环境中进行自动搜索和探索的安全轨迹计划的问题。确保安全（无碰撞）轨迹是一个具有挑战性的问题，由于其在搜索和勘探任务中成功利用机器人的重要性而获得了重要意义。这项工作贡献了一种在混乱的环境中生成保证的安全关键搜索轨迹的方法。我们的方法使用离散控制屏障功能（DCBF）与厄尔贡轨迹优化整合了安全 - 关键约束，以实现安全探索。沿途轨迹优化计划连续探索性轨迹，以确保空间的完全覆盖。我们通过对无人机进行的模拟和实验结果证明，我们的方法能够产生能够实现安全有效探索的轨迹。此外，我们展示了使用现实世界单和多人无人机平台进行安全探索的方法的功效。
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.
