让机器人在灾后动态废墟中自主安全搜寻，提升搜救效率与可靠性。
Enabling Robots to Autonomously Search Dynamic Cluttered Post-Disaster Environments
- 融合高效规划与鲁棒跟踪的控制框架，兼顾路径优化与不确定性应对。
- 仿真结果显示，目标抵达成功率提升最高达42.3%，且全程无碰撞。
- 适合研究自主机器人搜救、复杂环境导航的学者与工程师参考。
机器人将使灾难响应中的搜索与救援（SaR）迈上新台阶，前提是能自主承担危险的萨尔任务。自主萨尔机器人的主要挑战是在存在不确定性的杂乱环境中安全导航，同时避开静态和移动障碍物。本文提出一种集成控制框架，用于动态、不确定环境中的萨尔机器人，包括一个计算高效的启发式运动规划系统，可生成无碰撞的参考轨迹（假设无不确定性），以及一个鲁棒运动跟踪系统，可考虑不确定性影响，引导机器人跟踪该参考轨迹。该控制架构在实现多个萨尔目标之间取得平衡，同时满足安全等硬约束。多种基于计算机的仿真结果表明，所提出的集成控制架构在安全、无碰撞且最短时间内抵达目标（如被困人员）方面，相比两种常用先进方法（快速探索随机树与人工势场法）表现显著更优，性能提升最高达42.3%。
原文摘要 · Abstract (English)
Robots will bring search and rescue (SaR) in disaster response to another level, in case they can autonomously take over dangerous SaR tasks from humans. A main challenge for autonomous SaR robots is to safely navigate in cluttered environments with uncertainties, while avoiding static and moving obstacles. We propose an integrated control framework for SaR robots in dynamic, uncertain environments, including a computationally efficient heuristic motion planning system that provides a nominal (assuming there are no uncertainties) collision-free trajectory for SaR robots and a robust motion tracking system that steers the robot to track this reference trajectory, taking into account the impact of uncertainties. The control architecture guarantees a balanced trade-off among various SaR objectives, while handling the hard constraints, including safety. The results of various computer-based simulations, presented in this paper, showed significant out-performance (of up to 42.3%) of the proposed integrated control architecture compared to two commonly used state-of-the-art methods (Rapidly-exploring Random Tree and Artificial Potential Function) in reaching targets (e.g., trapped victims in SaR) safely, collision-free, and in the shortest possible time.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。