提出实时安全机器人运动规划算法，动态环境也能快速避障。
Real-Time Sampling-Based Safe Motion Planning for Robotic Manipulators in Dynamic Environments
- 基于采样构建动态扩展气泡模型，预判障碍物影响
- 在真实机器人上验证，仅用普通硬件实现毫秒级响应
- 适合人机共存场景，无需昂贵计算设备
本文提出动态快速探索广义树（DRGBT）算法，一种面向动态环境的采样式运动规划方法。通过详细的时间分析与调度优化，确保实时运行。识别出耗时关键模块及其与障碍物数量的依赖关系。利用到障碍物的距离信息构建动态扩展自由构型空间气泡结构，据此建立满足所有运动学约束的保证安全运动的充分条件。通过大规模随机仿真对比了该算法与现有先进方法的性能。最后，在真实机器人上进行了实验，涵盖多种场景，包括有人参与的情况。结果表明，在典型的传感器配置下，使用低成本硬件和串行架构，无需GPU或复杂并行化，即可实现该运动规划算法的实时可行性与有效性。
原文摘要 · Abstract (English)
In this paper, we present the main features of Dynamic Rapidly-exploring Generalized Bur Tree (DRGBT) algorithm, a sampling-based planner for dynamic environments. We provide a detailed time analysis and appropriate scheduling to facilitate a real-time operation. To this end, an extensive analysis is conducted to identify the time-critical routines and their dependence on the number of obstacles. Furthermore, information about the distance to obstacles is used to compute a structure called dynamic expanded bubble of free configuration space, which is then utilized to establish sufficient conditions for a guaranteed safe motion of the robot while satisfying all kinematic constraints. An extensive randomized simulation trial is conducted to compare the proposed algorithm to a competing state-of-the-art method. Finally, an experimental study on a real robot is carried out covering a variety of scenarios including those with human presence. The results show the effectiveness and feasibility of real-time execution of the proposed motion planning algorithm within a typical sensor-based arrangement, using cheap hardware and sequential architecture, without the necessity for GPUs or heavy parallelization.
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。