基于定时弹性带的变量分裂自主轨迹规划
Timed-Elastic-Band Based Variable Splitting for Autonomous Trajectory Planning
AI总结：
本文提出基于定时弹性带的变量分裂（TEB-VS）方法，通过约束优化解决轨迹摆动问题，提升导航稳定性，并在仿真和真实环境中用TurtleBot2验证其有效性。
摘要：
现有的轨迹规划方法在处理导航过程中的自主轨迹摆动问题时面临困难，导致到达目的地时产生显著误差。本文研究自主轨迹规划问题，旨在开发创新解决方案，以增强无人系统在复杂动态环境中导航的适应性和鲁棒性。我们首先引入变量分裂（VS）方法作为一种约束优化方法，重新构想著名的定时弹性带（TEB）算法，从而提出一种新颖的碰撞避免方法，命名为基于定时弹性带的变量分裂（TEB-VS）。所提出的TEB-VS方法展现出优越的导航稳定性，同时保持与TEB几乎相同的资源消耗。随后，我们分析了所提出的TEB-VS方法的收敛性。为了评估TEB-VS的有效性和效率，我们在仿真环境和真实世界数据集中使用TurtleBot2进行了大量实验。
英文摘要：
Existing trajectory planning methods are struggling to handle the issue of autonomous track swinging during navigation, resulting in significant errors when reaching the destination. In this article, we address autonomous trajectory planning problems, which aims at developing innovative solutions to enhance the adaptability and robustness of unmanned systems in navigating complex and dynamic environments. We first introduce the variable splitting (VS) method as a constrained optimization method to reimagine the renowned Timed-Elastic-Band (TEB) algorithm, resulting in a novel collision avoidance approach named Timed-Elastic-Band based variable splitting (TEB-VS). The proposed TEB-VS demonstrates superior navigation stability, while maintaining nearly identical resource consumption to TEB. We then analyze the convergence of the proposed TEB-VS method. To evaluate the effectiveness and efficiency of TEB-VS, extensive experiments have been conducted using TurtleBot2 in both simulated environments and real-world datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
