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Updated: Jul 11, 2025

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围绕障碍物进行运动规划,并使用凸起式优化
Tobia Marcucci1, Mark Petersen2, David von Wrangel1
1Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.
本研究引入了机器人运动规划的新凸式优化框架,使绕过障碍物产生高效可靠的轨迹. 凸集图 (GCS) 方法在复杂环境中明显优于采样方法.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 优化优化 优化优化
- 运动规划 运动规划
背景情况:
- 在各种应用中,围绕障碍物进行运动计划对机器人至关重要.
- 基于优化的规划者在混乱的环境中与非凸度作斗争.
- 基于采样的规划器在高维度和差异性约束方面存在局限性.
研究的目的:
- 开发一个框架,使凸优化为高效和可靠的无障碍的运动规划.
- 解决复杂,高维空间中现有规划方法的局限性.
主要方法:
- 使用凸集图 (GCS) 开发了一个动作规划问题的实际凸放松.
- 利用最近的技术在GCS中找到最短的路径.
- 在放松的溶液中应用了具有成本效益的后处理步骤.
主要成果:
- 凸起式放松通常非常紧密,产生接近全球最佳的无碰撞轨迹.
- 与基于采样的算法相比,GCS计划器在更短的时间内找到更好的轨迹.
- 通过模拟和硬件实验,在高维,复杂的环境中展示了可靠的轨迹设计.
结论:
- GCS框架在机器人运动规划方面取得了重大进展,特别是在混乱的环境中.
- 凸式优化可以有效地应用于具有复杂约束的运动规划问题.
- 拟议的方法为传统采样方法提供了更有效和可靠的替代方案.
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