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在动态环境中使用改进的人工潜力场方法追踪轨迹和避开障碍物
Long Di1, Naiwei Huang1, Jiaqi He1
1Zhaoqing Power Supply Bureau, Guangdong Power Grid Co., Ltd., Zhaoqing, Guangdong, China.
PloS one
|July 10, 2025
概括
这项研究引入了一个新的实时轨迹规划框架,用于机器人. 它增强了人工潜力场 (APF),以改善动态环境中的路径跟踪和障碍回避.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 机器人需要精确的轨迹跟踪和在动态环境中避开障碍物.
- 现有的方法难以平衡跟踪路径的准确性和实时避开障碍.
研究的目的:
- 为机器人提出一个实时轨迹规划框架.
- 为了应对同时准确的轨迹跟踪和动态避难障碍的挑战.
- 在非结构化环境中提高机器人的安全性和性能.
主要方法:
- 一种增强的人工潜力场 (APF) 方法,使用虚拟目标点来实现路径精度.
- 一个动态的障碍驱逐模型,结合速度和加速,以积极避免.
- 一个使用运动模式分析的概率障碍运动预测框架.
主要成果:
- 与改进的APF方法相比,轨迹跟踪错误减少了55.8%.
- 与动态运动原始体 (DMP) 基线相比,追踪误差下降了41.5%.
- 在复杂的,模拟的动态障碍场景中验证了优越的强度和安全性能.
结论:
- 拟议的框架有效地平衡了轨迹跟踪和避开障碍的方法.
- 预测运动分析的整合提高了机器人的适应性和安全性.
- 该方法显示了与现有的APF和DMP方法相比的显著改进.
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