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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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基于RRT星和APF的AUV的混合路径规划算法.

Boyu Zhang1, Yishan Su2, Shanlin Sun3

  • 1School of Aeronautics and Astronautics, Guilin University of Aerospace Technology, Guilin, 541004, China.

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概括

本研究介绍了自动水下车辆 (AUV) 的新路径规划算法,该算法显著提高了效率和路径质量. 这种新的方法提高了实时性能,并确保了复杂的水下环境中的动力学可行性.

关键词:
适应性步骤大小适应性步骤大小人工潜力场是一个人造潜力场.自主水下车辆自动驾驶立方不均的B-spline曲线曲线采用随机抽样进行抽样.

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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
  • 海洋工程 海洋工程

背景情况:

  • 自主水下车辆 (AUV) 面临着动力学约束和实时路径规划的挑战.
  • 现有的基于RRT的方法在复杂环境中经常与效率和路径质量扎.

研究的目的:

  • 为AUV开发一种新的路径规划算法,以解决动力学约束和实时要求.
  • 与传统方法相比,提高采样效率,路径质量和轨道平滑性.

主要方法:

  • 拟议的定向圆和目标偏差的动态人工潜力场RRT* (DCGB-DAPF-RRT*) 算法.
  • 集成定向圆采样,目标偏差采样,自适应步长和动态人工潜力场.
  • 利用冗余节点修剪和立方非均的B-spline插值来增强轨迹.

主要成果:

  • 路线规划时间缩短了50.0-73.6%.
  • 节点数减少了71.0-77.8%,代减少了61.0-85.0%.
  • 实现了100%的成功率,路径长度为1766.1米,最大转角度为11.35°.

结论:

  • 该DCGB-DAPF-RRT*算法有效地满足AUV运动约束和实时需求.
  • 拟议的方法为AUV路径规划的效率,路径质量和动力学可行性提供了显著的改进.
  • 在复杂的环境中表现出卓越的性能,确保成功的导航.