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An Adaptive RRT Based on Dynamic Step for UAV Path Planning

机译:基于动态步长的自适应RRT无人机路径规划

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摘要

In view of the shortcomings of planning time and path quality for Rapidly-exploring Random Tree (RRT) in the mountain terrain, this paper presents a adaptive RRT algorithm based on dynamic step and uses the hermite difference polynomials to smooth the planned path. Adaptive RRT algorithm based on dynamic step introduces feedback factor and flight bound, dynamically adjusts the step of exploration tree and the selection of growing point according to the distribution of obstacles in the flight environment, it reduces the time required for planning and improves the path quality. The simulation results show that after the 300th iteration, compared to the CPSO, the average number of explore failure times reduced 1335 times, consuming time reduced 9144 ms and flying distance reduced 3.288 km by using the adaptive RRT algorithm based on dynamic step.
机译:针对山区快速探索随机树(RRT)的规划时间和路径质量的不足,提出了一种基于动态步长的自适应RRT算法,并使用Hermite差分多项式对规划路径进行平滑处理。基于动态步长的自适应RRT算法引入了反馈因子和飞行边界,根据飞行环境中障碍物的分布动态调整了探索树的步长和生长点的选择,减少了规划所需的时间并提高了路径质量。仿真结果表明,经过第300次迭代后,基于动态步长的自适应RRT算法与CPSO相比,平均探索失败次数减少了1335次,耗时减少了9144 ms,飞行距离减少了3.288 km。

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