基于强化学习的扑翼飞行器路径规划算法
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作者单位:

浙江大学航空航天学院

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中图分类号:

TP242

基金项目:

装备预研教育部联合基金(重点)项目(6141A02011803)


Local Planner for Flapping Wing Micro Aerial Vehicle Based on Deep Reinforcement Learning
Author:
Affiliation:

School of Aeronautics and Astronautics, Zhejiang University

Fund Project:

The Key Project of Joint Fund of Ministry of Education for Equipment Pre-research(6141A02011803)

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    摘要:

    针对扑翼飞行器机动性能弱的问题,提出了一种在未知环境下,示教学习辅助的强化学习局部路径规划算法(IL-PPO2)。首先,基于扑翼飞行器的受限视角的双目感知系统,提出了一种心形避障算法,降低避障时对扑翼飞行器控制精度的要求,提高避障鲁棒性;其次,根据心形避障算法的特性,提出了一种U型障碍的避障策略;最后,提出了一种示教学习辅助的强化学习局部路径规划算法,将心形避障算法与局部路径规划算法相结合,实现扑翼飞行器的局部路径规划。仿真结果表明,与TD3fD强化学习算法相比,IL-PPO2算法缩短了模型训练时间,路径规划效率与成功率明显高于TD3fD算法;与动态窗口法(DWA)相比,IL-PPO2算法提高了路径规划的成功率,并且有效融合了心形算法,提高了路径的平滑程度。

    Abstract:

    Since the poor maneuverability of Flapping Wing Micro Aerial Vehicle (FWMAV), a deep reinforcement learning (DRL) based local path planning method (IL-PPO2) was proposed with the assistant of demonstration learning in unknown environment. Firstly, due to the limited visual angle of stereo camera on FWMAV, a “Heart” algorithm was proposed to reduce the requirement for control accuracy and meanwhile improve robustness. Secondly, according to the characteristics of Heart algorithm, a U trap avoidance framework was developed. Finally, with the help of demonstration learning, a DRL based local path planning method was put forward, which was realized with the combination of Heart algorithm and local planner. According to the simulation results, compared to TD3fD DRL method, the path planning efficiency and success rate of IL-PPO2 is higher than TD3fD with shorter training time. Besides, compared to Dynamic Window Approach (DWA), the success rate of IL-PPO2 is improved, and the path smoothness is promoted considering the integration of Heart algorithm.

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历史
  • 收稿日期:2020-11-15
  • 最后修改日期:2021-01-11
  • 录用日期:2021-01-19
  • 在线发布日期: 2021-02-04
  • 出版日期: