基于强化学习的挖掘机时间最优轨迹规划

针对挖掘机的自主作业场景,提出基于强化学习的时间最优轨迹规划方法.首先,搭建仿真环境用于产生数据,以动臂、斗杆和铲斗关节的角度、角速度为状态观测变量,以各关节的角加速度值为动作信息,通过状态观测信息实现仿真环境与自主学习算法的交互;然后,设计以动臂、斗杆和铲斗关节运动是否超出允许范围、完成任务 总时间和目标相对距离为奖励函数对策略网络参数进行训练;最后,利用改进的近端策略优化算法(proximal policy optimization, PPO)实现挖掘机的时间最优轨迹规划.与此同时,与不同连续动作空间的强化学习算法进行对比,实验结果表明:所提出优化算法效率更高,收敛速度更快,作业轨迹更平滑,可有效避免各关节受到较大冲击,有助于挖掘机高效、平稳地作业.

Time optimal trajectory planning of excavator based on deep reinforcement learning

Aiming at the autonomous operation scenarios of excavators, a time optimal trajectory planning method based on reinforcement learning is proposed. This method builds a simulation environment to generate data. The angle and velocity of the boom, arm and bucket joints are used as state observation variables, and the angle acceleration of each joint is used as action information, and the simulation environment and autonomous learning are realized through the state observation information. The interaction of the algorithm is designed to train the policy network parameters using whether the joint motion of the boom, arm and bucket exceeds the allowable range, the total time to complete the task and the relative distance of the target as the reward function to train the policy network parameters. Finally, using the improved proximal policy optimization(PPO) realizes the time optimal trajectory planning of the excavator. At the same time, compared with the results of the different reinforcement learning algorithms with continuous action spaces, the experimental results show that the proposed optimization algorithm has higher efficiency, faster convergence speed, and smoother operation trajectory, which can effectively avoid the large impact on each joint and contribute to the efficient and stable operation of the excavator.