利用示教学习的移动机械臂轨迹避障算法

To improve the environmental adaptability of service robots and alleviate user loads, a trajectory amendment algorithm utilizing learning from demonstration is proposed in this paper. First, a new trajectory with a shape similar to that previously demonstrated was generated by utilizing the dynamic movement primitives model, after which an improved distance-weighted k-nearest neighbor algorithm was developed to realize local modification for the trajectory shape at the end of the mobile manipulator. An online updating method was designed to avoid loss of adjacent effective training data. Obstacle avoidance experiments and real-time tests were then implemented in the human-robot interaction system. The experimental results showed the adaptability of the proposed obstacle avoidance algorithm to the new task, the obstacle avoidance decision ability and the online modification ability, to ensure friendly and smooth human-robot interactions.