利用示教学习的移动机械臂轨迹避障算法

Learning from demonstration based obstacle avoidance algorithm to plan the trajectory of a mobile manipulator

为了提高服务机器人的环境适应性和减轻操作者的控制负担,本文提出了一种利用示教学习的轨迹修正算法。利用动态动作基元模型,生成与示教轨迹形状相似的新轨迹。进而提出改进的距离加权k-近邻算法,实现移动机械臂末端轨迹形状的局部修正。本文设计了避免相邻有效训练数据丢失的在线更新方法,并在人机交互系统上进行避障和实时性测试。实验结果证明了本文提出的轨迹避障算法具有对新任务的适应能力,不同场景下的避障决策能力和在线修正能力,从而保证友好、流畅的人机交互过程。

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.