融合长短时记忆机制的机械臂多场景快速运动规划

Multi-scene rapid motion planning combining with long and short time memory mechanisms for manipulators

针对快速扩展随机树(rapid-exploration random trees,RRT)算法难以有效解决多场景环境下的机械臂快速运动规划问题,提出一种融合长短时记忆机制的快速运动规划算法.首先,采用高斯混合模型(Gaussian mixture models,GMM)在规划的初始阶段通过随机采样构建环境的场景模型,并利用该模型进行碰撞检测,以提高运动规划效率;然后,根据人类的记忆机制原理,对多场景的不同GMM按照即时记忆、短期记忆和长期记忆进行存储,并通过场景匹配算法实现不同场景GMM的快速自适应提取,提高对变化环境的适应能力;最后,通过在Matlab以及ROS仿真环境下6自由度柔性机械臂的运动规划仿真实验对所提出的算法进行验证.实验结果表明,所提出算法可以快速提取场景的记忆信息,有效提高多场景环境下的运动规划效率,具有较强的适应性.

Aiming at the problem of that the rapid-exploration random trees(RRT)algorithm is difficult to solve the rapid motion planning of a manipulator in the multi-scene environment,a fast motion planning algorithm combining long and short time memory mechanism is proposed.Firstly,the paper uses the Gaussian mixture model(GMM)to build a scene model of the environment through random sampling in the initial stage of planning,and uses this model for collision detection to improve the efficiency of motion planning.Then,according to the principle of human memory mechanism,different GMMs for multiple scenes are stored according to real-time memory,short-term memory and long-term memory,and the scene matching algorithm is used to achieve fast adaptive extraction of different scene GMM to improve the adaptability to changing environments.Finally,the proposed algorithm is validated by the grab simulation experiment of the 6-DOF flexible manipulator in Matlab and ROS simulation environment.Experimental results show that the algorithm can quickly extract the memory information of the scene,effectively improve the efficiency of motion planning in multi-scene environment,and has strong adaptability.