Trajectory Planning Of Manipulator Based On RBFNN And Kinematics Simulation

Robot's trajectory planning is very important in the control of robotics. Based on demands and restrictions of AS-MRobot6 manipulator, this paper presents a novel method of trajectory planning. By the researching processes of concrete analysis of trajectory planning on robot's manipulator arm, imitation of trajectory based on kinematics and optimization of trajectory in the articulation space, this paper establishes two models according to RBF neural networks algorithm, one is used for curve interpolation in operation space and the other is used for curve approximation in articulate space.To realize trajectory planning in operation space, an one-input-one-output RBF neural networks model is built and trained using x-axis value of discrete points as inputs and y-axis value outputs. Meanwhile, an one-input-six-output one is established and trained using discrete time as inputs and value of six angle outputs in articulate space.With character of rapid convergence and near approximation, this new algorithm is fault tolerant and irrelative with order of inputs, which can achieve firing requirement of engineering.Making use of the virtual prototyping technology, the manipulator's model is founded in software ADAMS that developed for dynamics analysis of mechanical system. Furthermore, according to the planned path, the changes of the kinematics and dynamics indexes of the manipulator in the course of moving are studied and its effectiveness and feasibility validated.