A composite motion planning scheme based on time-varying recurrent neural network for mobile robot manipulators
Xitong Gao, Luwen Yang, Zhijun Zhang · Robotics and Autonomous Systems · 2025
In order to solve the double ended (end-effector and mobile platform) motion planning problem of mobile robot manipulator, a composite motion planning (CMP) scheme based on time-varying recurrent neural network is proposed and analyzed. In traditional schemes, motion planning of the end-effector is common, the route of the platform is generated from the end-trajectory calculation. However, in realistic tasks, the end trajectory and platform route are often independent to each other. To do so, kinematic models of the double ended are first derived in detail and formulated as equality constraints, respectively. Secondly, the posture constraint and combined physical constraint are designed and formulated as an equation and inequality constraint, respectively. Then, the CMP scheme is proposed and formulated as a constrained quadratic programming problem. Thirdly, the optimal solution of the quadratic programming problem is obtained by the designed time-varying recurrent neural network. Finally, experiments verify that the proposed CMP scheme can simultaneously plan the double ended of the mobile manipulator.