MPC-DCBF Based Motion Planning of Mobile Manipulator in Dynamic Environment

Traditional planning algorithms face many problems in trajectory planning for mobile robot arms, including long path planning time, long computation and data processing time, and limited ability to solve complex obstacle environments. To address these challenges and issues, this paper proposes a method that combines global path planning with local path safety planning. Using Rapidly exploring Random Trees Star (RRT*) for global planning of mobile chassis, combined with the tasks given by Model Predictive Control (MPC) at the end of the robotic arm, and using Dynamic Control Barrier Function (DCBF) method to provide overall safety assurance for the mobile robotic arm. This method can plan trajectories in environments with complex obstacles while considering external disturbances, ensuring system safety and achieving obstacle avoidance.