DCBF-based Trajectory Planning for Mobile Manipulators in Complex and Dynamic Work Environments

Traditional trajectory planning methods are challenged by high-dimensional robot navigation, particularly in handling high-velocity obstacles and computation efficiency. This paper introduces a novel approach leveraging Dynamic Control Barrier Functions (DCBF) to address these issues. The proposed method ensures safety and precise obstacle avoidance in dynamic environments, demonstrated through superior performance in mobile manipulator experiments. Key contributions include the design of efficient DCBF functions, real-time trajectory planning under dynamic conditions, and validation of the algorithm's effectiveness, offering a significant advancement for mobile manipulators in complex work settings.