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High-dimensional robot dynamic trajectory planning poses many challenges for traditional planning algorithms. Existing planning methods suffer from issues such as long computation times, limited capacity to address intricate obstacle models, and lack of consideration for external disturbances and measurement inaccuracies in these high-dimensional systems. To tackle these challenges, this paper proposes a novel trajectory planning approach that combines Dynamic Control Barrier Function (DCBF) with a disturbance observer to create a Robust Dynamic Control Barrier Function (RDCBF) planner. This approach successfully plans trajectories in environments with complex dynamic obstacles while accounting for external disturbances and measurement uncertainties, ensuring system safety and enabling precise obstacle avoidance. Experimental results on a mobile manipulator demonstrate outstanding performance of the proposed approach.
Xu et al. (Sat,) studied this question.