Robot Motor Motion Planning Algorithm Based on Improved TEB

To address the issues of global path deviation, path discontinuity, and robot motion discontinuity in dense obstacle environments inherent in the traditional time elastic band(TEB) algorithm, an improved TEB smooth path planning algorithm combined with motor control optimization is proposed. The algorithm initially employs dynamic adaptive sampling of the robot’s static pose, ensuring close adherence to the global path in high-density obstacle regions. Based on this, the TEB optimization objective function is extended by incorporating a trajectory continuity cost constraint through quantic polynomial interpolation. This incorporation reduces the motor’s frequent acceleration and deceleration, thereby facilitating smoother path planning and more continuous robot motion. Simulation comparison experiments are conducted, the results show that, compared with existing benchmark algorithms, the proposed algorithm plans trajectories that are closer to the global path and smoother, while maintaining stable motor operation. This enables efficient navigation of the robot through dense obstacle areas. The research presented offers an effective method for enhancing robots’ path planning capabilities and motor control performance in complex environments.