Hybrid Robot Trajectory Planning Using FC-SSA-PID and DWA-Enhanced BIT*Algorithms

Abstract

Planning the movement path of a robot is crucial to ensure it reaches the target area smoothly. Existing methods tend to fall into local optima, have low accuracy in route calculation, and fail to effectively avoid obstacles. To address these issues, this study introduces the Sparrow Search Algorithm and Fuzzy Control, as well as the Dynamic Window Approach, to optimize Proportional-Integral-Derivative control and Batch Informed Trees, respectively. Based on these two optimization algorithms, a robot trajectory planning model is proposed, and its feasibility and reliability are demonstrated through comparative experiments. In standardized 50m×50m grid environments with 20%-30% obstacle density and dynamic obstacles, 30 independent simulation runs were conducted. Comparative analysis with RRT*, Ant Colony Optimization (ACO), and Genetic Algorithm (GA) demonstrates that the proposed model achieves a success rate of 95.5%, a high accuracy rate of 99.4%, and a low accuracy error rate of 0.0011%. The locally optimal route length planned by the model is 12.6m, while the global average optimal route length is reduced to 21.2m, significantly outperforming the comparison models. These findings demonstrate that the proposed model has strong trajectory planning capabilities, minimal error, and shorter routes, enabling the robot to respond correctly to external environments in a timely manner and complete tasks effectively even in complex dynamic conditions.