Research on Path Smoothing Optimization based on Improved RRT-Connect Algorithm and third-order Bezier curve

Abstract

Targeting the deficiencies of the original RRT-Connect path planning algorithm in dealing with obstacle avoidance, planning efficiency and path smoothing in static environments, this paper proposes an improved path optimization method that fuses the RRT-Connect path planning algorithm with the Greedy search strategy and adopts the third-order Bezier curve for path smoothing. First, the Greedy strategy is integrated into the path planning process of the original RRT-Connect algorithm to guide the search direction, make the path search goal-oriented, reduce the time of path planning, and improve the efficiency. Second, the generated paths are smoothed with third-order Bezier curves, and the control point selection strategy is used to set reasonable values of sampling variables to reduce the complexity of path smoothing of RRT-Connect algorithm, and to reduce the phenomena such as sharp turns and folding back. Then, the smooth path with continuous curvature is generated to ensure that the intelligent vehicle can perform path tracking. Finally, the algorithm before and after optimization is simulated and analyzed to check the performance of the algorithm and view the effect of path smoothing after optimization. The results show that the global path length of the optimized algorithm is reduced by an average of 14.41% and the path planning time is shortened by an average of 32.78% compared with the original RRT-Connect algorithm in complex environments. The method proposed in this paper is efficient, with short planning paths and smoother generated paths.