融合TangentBug与Dubins曲线的智能轮式车辆局部路径规划算法

Due to the limitation of environmental conditions, some intelligent wheeled vehicle with Ackermann steering can only obtain local map and location, which makes path planning difficult. To solve the problem, this paper proposes a local path planning algorithm combining TangentBug and Dubins path. Firstly, a set of reference points is established by sampling method. Then, Dubins path is generated as the planning path to satisfy the motion constraints of the minimum turning radius of the vehicle and the desired heading at the target point. Besides, collision checking along the planning path and mode switching rules considering the positioning error are added. Finally, real vehicle tests are conducted with results showing that the proposed algorithm can navigate the vehicle to the target with the required state while meeting the safety and real-time performance requirements of path planning, and eliminate the disturbance from the error of positioning. Compared with using arc curve, the path planned by this algorithm is more beneficial to path following control.