CritBug: A Bug Algorithm for Path Planning of Intelligent Vehicle

Path planning is crucial for the performance of intelligent vehicles. Traditional path planning algorithms face the issues of long planning time and suboptimal paths in obstacle-rich environments. Therefore, this paper proposes a global path planning algorithm called CritBug. In the proposed algorithm, the concept of Critical-Points is introduced to ensure that the path can be acquired more efficiently. Guided by the Critical-Points, repetitive obstacle-based detections can be avoided, which achieves much shorter planning time. Meanwhile, considering the asymmetry of obstacles, bidirectional planning is employed to guarantee the optimality of the path. Comparative case studies are conducted to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm delivers shorter computational time and comparable path lengths compared to other widely used planners.