A New Intelligent Trajectory Planning Algorithm Based on Bug2 Algorithm:Bridge Algorithm* A new algorithm is developed for unmanned omnidirectional robot to establish a feasible path that avoids obstacles and does not have randomness in a given environment : Bridge Algorithm. The implementation of the Bridge Algorithm is similar to the process of finding the shortest feasible path for human beings, which determines the straight line where the Starting point and the end point are located, then determines the obstacles intersecting with the straight line, selects the short edge of the obstacle contour as the obstacle avoidance path, and repeats the process until the end point. The implementation ensures the consistent of the path generated by the algorithm, i.e., for the same Starting point, end point and environment, the path generated each time is consistent. This consistency is crucial for application occasions that emphasize safety and stability. According to the experimental results of the algorithm in the real unmanned omnidirectional wheel robot, Bridge Algorithm has good realtime performance, and the actual walking trajectory of the robot is basically the same as the planned trajectory, which proves that the algorithm has good performance.

An Improved Local Dynamic Path Planning Algorithm for Autonomous Driving In this paper, an improved autonomous driving local dynamic path planning algorithm is proposed. Based on the predefined road center points, a set of path control points is constructed, and a onedimensional cubic equation is used to fit the path and construct a center line. A new curved coordinate system is provided using the center line, and the path candidates are generated by arc length and lateral offset. The overall path is selected in consideration of the total cost of path safety and comfort. The results showed that under different scenarios, the proposed local path planning algorithm can plan an optimal path that does not collide with static obstacles, and can ensure the comfort of autonomous driving vehicles and the realtime path planning.