Yang S M, Lin Y A
Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan City 70101, Taiwan.
Sensors (Basel). 2021 Mar 23;21(6):2244. doi: 10.3390/s21062244.
Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a "near"-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional-integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.
已经开发出用于自动驾驶车辆避障的安全路径规划方法。基于快速扩展随机树（RRT）算法，一种集成了路径修剪、平滑和优化以及几何碰撞检测的改进算法被证明可以提高规划效率。路径修剪是路径平滑的前提，它用于去除随机树为新路径生成的冗余点，同时不与障碍物碰撞。路径平滑用于修改路径，使其在车辆可实现的曲率方面变得连续可微。优化用于在可行路径中选择“接近”最优的最短距离路径，以提高运动效率。在实验验证中，应用了纯追踪转向控制器和比例积分速度控制器，以使自动驾驶车辆跟踪由改进的RRT算法预测的规划路径。结果表明，车辆能够成功高效地跟踪路径并安全到达目的地，平均跟踪控制偏差为车辆宽度的5.2%。该路径规划还应用于变道，变道期间和之后与车道的平均偏差保持在车辆宽度的8.3%以内。
