An Efficient RRT-Based Framework for Planning Short and Smooth Wheeled Robot Motion Under Kinodynamic Constraints
Biao Hu, Zhengcai Cao, MengChu Zhou
Abstract
This article presents a framework that extends a rapidly exploring random tree (RRT) algorithm to plan the motion for a wheeled robot under kinodynamic constraints. Unlike previous RRT-based path planning algorithms that apply complex steer functions during a path sampling phase, this framework uses a straight line to connect a pair of sampled waypoints such that an obstacle-free path can be quickly found. This path is further pruned by the short-cutting algorithm. Under the kinodynamic constraints, we propose a motion-control law that is guided by a pose-based steer function for the robot to reach its destination in a short time. A path deformation strategy is presented that shifts the waypoint away from the collision point such that the trajectory can be generated without any collision. Simulation results demonstrate that the proposed framework needs less computation to generate a smoother trajectory with shorter length than its peers, and experimental results show that simulated trajectories of our controller are very close to real ones and the performance is better than that of a prior pose-based controller.