Trajectory Planning for Car-Like Robots in Unknown, Unstructured Environments

We describe a variable-velocity trajectory planning algorithm for navigating car-like robots through unknown, unstructured environments along a series of possibly corrupted GPS waypoints. The trajectories are guaranteed to be kinematically feasible, i.e., they respect the robot's acceleration and deceleration capabilities as well as its maximum steering angle and steering rate. Their costs are computed using LiDAR and camera data and depend on factors such as proximity to obstacles, curvature, changes of curvature, and slope. In a second step, velocities for the least-cost trajectory are adjusted based on the dynamics of the vehicle. When the robot is faced with an obstacle on its trajectory, the planner is restarted to compute an alternative trajectory. Our algorithm is robust against GPS error and waypoints placed in obstacle-filled areas. It was successfully used at euRathlon 20131, where our autonomous vehicle MuCAR-3 took first place in the "Autonomous Navigation" scenario.