Motion planning for car-like vehicles in dynamic urban scenarios

This paper focuses on development of a motion planning strategy for car-like vehicles in dynamic urban-like scenarios. The strategy can be summarized as a search for a collision-free trajectory among linearly moving obstacles applying Rapidly-exploring Random Trees (RRT) and B-splines. Collision avoidance is based on geometric search in transformed state space of chained form kinematic model decomposition. The time criterion for avoiding obstacles is based on relative robot to obstacle motion and is checked iteratively for possible collisions within the RRT exploration phase. The line segment geometric path is interpolated with a B-spline curve in order to generate a feasible trajectory that takes into account non-holonomic constraints. The exploration strategy aims at finding an optimal steering and longitudinal control of the vehicle in minimum time and steering activity sense. In order to test the strategy a MatLab based simulator was developed. This simulator reproduces a simple 2D urban-like environment with parked and moving cars, buses, trucks, people, buildings, streets, and trees. The test vehicle, a modified Smart Car equipped with several sensors was kinematically modeled. The sensor data are extracted from the environment based on its geometrical description and used as input data for the motion planning strategy which was verified in a dynamic urban scenario simulation.