Path Optimization for Ground Vehicles in Off-Road Terrain
This work provides a method for generating kinematically feasible paths for off-road ground vehicles, which is important for autonomous navigation in challenging environments.
This paper addresses path optimization for high-speed off-road ground vehicles by representing paths as a list of steering angles, which transforms kinematic constraints into steering angle limits. The method uses a gradient descent solver to produce kinematically feasible and optimized paths, tested successfully in simulation and on an off-road vehicle at 5 m/s.
We present a method for path optimization for ground vehicles in off-road environments at high speeds. This path optimization considers the kinematic constraints of the vehicle. By thinking in the actuator space we can represent these constraints as limits in the space rather than derived properties of the path. In this paper we present a actuator space approach to path optimization for off-road ground vehicles. This is done by representing and operation on the path as a list of steering angles over the path length. This transforms the set of kinematic constraints into constraints on the steering angle. We then put this path into a gradient descent solver. This produced paths that are kinematically feasible and optimized in accordance with our cost function. Finally, we tested the system both in simulation and on an off-road vehicle at speeds of 5 m/s.