High quality goal connection for nonholonomic obstacle navigation allowing for drift using dynamic potential fields

The problem we address in this paper is how to plan and execute high quality paths for robots subject to nonholonomic constraints while navigating obstacles in 2D space. The navigation is to be carried out continuously at speed and may be subject to drift that is not predictable a priori. The problem raises the challenge of adaptively maintaining a smooth robust path of low computational cost. The algorithm is complete in providing feasible paths connecting to the goal in cluttered environments without global maps or positioning while also optimising the path curvature in free space. The approach is a generic gradient-based methodology set in dynamic potential fields that are not subject to fixed local minima or other misdirecting surface features of static fields. Multiple planning and execution cycles are interleaved to allow frequent updates for dealing with unanticipated obstacles and drift. We present our methodology and demonstrate experimental results for simulated robots. The results show that low curvature paths are found that robustly connect to the goal under perturbation through a sequence of fast adaptive replanning.