A Sampling-Based Algorithm for Planning Smooth Nonholonomic Paths

The ability to navigate in an environment is essential to the autonomy of mobile robots and unmanned autonomous vehicles. Informally, path planning computes a collision-free path from a start location to a goal location in a known environment. Computing such paths accounting for the kinematics of the robot is a problem widely addressed in the literature, often focusing on feasibility and optimality of the planned paths. Although the smoothness of the paths is a major concern in most applications, the widely used sampling-based approaches often produce quirky winding paths. In this paper, we propose a novel path planning algorithm that is able to produce smooth paths, particularly when considering nonholonomic robot kinematics, like the differential drive kinematics. Comparative experiments show the effectiveness of the proposed algorithm in producing smooth paths.

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