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Rapidly exploring random trees (RRTs) have proven effective in quicklyfinding feasible solutions to complex motion planning problems. RRT* is anextension of the RRT algorithm that provides probabilistic asymptoticoptimality guarantees when using straight-line motion primitives. This workprovides extensions to RRT and RRT* that employ fillets as motion primitives,allowing path curvature constraints to be considered when planning. Two filletsare developed, an arc-based fillet that uses circular arcs to generate pathsthat respect maximum curvature constraints and a spline-based fillet that usesBezier curves to additionally respect curvature continuity requirements.Planning with these fillets is shown to far exceed the performance of RRT*using Dubin's path motion primitives, approaching the performance of planningwith straight-line path primitives. Path sampling heuristics are alsointroduced to accelerate convergence for nonholonomic motion planning.Comparisons to established RRT* approaches are made using the Open MotionPlanning Library (OMPL).

Fillet-based RRT*: A Rapid Convergence Implementation of RRT* for Curvature Constrained Vehicles

Fillet-based RRT: A Rapid Convergence Implementation of RRT for Curvature Constrained Vehicles