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

TL;DR

This paper introduces fillet-based motion primitives for RRT* to better handle curvature constraints in nonholonomic vehicle path planning, significantly improving convergence speed and path quality.

Contribution

It develops arc-based and spline-based fillet primitives for RRT*, enabling curvature-aware planning with improved efficiency over traditional methods.

Findings

Fillet-based primitives outperform Dubin's path in RRT* planning.

Curvature constraints are effectively incorporated using arc and spline fillets.

Path planning convergence is accelerated with new heuristics.

Abstract

Rapidly exploring random trees (RRTs) have proven effective in quickly finding feasible solutions to complex motion planning problems. RRT* is an extension of the RRT algorithm that provides probabilistic asymptotic optimality guarantees when using straight-line motion primitives. This work provides extensions to RRT and RRT* that employ fillets as motion primitives, allowing path curvature constraints to be considered when planning. Two fillets are developed, an arc-based fillet that uses circular arcs to generate paths that respect maximum curvature constraints and a spline-based fillet that uses Bezier 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 planning with straight-line path primitives. Path sampling heuristics are also introduced to accelerate convergence for nonholonomic motion planning. Comparisons to established RRT* approaches are made using the Open Motion Planning Library (OMPL).