Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction

TL;DR

This paper introduces CL-RRT#, a novel sampling-based motion planning algorithm that uses closed-loop prediction to efficiently handle complex unstable dynamics and improve solution optimality in kinodynamic planning.

Contribution

The paper presents CL-RRT#, a new algorithm combining RRT# ideas with closed-loop trajectory prediction to address unstable dynamics and avoid complex steering computations.

Findings

Handles unstable dynamics effectively.

Improves solution quality through alternative trajectory search.

Demonstrates benefits via numerical simulations.

Abstract

Motion planning under differential constraints, kinodynamic motion planning, is one of the canonical problems in robotics. Currently, state-of-the-art methods evolve around kinodynamic variants of popular sampling-based algorithms, such as Rapidly-exploring Random Trees (RRTs). However, there are still challenges remaining, for example, how to include complex dynamics while guaranteeing optimality. If the open-loop dynamics are unstable, exploration by random sampling in control space becomes inefficient. We describe a new sampling-based algorithm, called CL-RRT#, which leverages ideas from the RRT# algorithm and a variant of the RRT algorithm that generates trajectories using closed-loop prediction. The idea of planning with closed-loop prediction allows us to handle complex unstable dynamics and avoids the need to find computationally hard steering procedures. The search technique presented in the RRT# algorithm allows us to improve the solution quality by searching over alternative reference trajectories. Numerical simulations using a nonholonomic system demonstrate the benefits of the proposed approach.