RRT2.0 for Fast and Optimal Kinodynamic Sampling-Based Motion Planning

by   Michal Kleinbort, et al.

We present RRT2.0: a simple yet efficient tree-based planner for asymptotically-optimal motion planning with kinodynamic constraints. RRT2.0 uses forward propagation and does not rely on the availability of a two-point boundary-value solver. The latter is a limiting requirement for some kinodynamic planners that are asymptotically optimal. The proposed approach improves upon a technique by Hauser and Zhou (2016), who explore an augmented state space with an additional coordinate, which endows every point in space with its cost-to-come value. Importantly, our optimality proofs require a milder and easily-verifiable set of assumptions on the problem and system: Lipschitz-continuity of the cost function and the dynamics. In particular, we prove that for any system satisfying those assumptions, any trajectory having a piecewise-constant control function and positive clearance from obstacles can be approximated arbitrarily well by a trajectory found by RRT2.0. We provide experimental results demonstrating on a couple of robot models with kinodynamic constraints that RRT2.0 outperforms the existing alternatives in practice.


A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods

Motion planning is a fundamental problem in autonomous robotics. It requ...

Probabilistic completeness of RRT for geometric and kinodynamic planning with forward propagation

The Rapidly-exploring Random Tree (RRT) algorithm has been one of the mo...

BITKOMO: Combining Sampling and Optimization for Fast Convergence in Optimal Motion Planning

Optimal sampling based motion planning and trajectory optimization are t...

LTO: Lazy Trajectory Optimization with Graph-Search Planning for High DOF Robots in Cluttered Environments

Although Trajectory Optimization (TO) is one of the most powerful motion...

Sampling-based optimal kinodynamic planning with motion primitives

This paper proposes a novel sampling-based motion planner, which integra...

Towards Learning Efficient Maneuver Sets for Kinodynamic Motion Planning

Planning for systems with dynamics is challenging as often there is no l...

Non-Linearity Measure for POMDP-based Motion Planning

Motion planning under uncertainty is essential for reliable robot operat...

Please sign up or login with your details

Forgot password? Click here to reset