Kinodynamic Planning on Constraint Manifolds

TL;DR

This paper introduces a novel kinodynamic motion planner that operates on constraint manifolds, enabling efficient planning for systems with complex kinematic and dynamic constraints, validated on challenging robotic systems.

Contribution

It presents the first randomized kinodynamic planner for implicitly-defined state spaces using an incremental atlas construction approach.

Findings

Successfully plans in complex systems with kinematic loops.

Constructs an atlas to handle implicit manifolds effectively.

Demonstrates effectiveness on challenging robotic scenarios.

Abstract

This paper presents a motion planner for systems subject to kinematic and dynamic constraints. The former appear when kinematic loops are present in the system, such as in parallel manipulators, in robots that cooperate to achieve a given task, or in situations involving contacts with the environment. The latter are necessary to obtain realistic trajectories, taking into account the forces acting on the system. The kinematic constraints make the state space become an implicitly-defined manifold, which complicates the application of common motion planning techniques. To address this issue, the planner constructs an atlas of the state space manifold incrementally, and uses this atlas both to generate random states and to dynamically simulate the steering of the system towards such states. The resulting tools are then exploited to construct a rapidly-exploring random tree (RRT) over the state space. To the best of our knowledge, this is the first randomized kinodynamic planner for implicitly-defined state spaces. The test cases presented in this paper validate the approach in significantly-complex systems.