Kinetostatic Path Planning for Continuum Robots By Sampling on Implicit Manifold

TL;DR

This paper introduces a novel method for path planning of continuum robots in complex environments by sampling on an implicit manifold, effectively handling multiple configurations and external contacts.

Contribution

It models the feasible configurations as a smooth manifold and applies a sampling-based planner, AtlasRRT*, for efficient path planning in these complex scenarios.

Findings

Outperforms Euclidean space planners in success rate

Achieves higher computational efficiency

Handles external elastic contact effectively

Abstract

Continuum robots (CR) offer excellent dexterity and compliance in contrast to rigid-link robots, making them suitable for navigating through, and interacting with, confined environments. However, the study of path planning for CRs while considering external elastic contact is limited. The challenge lies in the fact that CRs can have multiple possible configurations when in contact, rendering the forward kinematics not well-defined, and characterizing the set of feasible robot configurations as non-trivial. In this paper, we propose to solve this problem by performing quasi-static path planning on an implicit manifold. We model elastic obstacles as external potential fields and formulate the robot statics in the potential field as the extremal trajectory of an optimal control problem obtained by the first-order variational principle. We show that the set of stable robot configurations is a smooth manifold diffeomorphic to a submanifold embedded in the product space of the CR actuation and base internal wrench. We then propose to perform path planning on this manifold using AtlasRRT*, a sampling-based planner dedicated to planning on implicit manifolds. Simulations in different operation scenarios were conducted and the results show that the proposed planner outperforms Euclidean space planners in terms of success rate and computational efficiency.