Neural Configuration Distance Function for Continuum Robot Control

TL;DR

This paper introduces a neural distance function for continuum robots that enables efficient shape modeling and collision checking, integrated with a control algorithm for safe motion planning in complex environments.

Contribution

It proposes a novel neural shape representation called N-CEDF, combining it with MPPI control for improved continuum robot navigation.

Findings

N-CEDF accurately models robot shape and enables fast collision checking.

Integration with MPPI allows safe trajectory generation in dynamic environments.

Validated on various simulated scenarios with static and moving obstacles.

Abstract

This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields for each link and combining them through the kinematics chain, the learned N-CEDF provides an accurate and computationally efficient representation of the robot's shape. The key advantage of a distance function representation of a continuum robot is that it enables efficient collision checking for motion planning in dynamic and cluttered environments, even with point-cloud observations. We integrate the N-CEDF into a Model Predictive Path Integral (MPPI) controller to generate safe trajectories for multi-segment continuum robots. The proposed approach is validated for continuum robots with various links in several simulated environments with static and dynamic obstacles.