Shape-Aware Whole-Body Control for Continuum Robots with Application in Endoluminal Surgical Robotics

2025-10-14

AI Summary

In bronchoscopic and other endoluminal procedures, continuum robots suffer from low navigation accuracy and high tissue injury risk due to complex, patient-specific anatomies. To address this, we propose a shape-aware whole-body control framework. Our method integrates a physics-guided skeleton model with an enhanced neural ODE residual learning module to enable high-fidelity, real-time deformation estimation and analytical Jacobian computation. A task manager enables online target adjustment, while a sampling-based Model Predictive Path Integral (MPPI) controller jointly optimizes end-effector trajectory, skeleton-anatomy alignment, and dynamic obstacle avoidance. In simulation and phantom bronchoscopy experiments, the system achieves sub-millimeter tracking accuracy, significantly reduces airway wall contact frequency, and improves lumen-following capability and operational robustness—outperforming conventional joystick control and state-of-the-art baseline methods.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

This paper presents a shape-aware whole-body control framework for tendon-driven continuum robots with direct application to endoluminal surgical navigation. Endoluminal procedures, such as bronchoscopy, demand precise and safe navigation through tortuous, patient-specific anatomy where conventional tip-only control often leads to wall contact, tissue trauma, or failure to reach distal targets. To address these challenges, our approach combines a physics-informed backbone model with residual learning through an Augmented Neural ODE, enabling accurate shape estimation and efficient Jacobian computation. A sampling-based Model Predictive Path Integral (MPPI) controller leverages this representation to jointly optimize tip tracking, backbone conformance, and obstacle avoidance under actuation constraints. A task manager further enhances adaptability by allowing real-time adjustment of objectives, such as wall clearance or direct advancement, during tele-operation. Extensive simulation studies demonstrate millimeter-level accuracy across diverse scenarios, including trajectory tracking, dynamic obstacle avoidance, and shape-constrained reaching. Real-robot experiments on a bronchoscopy phantom validate the framework, showing improved lumen-following accuracy, reduced wall contacts, and enhanced adaptability compared to joystick-only navigation and existing baselines. These results highlight the potential of the proposed framework to increase safety, reliability, and operator efficiency in minimally invasive endoluminal surgery, with broader applicability to other confined and safety-critical environments.

Problem

• Enabling precise navigation of continuum robots through tortuous anatomical pathways
• Reducing tissue trauma by optimizing tip tracking and obstacle avoidance
• Improving safety in endoluminal surgery via shape-aware whole-body control

Innovation

• Physics-informed backbone model with residual learning
• Sampling-based MPPI controller for multi-objective optimization
• Task manager enables real-time objective adjustment