Autonomous Robotic Tissue Palpation and Abnormalities Characterisation via Ergodic Exploration

2026-02-15

AI Summary

This work addresses the challenge of efficiently identifying abnormally stiff regions during autonomous robotic palpation by proposing an adaptive palpation framework based on ergodic control. The approach integrates model uncertainty, tissue stiffness magnitude, and spatial stiffness gradients to construct an expected information density that guides the robot to prioritize exploration of pathology-relevant areas. An extended Kalman filter is employed for online estimation of viscoelastic parameters, which, combined with Gaussian process regression, enables the construction of an elasticity map. A heat-equation-driven controller then generates continuous exploration trajectories. Experimental results demonstrate that the proposed method significantly outperforms Bayesian optimization on both synthetic stiffness maps and silicone phantoms, achieving improved reconstruction accuracy, enhanced anomaly segmentation, and greater robustness in detecting stiff inclusions.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

We propose a novel autonomous robotic palpation framework for real-time elastic mapping during tissue exploration using a viscoelastic tissue model. The method combines force-based parameter estimation using a commercial force/torque sensor with an ergodic control strategy driven by a tailored Expected Information Density, which explicitly biases exploration toward diagnostically relevant regions by jointly considering model uncertainty, stiffness magnitude, and spatial gradients. An Extended Kalman Filter is employed to estimate viscoelastic model parameters online, while Gaussian Process Regression provides spatial modelling of the estimated elasticity, and a Heat Equation Driven Area Coverage controller enables adaptive, continuous trajectory planning. Simulations on synthetic stiffness maps demonstrate that the proposed approach achieves better reconstruction accuracy, enhanced segmentation capability, and improved robustness in detecting stiff inclusions compared to Bayesian Optimisation-based techniques. Experimental validation on a silicone phantom with embedded inclusions emulating pathological tissue regions further corroborates the potential of the method for autonomous tissue characterisation in diagnostic and screening applications.

Problem

• autonomous robotic palpation
• tissue abnormalities characterisation
• elastic mapping
• ergodic exploration
• viscoelastic tissue model

Innovation

• ergodic exploration
• viscoelastic tissue modeling
• Expected Information Density
• autonomous palpation
• Gaussian Process Regression