机器人自主触诊，精准定位病变组织。
Autonomous Robotic Tissue Palpation and Abnormalities Characterisation via Ergodic Exploration
- 结合力传感器与自适应探索策略，实时估计组织弹性。
- 比贝叶斯优化方法更准、更稳，能更好识别硬块。
- 适合医学诊断中需要自动探测异常的场景。
我们提出一种新型自主机器人触诊框架，通过粘弹性组织模型实现实时弹性成像。该方法利用商用力/力矩传感器进行基于力的参数估计，并采用针对期望信息密度设计的遍历控制策略，同时考虑模型不确定性、刚度大小和空间梯度，主动引导探索至诊断相关区域。通过扩展卡尔曼滤波在线估计粘弹性模型参数，结合高斯过程回归实现弹性空间建模，再以热方程驱动的区域覆盖控制器完成自适应连续轨迹规划。在合成刚度图上的仿真表明，该方法在重建精度、分割能力及对硬质异物的检测鲁棒性方面均优于基于贝叶斯优化的方法。在含嵌入异物的硅胶仿体实验中进一步验证了其在诊断与筛查应用中实现自主组织表征的潜力。
原文摘要 · Abstract (English)
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.
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