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.

翻译：本文提出了一种新型自主机器人触诊框架，利用粘弹性组织模型在组织探查过程中实现实时弹性映射。该方法将基于商用六维力/力矩传感器的力参数估计与一种由定制化期望信息密度驱动的遍历控制策略相结合，通过联合考虑模型不确定性、刚度大小及空间梯度，明确地将探索偏向于诊断相关区域。采用扩展卡尔曼滤波器在线估计粘弹性模型参数，同时利用高斯过程回归对估计的弹性进行空间建模，并通过热方程驱动的区域覆盖控制器实现自适应连续轨迹规划。在合成刚度图上的仿真实验表明，与基于贝叶斯优化的技术相比，所提方法在重建精度、分割能力及检测刚性包埋体的鲁棒性方面均表现更优。在模拟病理组织区域的嵌入式包埋体硅胶模型上进行的实验验证进一步证实了该方法在诊断与筛查应用中实现自主组织特征化的潜力。