Force Perception in Robot Assisted Surgery: Integrating Force Sensing, Haptic Feedback, and AI Based Estimation for Next Generation Surgical Robotics

Robot assisted surgery has transformed minimally invasive procedures by delivering enhanced dexterity, tremor filtration, and three dimensional visualization. However, the near complete absence of force feedback in most deployed platforms remains a critical limitation, compelling surgeons to infer tissue interaction forces from visual cues alone, a deficiency associated with elevated tissue trauma, excess grasping forces, and prolonged training curves. This paper presents an integrative review of three coupled technology domains, force sensing hardware, haptic feedback modalities, and AI based force estimation, evaluating strain gauges, piezoelectric transducers, fiber Bragg grating sensors, and model based observers for sensing; kinesthetic, vibrotactile, and pneumatic tactile modalities for feedback; and deep learning, physics informed neural networks, and Gaussian process regression for sensorless force estimation. Drawing on these analyses, a three layer conceptual framework for next generation force aware surgical robots is proposed, with explicit consideration of deployment feasibility in resource limited environments, including sub Saharan African clinical contexts.