Safety-Aware Multi-Objective Motion Control With Online Hand–Eye Coordination for Robotic Laser Manipulation in Laparoscopic Surgery

Abstract

In laparoscopic surgery, vision-based robotic autonomous laser manipulation requires precise and flexible motion control within anatomical environments. However, challenges arise from hand–eye misalignment and multi-objective constraints. To address these issues, this paper proposes a unified multi-objective motion fusion control method for robotic laser manipulation. First, an online mapping-matrix updating strategy is developed to continuously estimate the relationship between fiber-tip motion and pixel-space laser-spot motion, thereby calibrating hand-eye misalignment caused by multiple error sources. Based on this, a multi-objective motion fusion controller is developed by integrating trajectory navigation, obstacle avoidance, and trajectory accuracy with dedicated evaluation functions. Through prediction-based evaluation and gradient-driven adaptive weighting, the controller dynamically coordinates these objectives to produce a unified control input. Extensive simulations and robotic experiments validate the effectiveness of the proposed method in hand–eye coordination and safe laser-spot manipulation. Quantitative results further demonstrate that the proposed method improves trajectory tracking accuracy by 53.1% in the hand–eye consistency experiment. In the multi-objective motion fusion experiment, the intrusion area of the laser trajectory into the avoidance region is reduced by 49.41%. Note to Practitioners—The motivation of this research is to achieve flexible and precise autonomous laser manipulation in hand–eye decoupled laparoscopic laser systems operating in multi-objective task. Existing approaches often suffer from hand–eye misalignment caused by multiple error sources and face difficulties in satisfying multi-objective requirements. This research proposes a practical control method that continuously updates the motion mapping between the robot space and the image space using online motion data, ensuring a high-level hand–eye consistency even in the presence of multiple disturbances. Multiple motion objectives are evaluated in real time and adaptively fused into a unified control input, enabling the system to automatically adjust objective weights according to the current laser motion state. The proposed method provides robust hand–eye coordination and flexible laser motion control, and can be deployed on existing robotic platforms with standard multi-port laparoscopic configurations.