Resection of pathological tissue is a common procedure in surgical oncology for treating tumors. In robot-assisted electrosurgery, the use of predefined markers to guide autonomous robotic resection is gaining traction. Accurate tracking of these markers and minimizing electrocautery damage are critical for the safe and effective autonomous resection of tumors. This paper introduces a safety enhanced autonomous resection method for laparoscopic surgery, designed to mitigate the risks posed by tissue deformation during the resection process. Initially, we pre-plan the cutting path and design a switching strategy for navigation waypoints based on a preview tracking mechanism. Then, we develop a depth-fused navigation controller and a safe withdrawal motion controller. Next, an inertial tracking mechanism is established to evaluate tissue deformation over short periods. Finally, we develop a confidence generator to fuse the two controllers, ensuring that tissue deformation during the resection process does not cause additional electrocautery damage. Simulation and phantom experiments were conducted, demonstrating the effectiveness of our proposed method. This work represents a significant step toward achieving autonomous robotic resection.

In minimally invasive surgery (MIS), tissue retraction is critical for exposing the surgical site and facilitating pathological tissue excision. However, tissue retraction in MIS is subject to multiple constraints, including restricted field of view, non-damaging tissue retraction force, and limited instrument operating range, which hinder the safe and continuous retraction of tissue during robotic-assisted surgery. This paper introduces a novel autonomous retraction method for MIS, capable of safely retracting tissue within the aforementioned multiple constraints, assisting the surgeon in tissue excision. The method takes into account information from three different modalities: vision, force, and position. Based on these, three distinct control objectives are defined: retraction angle, retraction force, and safety space constraints, with an individual controller and modelpredictive evaluation function designed for each objective. We propose a novel multi-objective motion fusion strategy designed to balance three distinct control objectives. This strategy evaluates the sensitivity of each objective to changes in the system state by comparing the gradients of their respective prediction evaluation functions and fuses the control inputs of the individual controllers. The proposed method allows for rapid addition or removal of objectives without altering the algorithmic framework. Experiments with phantoms andex vivoanimal tissue are conducted on robotic platform to validate the effectiveness of the proposed method in various configurations.

Laparoscope-holding robots significantly enhance the stability and precision of visualization in minimally invasive surgeries. Most existing robots of this kind depend on visual servo systems and struggle with efficient, rapid adjustments in the field-of-view (FOV), especially when identifying organs and needles outside the FOV. This paper presents a laparoscope-holding robot system capable of employing both vision-driven and force-driven mechanisms for continuous and large-scale FOV adjustments, respectively. The system features an integrated tactile handle, enabling the reception of human-robot interaction forces during surgical navigation. We propose a hybrid control method that leverages both force and vision inputs for laparoscopic FOV adjustments. This approach integrates a virtual wrench, generated from visual information, and an interaction wrench, obtained from the tactile handle, into the robot's dynamic model, which complies with remote center of motion constraints. The interaction wrench's gain is adjusted with the gripping force on the integrated tactile handle, ensuring that unintended movements caused by accidental contacts are prevented, thus safeguarding operational safety. The proposed method eliminates the need to switch control modes, enabling simultaneous visual tracking and tactile interaction guidance. Experimental results demonstrate that the proposed method not only allows for FOV adjustments with surgical instrument guiding but also adapts well to large-scale FOV adjustment tasks.

Numerous studies have emphasized the application of autonomous intelligence in human-robot shared control to enhance surgical convenience and efficiency. However, the neglect of human dominance may reduce surgical safety. This paper developed a safety-enhanced human-robot shared control method by intelligently allocating control authority, with the surgeon remaining the leader during the surgical procedure. Three controllers are designed initially, including a master hand position (MP) controller and a master hand velocity (MV) controller related to the surgeon's manipulation, and a planned trajectory tracking (PT) controller related to the robot. In precision surgical manipulation scenarios, precise tracking of the human's operation is achieved by combining MP and MV controllers, while a combination of MV and PT controllers is developed in high-efficiency surgical scenarios, which relaxes the requirement for precise tracking of hand position and enables precise robot assistance guided by the velocity of human hand. The autonomous scenarios and controllers switching are accomplished through a motion fusion mechanism, which is achieved via optimizing evaluation functions that are reliant on future states. Furthermore, a force feedback mechanism is proposed to help human understand the intent of autonomous control to improve safety. The feasibility and effectiveness of this method have been validated through simulations and experiments.