Minimally invasive surgery has undergone significant advancements in recent years, transforming various surgical procedures by minimizing patient trauma, postoperative pain, and recovery time. However, the use of robotic systems in minimally invasive surgery introduces significant challenges related to the control of the robot’s motion and the accuracy of its movements. In particular, the inverse kinematics (IK) problem is critical for robot-assisted minimally invasive surgery (RMIS), where satisfying the remote center of motion (RCM) constraint is essential to prevent tissue damage at the incision point. Several IK strategies have been proposed for RMIS, including classical inverse Jacobian IK and optimization-based approaches. However, these methods have limitations and perform differently depending on the kinematic configuration. To address these challenges, we propose a novel concurrent IK framework that combines the strengths of both approaches and explicitly incorporates RCM constraints and joint limits into the optimization process. In this paper, we present the design and implementation of concurrent inverse kinematics solvers, as well as experimental validation in both simulation and real-world scenarios. Concurrent IK solvers outperform single-method solvers, achieving a 100% solve rate and reducing the IK solving time by up to 85% for an endoscope positioning task and 37% for a tool pose control task. In particular, the combination of an iterative inverse Jacobian method with a hierarchical quadratic programming method showed the highest average solve rate and lowest computation time in real-world experiments. Our results demonstrate that concurrent IK solving provides a novel and effective solution to the constrained IK problem in RMIS applications.

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system’s robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system’s potential for real-world application.

The growing need for high levels of autonomy in Autonomous Robotic Surgery Systems (ARSS) calls for innovative approaches to reduce surgeons’ cognitive load, optimize hospital workflows, and ensure efficient task-level reasoning and adaptation during execution. This paper presents a novel hybrid framework that synergistically combines Task-Motion Planning and Dynamic Behavior Trees for ARSS in Minimally Invasive Surgery. Our approach is designed to address the challenges of coordinating multiple surgical tools within a small workspace, thereby making complex surgical tasks like multi-throw suturing feasible and efficient. Through an extensive evaluation in simulation across diverse initial conditions and noise scenarios, the proposed method demonstrates improved success rates, reduced execution times, and fewer regrasps compared to standalone approaches. Furthermore, it showcases robustness under increased noise conditions. By applying our framework to a complex multi-throw suturing task, we illustrate its capability to seamlessly handle comprehensive suturing tasks, including needle picking, insertion, extraction, and the handover of the needle between Patient Side Manipulators. The results suggest that our hybrid approach not only enhances ARSS autonomy but also adapts effectively to unexpected environmental changes, laying the groundwork for its potential applicability in real-world surgical robotics.

A visual assistance system has become attractive as a technique to improve the efficiency and stability of remote control. While an operator controls a working robot, another autonomous monitoring robot evaluates a suitable viewpoint to observe the work site, and dynamically moves to the optimal viewpoint for monitoring. Choosing the observation region (ROI: region of interest) is equivalent to deciding the action of the following autonomous monitoring system. Therefore, we focus on ROI detection in our visual support system. We propose an ROI selection method to identify the most suitable observation point and interobject relations. The monitoring robot detects a gestalt of the scene in order to identify the relations between objects. Such an adaptive ROI in real time improves the efficiency of the remote control. The experimental results indicate the effectiveness of the proposed system in terms of execution time and number of errors.

Adjustable autonomy is an attractive paradigm to deploy autonomous robots that require occasional interaction with humans or partner robots. In multi-robot applications, autonomy levels such as teleoperation and fully autonomous are extended to team levels, and depending on the task might have complex hierarchical relations.This paper presents a preliminary work on multi-robot coordination strategy based on evaluating the robot’s level of autonomy. With appropriate assumptions, choosing the level of autonomy can be interpreted as a cooperative planning problem. To this end, we propose to encode the robot’s task and motion plans as a Behavior Tree (BT) to monitor the execution and react to external disturbances. Our approach combines an informative path planning with BT synthesis to obtain a plan that allows a robot to explore and act depending on the uncertainty in an environment representation. We demonstrate how a robot can switch between the single and cooperative execution depending on the exploration’s outcome in a navigation among movable objects scenario.