This paper presents an uncertainty-aware shared control and calibration method for micromanipulation using a digital microscope and a tool-mounted, multi-joint robotic arm, integrating real-time human intervention with a visual-motor policy. Our calibration algorithm leverages co-manipulation control to calibrate the hand-eye transformation without requiring knowledge of the kinematics of the microtool mounted on the robot while remaining robust to camera intrinsics errors. Experimental results show that the proposed calibration method achieves a 39.6% improvement in accuracy over established methods. Additionally, our control structure and calibration method reduces the time required to reach single-point targets from 5.74 s (best conventional method) to 1.91 s, and decreases trajectory tracking errors from 392 μm to 40 μm. These findings establish our method as a robust solution for improving reliability in high-precision biomedical micromanipulation.

This paper proposes a novel, more computationally efficient method for optimizing robot excitation trajectories for dynamic parameter identification, emphasizing self-collision avoidance. This addresses the system identification challenges for getting high-quality training data associated with co-manipulated robotic arms that can be equipped with a variety of tools, a common scenario in industrial but also clinical and research contexts. Utilizing the Unified Robotics Description Format (URDF) to implement a symbolic Python implementation of the Recursive Newton-Euler Algorithm (RNEA), the approach aids in dynamically estimating parameters such as inertia using regression analyses on data from real robots. The excitation trajectory was evaluated and achieved on par criteria when compared to state-of-the-art reported results which didn’t consider self-collision and tool calibrations. Furthermore, physical Human-Robot Interaction (pHRI) admittance control experiments were conducted in a surgical context to evaluate the derived inverse dynamics model showing a 30.1% workload reduction by the NASA TLX questionnaire.

In mandibular angle split osteotomy (MASO), prominent mandibular angles need to be cut off with saws such as reciprocating saws. Compared to traditional-freehand methods, robot-assisted methods provide potentials for better cutting performance. In the robot-assisted mandibular angle split osteotomy (RAMASO), a cutting method based on shared control is proposed along with an optimization-planned osteotomy curve. Experimental verification using planes and skull phantoms were conducted and discussed for evaluation of accuracy and safety. The results in 7 cutting experiments for the following error were mainly within 0.76mm and -1.00mm (Q3±1.5*IQR), peaking at 1.80 mm. The maximum of time-consuming was 304.0s, with the average human robot interactive force reaching around 3.3 N. Experiments indicate the proposed method achieves better performance in accuracy and efficiency compared with the free hand. Note to Practitioners—This paper is inspired by the curve-cutting osteotomy task under the combination of pre-defined virtual fixtures and the kinematic constraint of the reciprocating saw. The motions of current surgical osteotomy robots are mostly generated by either of virtual fixtures and kinematic constraints, which is representing less autonomy on surgery. The introduction of autonomy in surgical robotics can greatly increase the surgeon’s performance in efficiency, accuracy, and safety. The technique of shared control is capable of achieving the semi-autonomy task to significantly improve the accuracy with feedback mechanisms in control science. Thus, the advanced control strategies are required into the process of surgical osteotomy operation. In this article, we proposed a novel methodology to control the hands-on robot executing a curve path. The developed control scheme has the following functionalities: 1) it enables the lateral control for the cutting task for the hands-on robot system. 2) it maintains the pre-defined path-generated virtual fixtures and finds the optimal-parameters of the virtual fixtures. For the convenience of presentation, the mandibular angle split osteotomy is chosen as the background, but in fact this method can be extended to more surgical and even industrial applications with a similar scenario.

The reactor power and the coolant level in the nuclear plant are monitored via the reactor core detectors. Every 4 to 5 years, the detectors with high-level radiation need to be removed, which is time-consuming and hazardous for workers. To address this issue, this paper introduces a novel robotic system and its strategy for the removal of the detectors. The modular mechanisms are designed to achieve diverse actions such as positioning, extracting, transporting, cutting, and coiling. The detector with different radiation doses is physically classified and minimized in volume. The experiments to simulate the removal process are conducted. The results demonstrate that the time for the robotic removal of one detector is reduced from more than 1 hour to 31.2±5.3 min compared with the manual mode. The radiation exposure time for workers is reduced to 0 under normal working conditions, which significantly reduces the radiation dose compared with the traditional methods.