Teleoperation robots remain superior to fully automated robots in complicated and unstructured environments (e.g., in-orbit assembly). However, the collision risk is also greatly increased in these environments. Therefore, the teleoperation robot should possess the capability of collision risk perception and be configured with security assistance strategy to improve safety and efficiency. With this objective in mind, this paper proposes a collision risk assessment system based on fuzzy theory, which comprehensively considers the effects of shortest distance, operation speed, and delay time. The introduction of fuzzy theory makes the risk assessment results more accurate. Furthermore, this paper also proposes a creative discrete expandable bounding box method to make the calculation of the nearest distance between complicated obstacles and the robot more efficient and faster. Secondly, to improve the safety and efficiency of teleoperation, this paper proposes a set of teleoperation assistance strategies for teleoperation robots based on collision risk. The strategies include partial view highlighting, variation in the motion mapping ratio, and haptic risk warning, all of which are achieved in the graphical interactive interface. Finally, this paper verifies the proposed collision risk model and the operation assistant strategy through experiments. The results show that the proposed fuzzy collision risk model has a correct trend with risk factors, and the proposed operation assistance strategies can effectively reduce the robot collision risk and improve the safety of teleoperation robots. In conclusion, this research contributes to the collision risk assessment and obstacle avoidance assistance strategy of teleoperation robots.

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Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System

Compared with rigid robotic arms, soft robotic arms have the advantages of high degree of freedom, lightness, and flexibility. However, insufficient torque and overshoot often cause difficulties in arm control. A prototype of a wire-driven robotic arm is proposed. The design concept of the robotic arm comes from the skeletal joints of snakes, which are formed by a combination of multiple solid polygons and spherical joints. The bending process is similar to the muscle contraction when the snake's body is curled. A look-up table based servo control method is presented to rotate the motor to drive the arm to bend and move to a specific point within the bending range. The motion model of the robot arm is established by the MATLAB Simulink, and the simulation results are compared with the actual operation results to verify the controllability and positioning accuracy of the robotic arm.

Design and Implementation of Wire-Driven Multi-Joint Robotic Arm

Compared with rigid robotic arms, soft robotic arms have the advantages of high degree of freedom, lightness, and flexibility. However, insufficient torque and overshoot often cause difficulties in arm control. A prototype of a wire-driven robotic arm is proposed. The design concept of the robotic arm comes from the skeletal joints of snakes, which are formed by a combination of multiple solid polygons and spherical joints. The bending process is similar to the muscle contraction when the snake's body is curled. A look-up table based servo control method is presented to rotate the motor to drive the arm to bend and move to a specific point within the bending range. The motion model of the robot arm is established by the MATLAB Simulink, and the simulation results are compared with the actual operation results to verify the controllability and positioning accuracy of the robotic arm. 

Collision risk assessment and automatic obstacle avoidance strategy for teleoperation robots

Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.

https://www.mdpi.com/1424-8220/23/9/4496/pdf?version=1683272681

Xiuze Yang

Papers

Collision risk assessment and automatic obstacle avoidance strategy for teleoperation robots

An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery