Penetration detection with intention recognition for cooperatively controlled robotic needle insertion

In lumbar puncture surgeries, force and position information throughout the insertion procedure is vital for needle tip localization, because it reflects different tissue properties. Especially in pediatric cases, the changes are always insignificant for surgeons to sense the crucial feeling of loss of resistance. In this study, a robot system is developed to tackle the major clinical difficulties. Four different control algorithms with intention recognition ability are applied on a novel lumbar puncture robot system for better human–robot cooperation. Specific penetration detection based on force and position derivatives captures the feeling of loss of resistance, which is deemed crucial for needle tip location. Kinematic and actuation modeling provides a clear description of the hardware setup. The control algorithm experiment compares the human–robot cooperation performance of proposed algorithms. The experiment also dictates the clear role of designed penetration detection criteria in capturing the penetration, improving the success rate, and ensuring operational safety.

A novel model-based robust control design for collaborative robot joint module

In order to reduce the impact of load and system parameter changes on the dynamic performance of collaborative robot joint module, a novel robust control algorithm is proposed in this paper to solve the problem of dynamic control of collaborative robot joint module trajectory tracking. The controller is composed of two parts: one is a nominal control term designed based on the dynamical model, aiming to stabilize the nominal robot system; the other is a robust control term based on the Lyapunov method, aiming to eliminate the influence of uncertainty on tracking performance, where the uncertainties include nonlinear friction, parameter uncertainty, and external disturbances. The Lyapunov minimax method is adopted to prove that the system is uniformly bounded and uniformly ultimately bounded. We performed numerical simulation and experimental validation based on an actual collaborative robot joint module experimental platform and the rapid controller prototype cSPACE. The numerical simulation and experimental results show that the controller has excellent control performance for the collaborative robot joint module and provides more accurate trajectory tracking under the influence of uncertainties.

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

In view of the low formation redundancy in the traditional rigid formation algorithm and its difficulty in dynamically adapting to the external environment, this study considers the use of the CVT (centroidal Voronoi tessellation) algorithm to control multiple robots to form the desired formation. This method significantly increases the complexity of the multi-robot system, its structural redundancy, and its internal carrying capacity. First, we used the CVT algorithm to complete the Voronoi division of the global map, and then changed the centroid position of the Voronoi cell by adjusting the density function. When the algorithm converged, it could ensure that the position of the generated point was the centroid of each Voronoi cell and control the robot to track the position of the generated point to form the desired formation. The use of traditional formations requires less consideration of the impact of the actual environment on the health of robots, the overall mission performance of the formation, and the future reliability. We propose a health optimization management algorithm based on minor changes to the original framework to minimize the health loss of robots and reduce the impact of environmental restrictions on formation sites, thereby improving the robustness of the formation system. Simulation and robot formation experiments proved that the CVT algorithm could control the robots to quickly generate formations, easily switch formations dynamically, and solve the formation maintenance problem in obstacle scenarios. Furthermore, the health optimization management algorithm could maximize the life of unhealthy robots, making the formation more robust when performing tasks in different scenarios.

Collision response and obstacle avoidance of the tethered-space net robot system with non-target objects

Purpose This paper aims to study the encounter issues of the Tethered-Space Net Robot System (TSNRS) with non-target objects on orbit during the maneuver, including the collision issues with small space debris and the obstacle avoidance from large obstacles. Design/methodology/approach For the collision of TSNRS with small debris, the available collision model of the tethered net and its limitation is discussed, and the collision detection method is improved. Then the dynamic response of TSNRS is studied and a closed-loop controller is designed. For the obstacle avoidance, the variable enveloping circle of the TSNRS has coupled with the artificial potential field (APF) method. In addition, the APF is improved with a local trajectory correction method to avoid the overbending segment of the trajectory. Findings The collision model coupled with the improved collision detection method solves the detection failure and speeds up calculation efficiency by 12 times. Collisions of TSNRS with small debris make the local thread stretch and deforms finally making the net a mess. The boundary of the disturbance is obtained by a series of collision tests, and the designed controller not only achieved the tracking control of the TSNRS but also suppressed the disturbance of the net. Practical implications This paper fills the gap in the research on the collision of the tethered net with small debris and makes the collision model more general and efficient by improving the collision detection method. And the coupled obstacle avoidance method makes the process of obstacle avoidance safer and smoother. Originality/value The work in this paper provides a reference for the on-orbit application of TSNRS in the active space debris removal mission.

An Intravascular Catheter Bending Recognition Method for Interventional Surgical Robots

Robot-assisted interventional surgery can greatly reduce the radiation received by surgeons during the operation, but the lack of force detection and force feedback is still a risk in the operation which may harm the patient. In those robotic surgeries, the traditional force detection methods may have measurement losses and errors caused by mechanical transmission and cannot identify the direction of the force. In this paper, an interventional surgery robot system with a force detection device is designed and a new force detection method based on strain gauges is proposed to detect the force and infer the bending direction of the catheter in the vessel by using BP neural network. In addition, genetic algorithm is used to optimize the BP neural network, and the error between the calculated results and the actual results is reduced by 37%, which improves the accuracy of catheter bending recognition. Combining this new method with traditional force sensors not only reduces the error caused by the traditional mechanical transmission, but also can detect the bending direction of the catheter in the blood vessel, which greatly improves the safety of the operation.

Collaborative spinal robot system for laminectomy: a preliminary study

OBJECTIVE The application of robots in the field of pedicle screw placement has achieved great success. However, decompressive laminectomy, a step that is just as critical as pedicle screw placement, does not have a mature robot-assisted system. To address this lack, the authors designed a collaborative spine robot system to assist with laminectomy. In this study, they aimed to investigate the reliability of this novel collaborative spinal robot system and compare it with manual laminectomy (ML). METHODS Thirty in vitro porcine lumbar vertebral specimens were obtained as experimental bone specimens. Robot-assisted laminectomy (RAL) was performed on the left side of the lamina (n = 30) and ML was performed on the right side (n = 30). The time required for laminectomy on one side, whether the lamina was penetrated, and the remaining thickness of the lamina were compared between the two groups. RESULTS The time required for laminectomy on one side was longer in the RAL group than in the ML group (median 326 seconds [IQR 133 seconds] vs 108.5 seconds [IQR 43 seconds], p < 0.001). In the RAL group, complete lamina penetration occurred twice (6.7%), while in the ML group, it occurred 9 times (30%); the difference was statistically significant (p = 0.045). There was no statistically significant difference in the remaining lamina thickness between the two groups (median 1.035 mm [IQR 0.419 mm] vs 1.084 mm [IQR 0.383 mm], p = 0.842). CONCLUSIONS The results of this study confirm the safety of this novel spinal robot system for laminectomy. However, its efficiency requires further improvement.

Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field

The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.

Playback Robot Programming Framework for Fiber Spraying Processes

Robot-based automation is still not widespread in small and medium-sized enterprises, since programming industrial robots is usually costly and only feasible by experts. This disadvantages can be resolved by using intuitive robot programming approaches like playback programming. At the same time, there are currently not automatized automatized, like fiber spraying. We present a novel approach in programming a robot system for fiber spraying processes, which extends a playback programming framework inspired by video editing concepts. The resulting framework allows the programming of also the periphery devices needed for the fiber spraying process. We evaluated the resulting programming framework to measure the intuitiveness in the use and show that the framework is not only able to program fiber spraying tasks but is also rather intuitive to use for domain experts.