‘Surgery-by-wire’: A new cross-domain perspective on robotic control

To meet the needs of small and medium-sized processing plants by establishing a software and hardware platform for a robotic arm control system with a hierarchical structure and designing a robot robotic arm motion control system that is adaptable to multiple environments and cost-effective. In this paper, the industrial robot robotic arm motion control system is designed based on the consistent fusion tracking algorithm under the decentralized Internet. While using the VisualC++ development environment for writing human-machine interface programs using the MFC framework, the device controls the robotic arm body to complete predetermined movements or operational tasks based on command information, sensing information, and the robot controller. The AMS1086CD-3.3 low-voltage differential linear regulator chip used in this design effectively implements the circuit design from 4.8V to 3.4V and successfully meets the 3.4V voltage supply required by the controller. The industrial robot robotic arm control system adopts an adaptive backstepping algorithm controller, and the error of the tracking system at 0 seconds in the simulation results is always kept within the range of [-1~1] that meets the requirements, which effectively reduces the control error of the robotic arm position. Therefore, the robotic arm control studied in this paper is composed of a complete control system in hardware and software, and the designed adaptive backstepping algorithm controller achieves the demand for good control performance of the robotic arm proper.

Triboelectric nanogenerators and sensors can be applied as human-machine interfaces to the next generation of intelligent and interactive products, where flexible tactile sensors exhibit great advantages for diversified applications such as robotic control. In this paper, we present a self-powered, flexible, triboelectric sensor (SFTS) patch for finger trajectory sensing and further apply the collected information for robotic control. This innovative sensor consists of flexible and environmentally friendly materials, i. e., starch-based hydrogel, polydimethylsiloxane (PDMS), and silicone rubber. The sensor patch can be divided into a two-dimensional (2D) SFTS for in-plane robotic movement control and a one-dimensional (1D) SFTS for out-of-plane robotic movement control. The 2D-SFTS is designed with a grid structure on top of the sensing surface to track the continuous sliding information on the fingertip, e. g., trajectory, velocity, and acceleration, with four circumjacent starch-based hydrogel PDMS elastomer electrodes. Combining the 2D-SFTS with the 1D-SFTS, three-dimensional (3D) spatial information can be generated and applied to control the 3D motion of a robotic manipulator, and the real-time demonstration is successfully realized. With the facile design and very low-cost materials, the proposed SFTS shows great potential for applications in robotics control, touch screens, and electronic skins.

This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications.

ABSTRACTThe rise of digital technology and Artificial Intelligence (AI) has led to the increased use of smart robots in various sectors. However, security and trust are significant concerns about deploying robots in critical infrastructures. Therefore, a secure and reliable robotic command control system is essential for successful robot integration. None of the prevailing systems focused on attack prediction during cloud‐based robot control and data processing. Hence, this paper proposes a secure model called RCA‐assisted attack detection and robotic command verification using LADA‐C‐RNN and S‐Fuzzy. The robot controller is initially registered using the user ID and password in the cloud application. During login, the SCTDA is used to verify the robot controller's authority. Then, the robot controller's task is subjected to the attack detection phase. In the attack detection phase, the dataset is initially gathered and preprocessed. Thereafter, the temporal pattern analysis is done, followed by feature extraction. Subsequently, the optimal features are selected via GMJFOA. Then, the selected features are inputted to the LADA‐C‐RNN, which performs attack detection. Next, the normal data is fed into the traffic prioritization. Then, the prioritized tasks are inputted to the robot command data verification, thus increasing the security level. Finally, the proposed approach had minimum latency with 98.42% accuracy.

Mitigating collision is a fundamental issue in contact problems, and is required to ensure the safety of a robotic cell. Research into the contact problem between robots and their environment is divided into two parts: one uses the environmental contact model and parameter estimation, the other uses the robot force control method. There are two main problems with this research method. One is that the two research levels are not effectively combined to form a complete solution for force control in practice. The other problem is that research on excessive contact force in the collision phase has not been studied in depth for force control. In this paper, a sensing-executing bionic system is proposed that combines environmental detection and robotic force control based on the way an ant functions. The bionic system clearly explains the process from environment detection to robot control, which can provide guidance when designing a new robot control system. An adaptive switching control algorithm is proposed to mitigate the collision force in the collision phase. From the simulation results, the collision force is significantly reduced due to the implementation of adaptive switching control. Finally, a new self-sensing device is designed which can be integrated into the robot control device. However, as there are no additional sensors or computational complexity in the system, the effectiveness of the circuit and superiority of the adaptive parameter update must be verified by experimentation.

In this paper, we present an object-oriented C++ library for robotic manipulator control and simulation, THRControl, which is inherited from Qt and VTK. The library package has advantages include support cross-platform applications; high flexibility and high computational efficiency meet the need of computations required in robot control such as forward kinematics, inverse dynamics, and jacobian. The experimental results show that it is very easy is construct a user-friendly manipulator control and simulation system.

The muscle-reflex mechanisms of private limbs are studied and modeled so that robotic controls may benefit from the findings. An extensive body of experimental evidence indictates that velocity-dependent force responses of the neuromuscular system have a nonlinear damping effect proportional to a fractional power of velocity. This highly nonlinear viscosity may help limbs adapt to different loads and bring movements to graceful terminations. To explore the characteristics of this nonlinear damping property, a theoretical study using the phase-plane approach is presented. The effects of different loads, damping constants, and stiffnesses are analyzed and simulated. From the results of this phase-plane analysis, a muscle-reflex model is developed and proposed for robotic compliance control. >

We propose a novel way of robotic device control with communicative eye movements that could possibly help to solve the problem of false activations during the gaze control, known as the Midas touch problem. The proposed approach can be considered as explicitly based on communication between a human operator and a robot. Specifically, we employed gaze patterns that are characteristic for “joint attention” type of communication between two persons. “Joint attention” gaze patterns are automatized and able to convey information about object location even under a high cognitive load. Therefore, we assumed that they may make robot control with gaze more stable. In a study with 28 healthy participants who were naive to this approach most of them easily acquired robot control with “joint attention” gaze patterns. The study did not reveal higher preference for communicative type of control, possibly because the participants did not practice before the tests. We discuss potential benefits of the new approach that can be tested in future studies.

To introduce a novel method of combining robotics and the CO(2) laser micromanipulator to provide excellent precision and performance repeatability designed for surgical applications. Pilot feasibility study. We developed a portable robotic controller that appends to a standard CO(2) laser micromanipulator. The robotic accuracy and laser beam path repeatability were compared to six experienced users of the industry standard micromanipulator performing the same simulated surgical tasks. Helium-neon laser beam video tracking techniques were employed. The robotic controller demonstrated superiority over experienced human manual micromanipulator control in accuracy (laser path within 1 mm of idealized centerline), 97.42% (standard deviation [SD] 2.65%), versus 85.11% (SD 14.51%), P = .018; and laser beam path repeatability (area of laser path divergence on successive trials), 21.42 mm(2) (SD 4.35 mm(2) ) versus 65.84 mm(2) (SD 11.93 mm(2) ), P = .006. Robotic micromanipulator control enhances accuracy and repeatability for specific laser tasks. Computerized control opens opportunity for alternative user interfaces and additional safety features.

The design of the neural net based visual-robotic controller, controlling a tactile "itch-scratch" robotic sensory motor control system is presented. The "itch-scratch" robotic motor control system is described in referenced and linked publications. The design of the visual-robotic system is obtained by adding an obstacle avoiding visual system to the sensory motor control functions of the tactile "itch-scratch" robotic system. The visual-robotic controller is unique in that the coordinate frame in which the robot is operating, determined by the optical visual sensors, is reflected onto a neural network located within the robotic controller. The associated visual and tactile sensory motor control systems within the controller may lead to insight into the biological pathways in the brain for 3D-optical imaging and sensory motor control with feedback from the somatic body sensors.

As part of the cooperation between the University of Souther California (USC) and the Institute of Robotics Research (IRF) of the University of Dortmund experiments regarding the control of robots over long distances by means of virtual reality based man machine interfaces have been successfully carried out. In this paper, the newly developed virtual reality system that is being used for the control of a multi-robot system for space applications as well as for the control and supervision of industrial robotics and automation applications is presented. The general aim of the development was to provide the framework for Projective Virtual Reality which allows users to project their actions in the virtual world into the real world primarily by means of robots but also by other means of automation. The framework is based on a new approach which builds on the task deduction capabilities of a newly developed virtual reality system and a task planning component. The advantage of this new approach is that robots which work at great distances from the control station can be controlled as easily and intuitively as robots that work right next to the control station. Robot control technology now provides the user in the virtual world with a prolonged arm into the physical environment, thus paving the way for a new quality of user-friendly man machine interfaces for automation applications. Lately, this work has been enhanced by a new structure that allows to distribute the virtual reality application over multiple computers. With this new step, it is now possible for multiple users to work together in the same virtual room, although they may physically be thousands of miles apart. They only need an Internet or ISDN connection to share this new experience. Last but not least, the distribution technology has been further developed to not just allow users to cooperate but to be able to run the virtual world on many synchronized PCs so that a panorama projection or even a cave can be run with 10 synchronized PCs instead of high-end workstations, thus cutting down the costs for such a visualization environment drastically and allowing for a new range of applications.

Given the continuous stream of movements that biological systems exhibit in their daily activities, an account for such versatility and creativity has to assume that movement sequences consist of segments, executed either in sequence or with partial or complete overlap. Therefore, a fundamental question that has pervaded research in motor control both in artificial and biological systems revolves around identifying movement primitives (a.k.a. units of actions, basis behaviors, motor schemas, etc.). What are the fundamental building blocks that are strung together, adapted to, and created for ever new behaviors? This paper summarizes results that led to the hypothesis of Dynamic Movement Primitives (DMP). DMPs are units of action that are formalized as stable nonlinear attractor systems. They are useful for autonomous robotics as they are highly flexible in creating complex rhythmic (e.g., locomotion) and discrete (e.g., a tennis swing) behaviors that can quickly be adapted to the inevitable perturbations of a dynamically changing, stochastic environment. Moreover, DMPs provide a formal framework that also lends itself to investigations in computational neuroscience. A recent finding that allows creating DMPs with the help of well-understood statistical learning methods has elevated DMPs from a more heuristic to a principled modeling approach. Theoretical insights, evaluations on a humanoid robot, and behavioral and brain imaging data will serve to outline the framework of DMPs for a general approach to motor control in robotics and biology.

Human-in-the-loop control of dynamics and robotics using augmented reality

Editorial to selected papers from the TC17 Events "International Symposium on Measurements and Control in Robotics" (ISMCR2021) and VRISE2021 - Topical Event on Robotics for Risky Interventions and Environmental Surveillance

IntroductionThe application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.MethodsThis study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.ResultsExperimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.DiscussionThe vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.