Cooperative Control Of Robots Research Articles

Target-Following Control of a Biomimetic Autonomous System Based on Predictive Reinforcement Learning.

Biological fish often swim in a schooling manner, the mechanism of which comes from the fact that these schooling movements can improve the fishes' hydrodynamic efficiency. Inspired by this phenomenon, a target-following control framework for a biomimetic autonomous system is proposed in this paper. Firstly, a following motion model is established based on the mechanism of fish schooling swimming, in which the follower robotic fish keeps a certain distance and orientation from the leader robotic fish. Second, by incorporating a predictive concept into reinforcement learning, a predictive deep deterministic policy gradient-following controller is provided with the normalized state space, action space, reward, and prediction design. It can avoid overshoot to a certain extent. A nonlinear model predictive controller is designed and can be selected for the follower robotic fish, together with the predictive reinforcement learning. Finally, extensive simulations are conducted, including the fix point and dynamic target following for single robotic fish, as well as cooperative following with the leader robotic fish. The obtained results indicate the effectiveness of the proposed methods, providing a valuable sight for the cooperative control of underwater robots to explore the ocean.

Multiagent-Reinforcement-Learning-Based Stable Path Tracking Control for a Bionic Robotic Fish With Reaction Wheel

The path tracking of the robotic fish is a hotspot with its high maneuverability and environmental friendliness. However, the periodic oscillation generated by bionic fish-like propulsion mode may lead to unstable control. To this end, this paper proposes a novel framework involving a newly-designed platform and multi-agent reinforcement learning (MARL) method. Firstly, a bionic robotic fish equipped with a reaction wheel is developed to enhance the stability. Secondly, a MARL-based control framework is proposed for the cooperative control of tail-beating and reaction wheel. Correspondingly, a hierarchical training method including initial training and iterative training is designed to deal with the control coupling and frequency difference between two agents. Finally, extensive simulations and experiments indicate that the developed robotic fish and proposed MARL-based control framework can effectively improve the accuracy and stability of path tracking. Remarkably, the head-shaking is reduced about 40%. It provides a promising reference for the stability optimization and cooperative control of bionic swimming robots featuring oscillatory motions.

Cooperation of robot manipulators with motion constraint by real-time RNN-based finite-time fault-tolerant control

The nonlinear cooperative dynamics of m robots with motion constraint and faulty input saturation is addressed. To the best of our knowledge, the motion constraint for the cooperative control of multiple robots is first discussed as one of the designed concepts such that the proposed cooperative control system is more practical and its performance is also improved. Together with a finite-time concept, a real-time recurrent-neural-network-based finite-time fault-tolerant cooperation constraint control (RT-RNN-FTFTCCC) is designed to tackle the aggregately dynamic uncertainties brought about by motion constraint error, cooperation uncertain dynamics, disturbance torque, faulty input saturation, unsatisfying skew-symmetric matrix condition, and others. Moreover, the stabilized learning law includes two terms to cover all the range of filtering cooperation errors such that learning weights converge even in uncertain circumstances. Finally, the stability analysis of the overall cooperation control system is achieved by Lyapunov stability theory. In comparison to adaptive compensation control, recurrent-neural-network-based control without the motion constraint compensation, and different stabilized learning laws, the cooperative control of bi-robotic arms of 3-link in-plane with new constraint validates the improvement and robustness of the proposed control.

Adaptive control of cooperative robots in the presence of disturbances and uncertainties: A Bernstein–Chlodowsky approach

The function approximation technique (FAT) is a powerful mathematical tool recently utilized to design model‐free controllers for robots. However, some FAT‐based controllers depend on joint velocities, which may not be available in many real‐world applications. This problem is solved in this paper by proposing an output feedback tracking control for cooperative robotic arms using the Bernstein–Chlodowsky polynomial as an uncertainty approximator. In other words, the Bernstein–Chlodowsky approach is adopted to approximate the lumped uncertainties consisting of disturbances and unmodeled dynamics. An adaptive rule is then suggested to update the approximator's coefficients matrix. Moreover, it is assured that controlled system error signals are uniformly ultimately bounded (UUB) utilizing the Lyapunov lemma. Finally, the designed Bernstein–Chlodowsky controller is applied to a cooperative system with two arms handling a load. Besides, the results of applying the designed technique are compared with the outcomes of the Chebyshev neural network (CNN) as a state‐of‐the‐art approximation method. The simulation outcomes indicate the capability of the designed method.

Multimobile Robot Cooperative Localization Using Ultrawideband Sensor and GPU Acceleration

To tackle the poor localization accuracy of multimobile robots caused by non-line-of-sight (NLOS) errors in a complex indoor environment and to meet the real-time requirement, this article proposes a multimobile robot cooperative localization system using ultrawideband (UWB) sensor and GPU hardware acceleration. First, a UWB multinode ranging network is established to obtain the relative distance information between robots and anchors. Then, the line-of-sight (LOS) and NLOS errors in distance information are effectively mitigated by using the proposed UWB ranging error mitigation algorithm based on the Bayesian filter. A cooperative particle filter (PF) localization algorithm based on the Gibbs sampling is designed to estimate the position information of each robot at any time. Finally, in order to improve the real-time performance of the collaborative localization system, a parallel Gibbs collaborative localization algorithm that can be accelerated by GPU is proposed considering the characteristics of GPU hardware and CUDA programming model. The experimental results of three TurtleBot2 mobile robots in real scene show that the proposed multimobile robot cooperative localization system using UWB technology can estimate the position information of each robot robustly and accurately, and the localization accuracy is superior to that of the popular extended Kalman filter (EKF) and PF algorithms. It is shown through further evaluations that the proposed parallel algorithm achieves about 3.2 times acceleration effect in the scenarios of three mobile robots. The speed gain is found more significant with more robots, which substantially improves the real-time performance of the cooperative localization system. In the test with seven mobile robots, the speedup is as high as 11.9, that is, the execution time of the algorithm is only 8.39% of that of the original algorithm. Note to Practitioners—The purpose of this article is to improve the accuracy and real-time indoor multimobile robot cooperative localization, but the method proposed in this article is also applicable to outdoor multimobile robot cooperative localization. The existing methods for indoor cooperative localization of mobile robots usually use Bluetooth, infrared, RFID, and other technologies to establish a wireless sensor network (WSN) and then combine Karman filter or particle filter (PF) to achieve cooperative localization, which is difficult to achieve low-cost and high-precision real-time localization. In this article, a new method of cooperative localization is proposed, which uses ultrawideband (UWB) ranging network with high penetration and high precision to obtain accurate distance information, then weakens NLOS error by the Bayesian filtering to further improve the accuracy of distance information, and, finally, uses a novel cooperative localization approach to realize fast and high-precision indoor multimobile robot localization. In addition, by redesigning the collaborative localization algorithm in parallel, the real-time performance of the algorithm is improved while ensuring high accuracy. The collaborative localization experiment of three mobile robots in the real scene shows that the proposed algorithm can effectively improve the localization accuracy, and the real-time performance of collaborative localization is significantly improved. However, the algorithm still has some limitations when mitigating UWB ranging errors in highly obstructed environments, and the algorithm parallelization framework can be further improved for higher real-time performance. In the future, we will further improve the robustness of cooperative localization and apply this algorithm to cooperative control of multiple mobile robots, such as formation control and cooperative search.

Cooperative localization and control of multiple heterogeneous robots using a string formation

AbstractThis paper is on cooperative localization and control of multiple heterogeneous robots utilizing a string formation. This formation is preferred, since robots can move along a narrow passage utilizing this formation. Dead reckoning localization based on inertial measurement units leads to accumulated localization error. To avoid the error accumulation in dead reckoning localization, this paper introduces the last‐move strategy for multiple heterogeneous robots. In the last‐move strategy, a single robot is selected for maneuvering, and it turns on its bearing‐range sensors for a short amount of time, in order to locate itself. While the selected robot moves, all other robots stop moving and perform as static landmarks for the moving robot. A robot may not maintain its desired course, in the case where environmental disturbance is severe. We thus develop a control strategy for avoiding obstacles while estimating the disturbance direction at a robot's location. To the best of our knowledge, this paper is novel in localization and control of a team of heterogeneous robots, considering the case where environmental disturbance is severe. The proposed localization process is energy‐efficient, thus is suitable for practical applications. The performance of the proposed schemes is demonstrated utilizing MATLAB simulations.

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

A two-layer artificial fish swarm evacuation model based on heterogeneous pheromones is presented in this paper. Firstly, the movements of evacuees are simulated by the behaviors of an artificial fish swarm, including preying, swarming, and following. Then, the positive feedback mechanism of heterogeneous pheromones is introduced to improve evacuation performance. Based on the interaction and communication mechanisms of biological groups of social networks in nature, the perceptual and cooperative model among individuals and between individuals and the environment is established. An optimization scheme based on fish swarms and heterogeneous pheromones is proposed. The simulation and experimental results show that the two-layer evacuation model can optimize the spatial-temporal distribution of people and can finally achieve better evacuation plans. The proposed model and algorithm can provide effective guidance for emergency safety responses and robot cooperative control in intelligent robot systems.

Hierarchical planning in a supervisory control context with compositional abstraction

Hierarchy is a tool that has been applied to improve the scalability of solving planning problems modeled using Supervisory Control Theory. In the work of Hill and Lafortune (2016), the notion of cost equivalence was employed to generate an abstraction of the supervisor that, with additional conditions, guarantees that an optimal plan generated on the abstraction is also optimal when applied to the full supervisor. Their work is able to improve their abstraction by artificially giving transitions zero cost based on the sequentially-dependent ordering of events. Here, we relax the requirement on a specific ordering of the dependent events, while maintaining the optimal relationship between upper and lower levels of the hierarchy. This present paper also extends the authors’ work (Vilela and Hill 2020) where we developed a new notion of equivalence based on cost equivalence and weak bisimulation that we term priced-observation equivalence. This equivalence allows the supervisor abstraction to be generated compositionally. This helps to avoid the explosion of the state space that arises from having to first synthesize the full supervisor before the abstraction can be applied. Here, we also show that models with artificial zero-cost transitions can be created compositionally employing the new relaxed sequential dependence definition. An example cooperative robot control application is used to demonstrate the improvements achieved by the compositional approach to abstraction proposed by this paper.

Three controllers via 2nd-order sliding mode for leader-following formation control of multi-robot systems

The multi-robot formation has always been an attractive field for cooperative control of robots. This paper uses the improved second-order sliding mode method to control the leader-following formation system. The traditional second-order sliding mode control is not perfect because the disturbances and uncertainties of the system may cause the compensation control to overestimate. The adaptive gain super-twisting sliding mode control suppresses the output oscillation and speeds up the system response by improving the gain function and adding a differential link. However, this method is generally applied to single-input single-output systems. This paper attempts to apply this method to a multi-input-multi-output leader-following formation system and improve its switching function and compensation control part. We use the estimators, i.e., the extreme learning machine and the nonlinear disturbance observer. These estimators can observe disturbance changes to replace the adaptive gain function to realise disturbance estimation and compensation control. Then combine the extreme learning machine and the nonlinear disturbance observer with the super-twisting sliding mode control to produce two new sliding mode methods. We use the Lyapunov method to prove the system stability as well. Finally, we illustrate the feasibility of the three control methods.

Establishing the Improved Dynamic Model for the Extracorporeal Ultrasonic Lithotripsy Medical Cooperative Robot

The calculus is one of the common diseases with high incidence. The effective treatment method is extracorporeal ultrasonic lithotripsy. At present, it is low about the intelligent and automatic level of the lithotripter, and it has gradually failed to meet the treatment needs. The extracorporeal ultrasonic lithotripsy medical cooperative robot can solve such problems effectively, and it is equally critical for accurate modeling studies of dynamic models. Based on the previous research and experimental basis, this paper proposes a correction theory to improve the accuracy of the dynamic model for the model error in collaborative robot work. The study first establishes the dynamic model and the solid model of the collaborative robot and then subtracts the value of the dynamic model from the solid model to obtain the modified equation. Finally, the accuracy of the dynamic model is improved by modifying the equation. The experiments show that the kinetic model correction theory is effective and can improve the accuracy of the dynamic model modeling after the correction of the torque equation. The experiments show that the improved dynamic model theory is effective and can improve the modeling accuracy of the dynamic model after the correction of the torque equation. The modified equation has the best correction effect in the 5th degree polynomial and can be used for the extracorporeal ultrasonic lithotripsy medical cooperative robot control.

A Bean Optimization-Based Cooperation Method for Target Searching by Swarm UAVs in Unknown Environments

This paper studies the target searching problem using swarms of unmanned aerial vehicles (UAVs) in unknown environments which information is unknown to the UAVs, other than features they detect through their sensors. Effective decision and control methods are required for UAVs that consider their limitations and characteristics when confronted with target searching problems. A cooperative target searching method is proposed for swarm UAVs based on an improved bean optimization algorithm (BOA) called Robot Bean Optimization Algorithm (RBOA). Compared with conventional BOAs used for optimal computation, RBOA has two main modifications for the cooperative control of swarm robots: 1) it accounts for the free motion space of individual UAVs using a Thiessen polygon; and 2) it adds a free space search mechanism to improve the efficiency of target searching. Based on the above improvements, and by integrating a multi-phase search mechanism and scheduling control strategy, a swarm UAV collaborative search simulation platform is built for experimental purposes. The results obtained from search simulations show that the RBOA can outperform adaptive robotic particle swarm optimization (A-RPSO) in target searches in complex and unknown environments, especially with fewer evolutionary generations and smaller numbers of robots. The RBOA, which is inspired by plant population evolutionary patterns, has fast and effective search capabilities, distributed collaborative interaction, and emergent swarm intelligence. It provides new ideas and support for research into the control of swarm UAVs and swarm robots.

Suturing Support by Human Cooperative Robot Control Using Deep Learning

Considering the widespread use of surgical robots in recent years, computer-assisted surgery is becoming significantly popular. Because the automation of surgical tasks without human intervention remains complex owing to individual patient differences, a human cooperative control is proposed in this study. A system in which an operator manipulates one surgical instrument to insert a suture needle was developed, along with another surgical instrument that automatically pulls out the needle from the operated instrument. In the proposed method, YOLOv3 and a standard convolutional neural network (CNN) are used to estimate the penetration and pull state of the needle. An image-based state estimator classifies the state regardless of the stiffness of the object to which the suture needle is inserted. Furthermore, after the pull state is detected, despite a failure of the needle pulling, the position can be corrected and the automated surgical instrument can approach the needle again. Experiments on human cooperative control demonstrated the effectiveness of state estimation using the proposed method. In addition, the failure of grasping observed in a previous study caused by the needle angle error was reduced.

Human-robot cooperative control based on sEMG for the upper limb exoskeleton robot

An exoskeleton robot is a mechanical structure that integrates with the exterior of the human body to improve the wearer’s muscular power. The key to ensure performances and comfort of the system is human–robot cooperation. This paper proposes a human–robot cooperative control method based on sEMG (surface Electromyography) signals to drive a pneumatic upper limb exoskeleton to act in accordance with the wearer’s motion intentions. The intended movement information of the human is estimated by combining the regression method with the classification method. Based on the joint torque estimation model which is originated from the Hill-type musculoskeletal model, the regression method is used to estimate the joint’s desired torque by merging the sEMG signal with the joint angle. To avoid shaking and keep the robot’s limbs in the static condition, a classification method with the support vector machine is developed to find out the joint state that the human intends to keep. It was then applied to the exoskeleton’s elbow joint flexion and extension movement experiments to verify the controller’s effectiveness. The experimental results demonstrate that the controller can estimate human’s motion intention accurately and is appropriate for the human–robot collaboration.

Target clamping and cooperative motion control of ant robots

Carpenter ants possess the characteristics of division of labor, communication between individuals, cooperation, and the ability to solve problems. Inspired by the carpenter ant, we designed electromagnetically controlled ant millirobots that can move, clamp, and work cooperatively. The robot can receive power wirelessly to actuate its ionic polymer-metal composite gripper. Further, two robots can be controlled to manipulate small components individually or cooperatively. Dual-robot manipulation is found to take 76.7% of the time required for single-robot manipulation. The results show that complicated manipulation can be performed by robots that are multifunctional and flexible by utilizing electromagnetic actuation, intelligent materials, and wireless power transmission.

A cooperative paradigm for task-space control of multilateral nonlinear teleoperation with bounded inputs and time-varying delays

In this article, a novel multilateral teleoperation structure is presented for cooperative control of a redundant remote robot in the task-space. In the structure, each local robot is assigned a real number between zero and one, as its dominance factor. The sum of all the assigned factors is equal to one, and a greater dominance factor implies that its corresponding operator will have more power in controlling the remote robot. The time-varying delays in the communication channels, the actuators saturation, and the nonlinear dynamics are incorporated into the controller development. Using Barbalat’s lemma and a Lyapunov-Krasovskii functional, the asymptotic stability analysis of the closed-loop dynamics are shown, and the performance claims are delivered. Simulation and experimental results are provided to verify the theoretical findings.

Research on the forcefree control of cooperative robots based on dynamic parameters identification

PurposeThis paper aims to propose a forcefree control algorithm that is based on a dynamic model with full torque compensation is proposed to improve the compliance and flexibility of the direct teaching of cooperative robots.Design/methodology/approachDynamic parameters identification is performed first to obtain an accurate dynamic model. The identification process is divided into two steps to reduce the complexity of trajectory simplification, and each step contains two excitation trajectories for higher identification precision. A nonlinear friction model that considers the angular displacement and angular velocity of joints is proposed as a secondary compensation for identification. A torque compensation algorithm that is based on the Hogan impedance model is proposed, and the torque obtained by an impedance equation is regarded as the command torque, which can be adjusted. The compensatory torque, including gravity torque, inertia torque, friction torque and Coriolis torque, is added to the compensation to improve the effect of forcefree control.FindingsThe model improves the total accuracy of the dynamic model by approximately 20% after compensation. Compared with the traditional method, the results prove that the forcefree control algorithm can effectively reduce the drag force approximately 50% for direct teaching and realize a flexible and smooth drag.Practical implicationsThe entire algorithm is verified by the laboratory-developed six degrees-of-freedom cooperative robot, and it can be applied to other robots as well.Originality/valueA full torque compensation is performed after parameters identification, and a more accurate forcefree control is guaranteed. This allows the cooperative robot to be dragged more smoothly without external sensors.

Human–Robot Cooperative Control Based on Virtual Fixture in Robot-Assisted Endoscopic Sinus Surgery

In endoscopic sinus surgery, the robot assists the surgeon in holding the endoscope and acts as the surgeon’s third hand, which helps to reduce the surgeon’s operating burden and improve the quality of the operation. This paper proposes a human–robot cooperative control method based on virtual fixture to realize accurate and safe human–robot interaction in endoscopic sinus surgery. Firstly, through endoscopic trajectory analysis, the endoscopic motion constraint requirements of different surgical stages are obtained, and three typical virtual fixtures suitable for endoscopic sinus surgery are designed and implemented. Based on the typical virtual fixtures, a composite virtual fixture is constructed, and then the overall robot motion constraint model is obtained. Secondly, based on the obtained robot motion constraint model, a human–robot cooperative control method based on virtual fixture is proposed. The method adopts admittance control to realize efficient human–robot interaction between the surgeon and robot during the surgery; the virtual fixture is used to restrain and guide the motion of the robot, thereby ensuring motion safety of the robot. Finally, the proposed method is evaluated through a robot-assisted nasal endoscopy experiment, and the result shows that the proposed method can improve the accuracy and safety of operation during endoscopic sinus surgery.

Dynamic Plume Tracking by Cooperative Robots

This paper presents cooperative control of autonomous mobile robots to monitor and track dynamic pollutant plume propagation in $m$ -dimensional space. The dynamics of the pollutant plume is modeled by an advection-diffusion partial differential equation (PDE), and the plume front is described by a level set with a prespecified threshold value. We solve the problem of cooperative plume tracking using two cooperating robots under formation control, one is assigned as the sensing robot and the other is assigned as the tracking robot, where the sensing robot estimates the gradient and divergence information of the entire field based on its current concentration measurement, and the tracking robot tracks the plume front and patrols on it. Rigorous convergence analysis is provided using the set stability concept. Numerical simulations of pollutant plume tracking in both two- and three-dimensional spaces demonstrate the effectiveness of the proposed control scheme. This paper extends existing literature from static-level curve tracking to dynamic plume front tracking and presents a PDE-observer-based plume front tracking control design. The results are applicable to emerging environmental monitoring tasks by cooperative robots.

Infrared-based Short-Distance FSO Sensor Network System

Energy consumption is one of the major problems in the development of Wireless Sensor Network (WSN). Under certain application conditions, Free Space Optical (FSO) communication can effectively reduce system energy consumption. Combining the need of the short-distance communication in the cooperative control of the mobile robot, this paper focuses on the study for the infrared FSO communication method and devices. Eight infrared-transmitting and receiving modules with the angular spacing 45° were deployed to realize the omnidirectional communication. The systematic measurement shows that the designed communication system can effectively carry out data transmission within a distance of 14 meters when the bit error ratio (BER) is 0%. Through the link budget analysis, the theoretical transmission distance of the design system is about 24 meters, only accounting for 60% of the communication module data manual, and the BER rises rapidly as the distance increases. The analysis of the oscilloscope observation results indicates that due to the attenuation caused by each medium (mainly moisture, dust, etc.) in the propagation path, the BER rapidly increases when the distance increases. Therefore, based on the test results, this system is applicable for constructing a short-distance wireless sensor network to meet the needs of cooperative control of mobile robots, without involving the complicated pointing process.

Multi-robot Cooperation Strategy Based on Wireless Sensor Network

<p class="0abstract"><span lang="EN-US">To realize the design of multi-robot cooperative control based on wireless sensor networks, a large number of local control research experiments are carried out on a specific model of mobile robots. Combined with the kinematic model of mobile robot, the motion characteristics of this kind of mobile robot are grasped. On the basis of realizing the multi-target tracking and positioning of wireless sensor network, a multi mobile robot cooperative control system based on the feedback of wireless sensor network is developed, and the synchronous motion between the master and the robot is maintained through the feedback control of the network. Finally, a cooperative trajectory tracking control algorithm for mobile robot is introduced, and the principle of the controller design is mastered. A multi mobile robot cooperative trajectory tracking LQG controller is designed for mobile robot in wireless sensor network. The simulation results show that the algorithm can reach the cooperative control target.</span></p>