Multi-manipulator System Research Articles

Online task allocation and scheduling in multi-manipulator system considering collision constraints and unknown tasks

Compared to a single robot, multi-robot systems (MRS) offer several advantages in complex multi-task scenarios. The overall efficiency of MRS relies heavily on an efficient task allocation and scheduling process. Multi-robot task allocation (MRTA) is often formulated as a multiple traveling salesman problem, which is NP-hard and typically addressed offline. This paper specifically addresses the online allocation problem in multi-manipulator systems within multi-task scenarios. The tasks are initially pre-allocated to alleviate the computational burden of online allocation. Subsequently, considering collision constraints, we search for the current feasible set of manipulators and employ greedy algorithms to achieve local optima as the online allocation result within this set. Our method can handle the online addition of new, unknown tasks to the task list. Moreover, we demonstrate the feasibility of our approach through simulations and on a realistic platform, where multiple manipulators are tasked with polishing the white body of automobile parts. The results demonstrate that our method is effective and efficient for online allocation and scheduling scenarios.

A Collaboration Scheme for Controlling Multimanipulator System: A Game-Theoretic Perspective

In some task-oriented multimanipulator applications, the system not only needs to complete the main assigned tasks, but also should optimize some subobjectives. In order to tap the redundancy potential of individual manipulators and improve the performance of the system, a hybrid multiobjective optimization solution with robustness is proposed in accordance with the realistic execution requirements of the tasks. The entire control scheme is designed from the perspective of the Nash game and further refined into a problem to determine the Nash equilibrium point. Furthermore, a neural-network-assisted model is established to seek the best response of each manipulator to others. Theoretical analysis provides support for proving the convergence and robustness of the model. Finally, the feasibility of the control design is illustrated by simulation studies of the multimanipulator system.

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning.

Self-collision detection is fundamental to the safe operation of multi-manipulator systems, especially when cooperating in highly dynamic working environments. Existing methods still face the problem that detection efficiency and accuracy cannot be achieved at the same time. In this paper, we introduce artificial intelligence technology into the control system. Based on the Gilbert-Johnson-Keerthi (GJK) algorithm, we generated a dataset and trained a deep neural network (DLNet) to improve the detection efficiency. By combining DLNet and the GJK algorithm, we propose a two-level self-collision detection algorithm (DLGJK algorithm) to solve real-time self-collision detection problems in a dual-manipulator system with fast-continuous and high-precision properties. First, the proposed algorithm uses DLNet to determine whether the current working state of the system has a risk of self-collision; since most of the working states in a system workspace do not have a self-collision risk, DLNet can effectively reduce the number of unnecessary detections and improve the detection efficiency. Then, for the working states with a risk of self-collision, we modeled precise colliders and applied the GJK algorithm for fine self-collision detection, which achieved detection accuracy. The experimental results showed that compared to that with the global use of the GJK algorithm for self-collision detection, the DLGJK algorithm can reduce the time expectation of a single detection in a system workspace by 97.7%. In the path planning of the manipulators, it could effectively reduce the number of unnecessary detections, improve the detection efficiency, and reduce system overhead. The proposed algorithm also has good scalability for a multi-manipulator system that can be split into dual-manipulator systems.

Nonlinear State Estimation and Online Neighbor Selection for Multimanipulator Systems

In this article, we focus on the position synchronization of nonlinear multimanipulator systems. For robot manipulators that are only equipped with joint position measurement devices with measurement noises, a continuous-discrete adaptive unscented Kalman filter (CD-AUKF) is implemented to acquire smoother manipulator position states, and, meanwhile, estimate high-order states (e.g., velocity and acceleration). However, in closed-loop control of networked multimanipulator systems, using estimated states may result in a drastic increase in tracking errors. This shortcoming is addressed by an energy index-based neighbor selection policy (NSP). To maintain a higher tracking performance, the NSP allows each agent to actively select well-performing neighbors to interact with, while the poor-quality neighbor data is discarded. Finally, to regulate the multimanipulator system in the presence of parametric uncertainties, friction, disturbances, time-varying network delays, and packet loss, an adaptive nonsingular terminal sliding-mode (ANTSM) controller is designed. A group of Phantom Omni robotic devices were used to carry out numerical simulations and experimental studies that demonstrate the effectiveness of the proposed ANTSM control method, CD-AUKF estimation, and active neighbor-selection policy.

Adaptive Finite-Time Consensus Tracking Control for Coopetition Flexible Joint Multi-Manipulators With Full-State Constraints

In this paper, the adaptive full-state constraints bipartite consensus tracking problem for coopetition flexible joint multi-manipulator systems with finite-time convergence is investigated. Based on the finite-time convergent method, the command filtered backstepping technique is introduced to ensure the excellent convergence performance without the influence of the repeated differential problem which is called explosion of complexity, and the error compensation mechanism is proposed to reduce the error caused by the filtering progress. Furthermore, by using the barrier Lyapunov functions, the state of the system and error compensation signals are proved to be constrained in their respective expected ranges. It is worth mentioning that the flexible joint multi manipulator system is in the mode of coopetition. Finally, the practicability of the proposed algorithm is exhibited in the simulation.

A calibration-based method for interval reliability analysis of the multi-manipulator system

The multiple manipulators can construct a special multi-agent system with the distinction that the type can be serial or parallel according to their cooperative way. We proposed a comprehensive method to handle the problem of reliability estimation. The wide and narrow bound method are applied to calculate the interval reliability respectively when multiple manipulators work as the series system. Aims to decrease the system complexity and enhance the dynamic adjustment capability, the base frame calibration technique is presented to convert the series system to a parallel one, naturally the reliability can be improved significantly. A system composed by three manipulators is utilized as an example to illustrate the feasibility of the proposed method.

Adaptive Coordinated Control Strategy of Multi Manipulator System based on Multi-Agent

With people's increasing awareness of life and the increasing complexity of exploration in unknown environment, a single robot can not meet the increasing demand, including the price, flexibility and efficiency of robots. As a common mechanical control system in industrial production instead of human production, multi manipulator system can be applied in complex environment, multi task and other conditions. In order to settle the coordinated control fault of multi manipulator system, we study adaptive coordinated control strategy with the help of multi-agent research method in this paper, which can simplify the complexity of the problem and design an efficient and feasible system control protocol. The complex items in the multi manipulator system are treated as non affine systems. Using the design idea of non affine algorithm, combined with implicit function theorem and median theorem, the non affine system is transformed into affine systems, the controller is separated, and a distributed adaptive control strategy is designed. The results indicate that manipulator systems can effectively track the active manipulator system in finite time and the significance of the algorithm is proven by MATLAB simulation analysis.

Distributed fault-tolerant consensus tracking control for multiple Lagrangian systems with preset error bound constraints

Actuator faults often occur in physical systems, which seriously affect the transient performance and control accuracy of the system. For the finite-time consensus tracking problem of multiple Lagrangian systems with actuator faults and preset error constraints, a novel distributed fault-tolerant controller is proposed in this paper. The proposed controller is developed based on the barrier Lyapunov function method and the adding a power integrator technique, which can not only guarantee the steady-state performance of the system but also its transient performance. Due to its strong sensitivity to the variation of system errors, the proposed controller can quickly eliminate the system initial errors and the error perturbations caused by actuator faults. That is, the controller can guarantee that the consensus error converges to zero in a finite time and is always constrained within the preset error bound. Finally, the effectiveness of the developed controller is verified by simulation of a multi-manipulator system.

Distributed Cooperative Kinematic Control of Multiple Robotic Manipulators With an Improved Communication Efficiency

An efficiency-oriented solution is theoretically a preferred choice to support the efficient operation of a system. Although some studies on the multimanipulator system share the load of the control center by transforming the network topology, the whole system often suffers an increased communication burden. In this article, a multimanipulator cooperative control scheme with an improved communication efficiency is proposed to allocate limited communication resources reasonably. The entire control process is formulated from the perspective of the game theory, and finally, evolved into a problem of finding a Nash equilibrium with time-varying parameters. Then, a neural network solver is designed to update the strategies of manipulators. Theoretical analysis supports the convergence and robustness of the solver. In addition, Zeno behavior does not occur under the domination of the control strategy. Finally, simulative results reveal that the proposed control strategy has advantages over the traditional periodic control in communication.

Human Multi-Robot Physical Interaction: a Distributed Framework

The objective of this paper is to devise a general framework to allow a human operator to physically interact with an object manipulated by a multi-manipulator system in a distributed setting. A two layer solution is devised. In detail, at the top layer an arbitrary virtual dynamics is considered for the object with the virtual input chosen as the solution of an optimal Linear Quadratic Tracking (LQT) problem. In this formulation, both the human and robots’ intentions are taken into account, being the former online estimated by Recursive Least Squares (RLS) technique. The output of this layer is a desired trajectory of the object which is the input of the bottom layer and from which desired trajectories for the robot end effectors are computed based on the closed-chain constraints. Each robot, then, implements a time-varying gain adaptive control law so as to take into account model uncertainty and internal wrenches that inevitably raise due to synchronization errors and dynamic and kinematic uncertainties. Remarkably, the overall solution is devised in a distributed setting by resorting to a leader-follower approach and distributed observers. Simulations with three 6-DOFs serial chain manipulators mounted on mobile platforms corroborate the theoretical findings.

TAMS: development of a multipurpose three-arm aerial manipulator system

This paper describes the concept, design and realization of a novel manipulator system for a multirotor aerial robot. The proposed manipulator system consists of three robotic arms attached to a multirotor frame. To overcome the limited payload capabilities in multirotor aerial platforms, the same set of on-board manipulators are used to realize various tasks, primarily the manipulation or grasping of objects of various sizes. Furthermore, they can provide aid in complex navigation tasks by doing physical, contact-based obstacle avoidance and are able to act as adaptive landing gear in uneven terrain. The design requirements and the analysis to realize these tasks is discussed. The proposal is supported with the successful demonstration of these tasks using the aerial multi manipulator system in an outdoor environment.

Vision-based neural predictive tracking control for multi-manipulator systems with parametric uncertainty

To deal with the coordination problem for multi-manipulator trajectory tracking systems with parametric uncertainties, this paper proposes a two-layer control scheme incorporating a model predictive strategy and an extended state observer. In the kinematic layer, the visual information is implemented and a visual servoing error model is derived by the image-based visual servoing strategy. A recurrent neural network model predictive control approach is proposed to obtain velocities which are regarded as the reference signals for the dynamic layer. For dynamics, a linear time-varying dynamic model of the multi-manipulator system coupled with the object is established, where the parametric uncertainty is recognized as an added disturbance. An extended state observer is sequentially designed to estimate the disturbance by using pole placement method. The input-to-state practical stability of the system is further analyzed with a bounded disturbance. Finally, simulations and comparison are given to verify the effectiveness and robustness of the proposed algorithm.

Trajectory coordination for a cooperative multi-manipulator system and dynamic simulation error analysis

To achieve temporal and spatial correspondence between multiple robotic manipulators, the system must correctly analyze the coordinated path required for a specific task. Based on manipulator kinematics analysis, we first studied the kinematic constraints between the end-effectors of cooperative manipulators, and deduced the multi-manipulator cooperative kinematics constraint equations in the Cartesian coordinate system space under different motion modes. Then, we created two MD-6 manipulator models to simulate the trajectory simulation of the synchronous and relative motion of the manipulator. This allowed us to verify the correctness of the proposed trajectory coordination method, and analyze the influence of external loads on the position and posture of the end-effector of the manipulator, to effectively predict the cooperative motion error of the manipulator system. Finally, in order to verify the effectiveness of the proposed trajectory coordination method, we established a robotic experimental platform and conducted experimental research. The results show that the multi-manipulator trajectory coordination method studied in this paper can make multi-manipulators effectively achieve the target requirements of tasks such as time and space cooperative handling and circular drawing operations.

Model-based force/position control of cooperative manipulation systems

ABSTRACTThis paper presents a centralized force/position controller for a heavy object manipulation in a multi-manipulator cooperative system. System dynamics of cooperative manipulating tasks comes from complex interaction of the object with robot manipulators and the environment. In this paper, focussing on the interaction effects in the system as well as by noting the role of imposed kinematics and force constraints, manipulator coordination and minimizing internal forces a pre-designed impedance behaviour between manipulator end effectors and the object is developed. The stability of the feedback system is then presented through passivity theorem and simulation results are finally provided with three manipulators handling the object supporting the relevance of the theoretical results.

Fault-tolerant Synchronization Control for Markovian Switching Complex Dynamical Networks with Stochastic Perturbations and Nonlinearly Time-varying Delayed Interactions

In this study, the fault-tolerant exponential synchronization control is researched for the multi-manipulator system which is characterized by the large number of interconnected manipulators, the stochastic perturbations and the nonlinearly time-varying delayed interactions. After abstracting the corresponding mathematical complex dynamical network model from the system, an effective controller is designed with some definitions, assumptions and lemmas. Particularly, based on the linear matrix inequalities (LMIs), Ito’s formula and the Lyapunov stability theory, the controlled network model is proved to be stochastically synchronous under the actuator failure by constructing a switching stochastic Lyapunov-Krasovskii functional and some sufficient conditions for the mean square exponential synchronization are derived. A numerical example is illustrated for the further verifications and conclusions.

Neural Network-Based Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multiagent State-Delay Systems.

Compared with the existing neural network (NN) or fuzzy logic system (FLS) based adaptive consensus methods, the proposed approach can greatly alleviate the computation burden because it needs only to update a few adaptive parameters online. In the multiagent agreement control, the system uncertainties derive from the unknown nonlinear dynamics are counteracted by employing the adaptive NNs; the state delays are compensated by designing a Lyapunov-Krasovskii functional. Finally, based on Lyapunov stability theory, it is demonstrated that the proposed consensus scheme can steer a multiagent system synchronizing to the predefined reference signals. Two simulation examples, a numerical multiagent system and a practical multimanipulator system, are carried out to further verify and testify the effectiveness of the proposed agreement approach.

Collaborative Simulation and Experimentation on the Dental Arch Generator of a Multi-manipulator Tooth-arrangement Robot

In the orthodontic treatment and manufacture of complete dentures, the most important steps are designing and generating a dental arch curve which adapts to the requirements of the patient according to their jaw arch form. The traditional way of acquiring the dental arch curve form is based on manual operation, which will randomly generate a lot of errors caused by human factors. The purpose of this paper is to automatically acquire the dental arch curve and implement the coordinated control of the dental arch generator of the multi-manipulator tooth-arrangement robot, which can be used in full denture manufacturing. According to the work principle, motion planning method of the dental arch generator will be analysed. A collaborative simulation of the dental arch generator is realized based on Matlab and ADAMS. Controlled experimentation of the dental arch generator and preliminary tooth-arrangement experimentation are performed using the multi-manipulator tooth-arrangement robot system in order to verify the feasibility of the motion planning method and the technical route. It will lay an important theoretical foundation for quantitative research on oral restoration and also provide a way to standardize the manufacturing process of full dentures and orthodontic treatments.

Dynamic Analysis and Control Synthesis of Grasping and Slippage of an Object Manipulated by a Robot

Grasping an object by a cooperating system such as multi-fingered hands and multi-manipulator robotic system has received much attention. Research has focused on analysis of force-closure grasps and the synthesis of optimal grasping, when there is no slipping condition. Although the control system is designed to keep the contact force in the friction cone and avoid the slipping condition, slippage can occur for many reasons. In this research, dynamics analysis and control synthesis of a manipulator moving an object on a horizontal surface using the contact force of an end-effector are performed considering the slipping condition. Equality and inequality equations of frictional contact conditions are replaced by a single second-order differential equation with switching coefficients in order to facilitate the dynamic modeling. Accuracy of this modeling is verified by comparing the results of the model with those of SimMech. Using this modeling of friction, a set of reduced order form is obtained for equations of motion of the system. A new method is proposed to control the object motion and the end-effector undesired slippage based on the reduced form. Finally, performance of the method is evaluated both numerically and experimentally.

Comparative analysis of collision-free path-planning methods for multi-manipulator systems

Motion planning for manipulators with many degrees of freedom is a complex task. The research in this area has been mostly restricted to static environments. This paper presents a comparative analysis of three reactive on-line path-planning methods for manipulators: the elastic-strip, strategy-based and potential field methods. Both the elastic-strip method [O. Brock and O. Khatib, “Elastic strips: A framework for integrated planning and execution,” Int. Symp. Exp. Robot. 245–254 (1999)] and the potential field method [O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res.5(1), 90–98 (1986)] have been adapted by the authors to the problem at hand related to our multi-manipulator system (MMS) (three manipulators with five degrees of freedom each). Strategy-based method is an original contribution by the authors [M. Mediavilla, J. L. González, J. C. Fraile and J. R. Perán, “Reactive approach to on-line path planning for robot manipulators in dynamic environments,” Robotica20, 375–384 (2002); M. Mediavilla, J. C. Fraile, T. González and I. J. Galindo, “Selection of strategies for collision-free motion in multi-manipulator systems,” J. Intell. Robot Syst38, 85–104 (2003)].The three methods facilitate on-line path planning for our MMS in dynamic environments with collision avoidance, where the three manipulators may move at the same time in their common workspace. We have defined some ‘basic motion problems’ for the MMS, and a series of simulations has been running that will tell us how effective each path-planning method is. The simulations have been performed and the obtained results have been analysed by using a software program developed by the authors.The paper also presents experimental results obtained applying the path-planning methods to our MMS, that perform pick-and-place tasks sharing common working areas.

Independent force and position control for cooperating manipulators

The problem of independent force and position control of multitask cooperating manipulators is studied. Each manipulator is in a non-singular configuration and the object can be driven by the contact forces exerted by the end effectors of the manipulators. The design requirement is to control each coordinate of the position of the object as well as each component of the “rest” forces, except the moving forces that are determined uniquely from the object's position. The present design requirement has been proved to be always satisfied together with asymptotic and BIBO stability. A class of controllers solving the above simultaneous problem is analytically determined. The proposed state feedback controller does not depend upon the specific object dynamics and thus the resulting closed-loop multimanipulator system performs as required, not only when handling various objects but also when the dynamics of the objects are unknown or hard to compute or disturbed by external forces.