Multiple Mobile Manipulators Research Articles

Hybrid Orientation and Position Collaborative Motion Generation Scheme for a Multiple Mobile Redundant Manipulator System Synthesized by a Recurrent Neural Network.

To enable distributed multiple mobile manipulator systems to complete collaborative tasks safely and stably, this article investigates and presents a motion generation scheme that considers both orientation and position coordination based on a distributed recurrent neural network. Moreover, physical limits are also considered. Specifically, the orientation and position coordination constraints and physical limits are modeled separately as equality and inequality constraints with coupled variables. Subsequently, a motion generation scheme for multiple mobile manipulators based on quadratic programming is established. Finally, a distributed linear variational inequality-based primal-dual neural network is constructed to solve the motion generation scheme and obtain the motion trajectories of all the mobile manipulators. The simulation results demonstrate that the hybrid orientation and position collaboration motion generation scheme effectively addresses the position and orientation coordination problem for multiple mobile manipulator systems. Compared to other schemes, the proposed scheme based on a distributed computing structure greatly enhances the stability of the system. Additionally, the proposed approach introduces orientation coordination and physical limits, which increases the practicality of the system.

Multiple Self-Powered Sensor-Integrated Mobile Manipulator for Intelligent Environment Detection.

A multiple self-powered sensor-integrated mobile manipulator (MSIMM) system was proposed to address challenges in existing exploration devices, such as the need for a constant energy supply, limited variety of sensed information, and difficult human-computer interfaces. The MSIMM system integrates triboelectric nanogenerator (TENG)-based self-powered sensors, a bionic manipulator, and wireless gesture control, enhancing sensor data usability through machine learning. Specifically, the system includes a tracked vehicle platform carrying the manipulator and electronics, including a storage battery and a microcontroller unit (MCU). An integrated sensor glove and terminal application (APP) enable intuitive manipulator control, improving human-computer interaction. The system responds to and analyzes various environmental stimuli, including the droplet and fall height, temperature, pressure, material type, angles, angular velocity direction, and acceleration amplitude and direction. The manipulator, fabricated using 3D printing technology, integrates multiple sensors that generate electrical signals through the triboelectric effect of mechanical motion. These signals are classified using convolutional neural networks for accurate environmental monitoring. Our database shows signal recognition and classification accuracy exceeding 94%, with specific accuracies of 100% for pressure sensors, 99.55% for angle sensors, and 98.66, 95.91, 96.27, and 94.13% for material, droplet, temperature, and acceleration sensors, respectively.

Bimanual Telemanipulation Framework Utilising Multiple Optically Localised Cooperative Mobile Manipulators

Bimanual manipulation is valuable for its potential to provide robots in the field with increased capabilities when interacting with environments, as well as broadening the number of possible manipulation actions available. However, for a robot to perform bimanual manipulation, the system must have a capable control framework to localise and generate trajectories and commands for each sub-system to allow for successful cooperative manipulation as well as sufficient control over each individual sub-system. The proposed method suggests using multiple mobile manipulator platforms coupled through the use of an optical tracking localisation method to act as a single bimanual manipulation system. The framework’s performance relies on the accuracy of the localisation. As commands are primarily high-level, it is possible to use any number and combination of mobile manipulators and fixed manipulators within this framework. We demonstrate the functionality of this system through tests in a Pybullet simulation environment using two different omnidirectional mobile manipulators, as well a real-life experiment using two quadrupedal manipulators.

Multiple mobile robots motion control based on screw theory for aircraft panel assembly

As a crucial component of large aircraft, the assembly efficiency of aircraft skins directly impacts production efficiency. To achieve efficient manufacturing of aircraft skins, this paper proposes a multiple mobile robot control algorithm based on screw theory. The robot arm is integrated into a rail or AGV to increase its motion space, creating a mobile robot assembly system. To address the redundant degrees of freedom problem caused by the mobile manipulator, this paper adopts the screw theory to describe the motion of multiple robots. Furthermore, to ensure the constraint of the motion between multiple robots, this paper proposes a multi-robot control method based on screw constraint. Rigid body constraints are assigned to the end of each mobile manipulator, and the motion is decomposed to the mobile platform and the robot arm. Finally, the cooperative motion control of multiple mobile manipulators is realized. The proposed algorithm is applied in the multi-mobile manipulator cooperative aircraft panel assembly task, achieving efficient assembly of aircraft panel and long truss.

Force-reflecting event-triggered predefined-time cooperative control for teleoperation of NMMs via data-driven based observer

This paper investigates the cooperative control problem for the teleoperation system of networked mobile manipulators (NMMs), in which each mobile manipulator is subject to nonholonomic constraints, model uncertainty and external disturbances. The control objective of such a teleoperation system is to synchronize the task-space position between the human-controlled master manipulator and the slave mobile manipulators, and meanwhile enable the slave mobile manipulators to achieve a formation. For this purpose, a hierarchical predefined-time cooperative control (HPTCC) framework involving predefined-time distributed event-triggered (PTDET) estimator, predefined-time stability (PTS) controller and data-driven based (DDB) observer, is proposed. The PTDET estimator is used to generate the information that the slave mobile manipulators need to track and enable multiple slave mobile manipulators to interact through an event-triggered communication network. The PTS controller is presented to ensure the predefined-time stability of the master manipulator and the slave ones. Moreover, for improving the telepresence of the considered teleoperation system, the DDB observer is proposed to reestablish the interaction force between the slave mobile manipulators and the complex operating environment (i.e., the environmental force) on the human operator side. Based on the hierarchical control analysis as well as Lyapunov theory, several sufficient conditions are derived to guarantee the predefined-time stability for the teleoperation system of NMMs. Finally, several simulations are conducted to demonstrate the effectiveness of the proposed control scheme.

Distributed Neural-Network-Based Cooperation Control for Teleoperation of Multiple Mobile Manipulators Under Round-Robin Protocol.

This article addresses the distributed cooperative control design for a class of sampled-data teleoperation systems with multiple slave mobile manipulators grasping an object in the presence of communication bandwidth limitation and time delays. Discrete-time information transmission with time-varying delays is assumed, and the Round-Robin (RR) scheduling protocol is used to regulate the data transmission from the multiple slaves to the master. The control task is to guarantee the task-space position synchronization between the master and the grasped object with the mobile bases in a fixed formation. A fully distributed control strategy including neural-network-based task-space synchronization controllers and neural-network-based null-space formation controllers is proposed, where the radial basis function (RBF) neural networks with adaptive estimation of approximation errors are used to compensate the dynamical uncertainties. The stability and the synchronization/formation features of the single-master-multiple-slaves (SMMS) teleoperation system are analyzed, and the relationship among the control parameters, the upper bound of the time delays, and the maximum allowable sampling interval is established. Experiments are implemented to validate the effectiveness of the proposed control algorithm.

A Behavior-based Adaptive Dynamic Programming Method for Multiple Mobile Manipulators Coordination Control

A Behavior-based Adaptive Dynamic Programming Method for Multiple Mobile Manipulators Coordination Control

Control of coordinated multiple mobile manipulators with neural network-based fast terminal sliding mode control

Control of coordinated multiple mobile manipulators with neural network-based fast terminal sliding mode control

Hierarchical finite-time cooperative control for teleoperation of networked disturbed mobile manipulators

This paper investigates the teleoperation problem of networked disturbed mobile manipulators (NDMMs), in which the human operator remotely controls multiple slave mobile manipulators through a master manipulator. Each individual of the slave ones consisted of a nonholonomic mobile platform and a holonomic constrained manipulator that is mounted on the nonholonomic mobile platform. The cooperative control objective of the considered teleoperation problem includes: (1) synchronizing the states of the slave manipulators to the human-controlled master one; (2) forcing the mobile platforms of the slave ones to form a user-defined formation; (3) controlling the geometric center of all the platforms to track a reference trajectory. We present a hierarchical finite-time cooperative control (HFTCC) framework to achieve the cooperative control goal in a finite time. The presented framework includes the distributed estimator, the weight regulator and the adaptive local controller, where the estimator generates the estimated states of the desired formation and trajectory, the regulator selects the slave robot that the master one needs to track, as well as the presented adaptive local controller guarantees the finite-time convergence of the controlled states with model uncertainties and disturbances. Additionally, for improving the telepresence, a novel super twisting observer is presented to reconstruct the interaction force between the salve mobile manipulators and the remote operating environment on the master (i.e., the human) side. Finally, the effectiveness of the proposed control framework is demonstrated by several simulation results.

Reinforcement learning compensated coordination control of multiple mobile manipulators for tight cooperation

This study presents a coordinated control method based on reinforcement learning for multiple mobile manipulators when strong constraints and close coupling are involved in the tightly cooperative tasks. The reinforcement learning strategy is specifically designed to deal with the unknown vibrations between the mobile manipulators and the common object. Firstly, the problem is converted into a Markov decision process. Next, the grasping forces of the end-effectors are regarded as the parameters to be optimized, and the system states and learning framework are described based on advantage actor–critic algorithm. Thirdly, an agent is trained through interacting with the environment based on a proposed reward policy. To eliminate joint dynamic errors caused by trajectories tracking, an adaptive controller is designed for each mobile manipulator. For the simulations and experiments, two mobile manipulators are employed for transporting a common plate under various conditions. The results demonstrate that the proposed method has better control effects than well-known controllers. This study combines the advantages of both reinforcement learning and model-based method via a coordinated controller designed with the characteristics of tight cooperation.

Modeling and Coordinated Control of Multiple Mobile Manipulators with Closed-chain Constraints

Modeling and Coordinated Control of Multiple Mobile Manipulators with Closed-chain Constraints

An optimal control approach for hybrid motion/force control of coordinated multiple nonholonomic mobile manipulators using neural network

An optimal control approach for hybrid motion/force control of coordinated multiple nonholonomic mobile manipulators using neural network

An optimal control approach for hybrid motion/force control of coordinated multiple nonholonomic mobile manipulators using neural network

An optimal control approach for hybrid motion/force control of coordinated multiple nonholonomic mobile manipulators using neural network

Human Multi-Robot Safe Interaction: A Trajectory Scaling Approach Based on Safety Assessment

Nowadays, industrial robotics requires that robots and humans share the same workspace and collaborate to a certain extent. In such a scenario, the safety is the minimum requirement and, for this reason, many off-the-shelf collaborative robots are now available on the market which, basically, are able to limit the contact forces in the case of impact. Differently from many different works, this article presents a solution to human multi-robot safe interaction in which multiple mobile manipulators are in charge of performing a cooperative task in a workspace shared with human operators. The safety of the interaction is assessed by a safety field that considers the whole system and is general enough concerning its expression. Based on the value of this field, the cooperative task trajectory is properly modified so as to ensure a safe interaction while trying to preserve as much as possible the nominal task, which is instead completely aborted whenever the safety of the interaction cannot be guaranteed. The solution is first designed within a centralized architecture and, then, upon this, a distributed implementation is presented, which, in general, aims to exhibit the same performance as the centralized counterpart. Finally, both simulations and experiments on real industrial robots corroborate the designed solution.

Robust control design for teleoperation of multiple mobile manipulators under time delays

SummaryThis article addresses a novel multilateral teleoperation control scheme for single‐master multiple slave systems, which can be extended to n masters and n slaves without the loss of generality, where the master is a m degrees of freedom (DOF) manipulator arm and the slaves are m DOF mobile manipulators. The human operator operates the master robot to remotely control the slaves handling a target object. The master position signal is transmitted to the slave side to generate a desired object trajectory as well as the reference mobile base velocity. An adaptive robust controller is designed for the slaves to follow the desired trajectory from the master, which not only provides the excellent trajectory tracking but also optimize the internal force distribution of the object. A null space controller is designed for the mobile platforms of the mobile manipulators to achieve the velocity consensus while achieving the main task of object transportation. The novel control design uses the transmission of the environmental force feedback over the communication channel by the estimated parameters of the environment, which helps retain the stability of the overall system. The environmental force is predicted on the master side based on the estimated environmental parameters. The proposed control design can simultaneously achieve the objectives of stability, synchronization, and optimal internal force distribution. The simulation results of a single‐master and three slaves teleoperation system validate the efficacy of the proposed control algorithm.

Robust Anti-Disturbance Coordinated Control for Multiple Manipulators

In this paper, the anti-disturbance coordinated control is investigated for the multiple mobile manipulators with disturbances. Some disturbances are induced by the random white noise; the others are assumed to be described by an external system. These disturbances are rejected and attenuated based on the disturbance-observer-based-control (DOBC) method and the adaptive control approach. Subsequently, a robust adaptive antidisturbance coordinated control strategy is proposed for the multiple mobile manipulators based on the back-stepping control scheme. In accordance with Lyapunov stability theory and the stochastic control theory foundation, the tracking error systems are guaranteed to be exponentially practically stable in mean square. Finally, an example is used to verify the effectiveness of the presented control method.

Motion/force control scheme for electrically driven cooperative multiple mobile manipulators

This paper presents the motion and force control problem of rigid-link electrically driven cooperative mobile manipulators handling a rigid object. Although, the motion/force control problem of cooperative mobile manipulators has been enthusiastically studied. But there is little research on the motion/force control of electrically driven cooperative mobile manipulators. Due to the inclusion of the actuator dynamics with the manipulator’s dynamics, the controller exhibits some important characteristics. For the electromechanical system, we have designed a novel controller at the dynamic level as well as at the actuator level. In the proposed control scheme, at the dynamic level, uncertain non-linear mechanical dynamics is approximated with a hybrid controller containing model-based control scheme combined with model-free neural network based control scheme together with an adaptive bound. The adaptive bound is used to suppress the effects of external disturbances, friction terms, and reconstruction error of the neural network. At the actuator level, for the approximation of the unknown electrical dynamics, the model-free neural network is utilized. The developed control scheme provides that the position tracking errors, as well as the internal force, converge to the desired levels. Additionally, direct current motors are also controlled in such a way that the desired currents and torques can be attained. In order to make the overall system to be asymptotically stable, online learning of the weights and the parameter adaptation of the parameters is utilized in the Lyapunov function. The superiority of the developed control method is carried out with the numerical simulation results and its superior robustness is shown in a comparative manner.

Multimodal Human-Robot Interface for Accessible Remote Robotic Interventions in Hazardous Environments

Human-Robot Interfaces have a key role in the design of secure and efficient robotic systems. Great effort has been put during the past decades on the design of advanced interfaces for domestic and industrial robots. However, robots for intervention in unplanned and hazardous scenarios still need further research, especially when the mission requires the use of multiple robotic systems, to obtain an acceptable level of usability and safety. This paper describes the design and the software engineering process behind the development of a modular and multimodal Human-Robot Interface for intervention with a cooperative team of robots, as well as its validation and commissioning, as it is being used in real operations at CERN's accelerators complex. The proposed Human-Robot Interface allows the control of a heterogeneous set of robots homogeneously, providing the operator, among other features, with live scripting functionalities which can be programmed and adapted in run-time, for example, to increase operator's multi-tasking in a multi-agent scenario. The operator is given the capability to enter in the control loop between the HRI and the robot and customize the control commands according to the operation. To provide such functionalities, well-defined software development approaches have been adopted, for guaranteeing the modularity and the safety of the system during its continuous development. The paper describes the modules offered by the HRI, such as the multimodality, multi-robot control, safety, operators training, and communications architecture, among others. The HRI and the CERN Robotic Framework where it belongs are designed in a modular manner, in order to be able to adapt both, software and hardware architecture in a short time, to the next planned mission. Results present the experience gained with the system, demonstrating a high level of usability, learnability and safety when operated by both, non-experts and qualified robotic operators. The multimodal user interface has demonstrated to be very accurate and secure, providing a unique system to control, in a teleoperated or supervised manner, both single and multiple heterogeneous mobile manipulators. At the moment of writing, the user interface has been successfully used in 100 real interventions in radioactive industrial environments. The presented HRI is a novel research contribution in terms of multimodality, adaptability and modularity for mobile manipulator robotic teams in radioactive environments, especially for its software architecture, as part of the CERN Robotic Framework.

Cooperative transportation control of multiple mobile manipulators through distributed optimization

This paper investigates the problem of distributed control of multiple redundant mobile manipulators to collectively transport an object tracking a desired trajectory with energy and manipulability optimized.To solve this optimization problem, formation control tasks are introduced as equality constraints with the variables being the velocities. In this paper, we propose a distributed proximal gradient algorithm searching for the optimal solution, with which the stability of the closed-loop system is proved. Simulations demonstrate the effectiveness of the proposed distributed optimization scheme and proximal algorithm.

A New Hybrid Position/Force Control Scheme for Coordinated Multiple Mobile Manipulators

In this paper, a new hybrid position/force control scheme is proposed for coordinated multiple mobile manipulators holding a rigid object. The problem of the controller design for multiple mobile manipulators is much complicated as compared to single mobile manipulator. Many of the position/force control schemes for coordinated multiple mobile manipulators assume exact knowledge of the dynamical model. But the dynamic model of the coordinated multiple mobile manipulators is highly uncertain and faces external disturbances, uncertain environment intervention, etc. Therefore, model-based controller is inadequate to deal with such uncertain systems. In the proposed scheme, the inefficiency of the model-based controller is recovered by combining with RBF neural network-based mode-free controller along with a compensation controller. RBF neural network is utilized to estimate the unmodeled dynamics of the system without requiring the offline learning. The compensation controller is utilized to neutralize the effects of the friction terms, external disturbances, and the network reconstruction error. The online adaptation of the weights and the parameter updates are utilized in the Lyapunov function to make the system to be stable. Furthermore, the proposed control scheme assures that both the position and the internal force trajectory errors converge asymptotically. To depict the adequacy of the proposed control scheme, simulation results are provided with different existing controllers in a comparative manner.