Control Of Mobile Manipulators Research Articles

Backstepping based intelligent control of tractor-trailer mobile manipulators with wheel slip consideration

In this research, a new hybrid backstepping control strategy based on a neural network is proposed for tractor-trailer mobile manipulators in the presence of unknown wheel slippage and disturbances. To minimize the negative impacts of wheel slippage, the desired velocities of the tractor’s wheels are computed with a proposed kinematic control model with an adaptive term. As the system’s dynamical model contains unavoidable uncertainties, model-based backstepping control technique is unable to effectively manage these systems. Hence, the proposed controller blends a radial basis function neural network with the merits of a dynamical model-based backstepping approach. The neural networks are employed to approximate the non-linear unknown smooth function. To minimize the impact of external disturbances, and network reconstruction error an adaptive term is added to the control law. The Lyapunov theorem and Barbalat’s lemma are employed to guarantee the stability of the control method. The tracking error is shown to be bounded and to rapidly converge to zero with the proposed method. To demonstrate the efficacy and validity of the control mechanism, comparison simulation results are presented.

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

Whole Body Control of Mobile Manipulators With Series Elastic Actuators for Cart Pushing Tasks.

Human-centered environments provide affordance for the use of two-handed mobile manipulators. Yet robots designed to function in and physically interact with such environments are not yet capable of meeting human users' requirements. This work proposes a whole body control framework of a two-handed mobile manipulator driven by series elastic actuators (SEAs) for cart pushing tasks. A whole body dynamic model for an integrated mobile platform and on-board arms is revealed, which takes into account the interaction forces with the cart. Then, the explicit force/position control of the mobile manipulator is performed. It enables the robot to interact dynamically with the environment while providing motion, i.e., the manipulators provide both output force control and motion control for pushing a cart. To cope with the highly nonlinear system dynamics and parameter variation of a SEA-driven mobile manipulator, this work proposes an adaptive robust controller based on a novel integral barrier Lyapunov function for cart pushing tasks by considering model uncertainty. The proposed controller enables the mobile manipulator to complete cart pushing tasks by regulating the position and output force of the mobile base and arms. The experimental results show the effectiveness of this approach in cart pushing tasks.

Trajectory Tracking Control of Mobile Manipulator Based on Improved Sliding Mode Control Algorithm

Research on trajectory tracking control for climbing welding robots holds significant importance in the field of automated welding. However, existing trajectory tracking methods suffer from issues such as jitter and slow speed. In this paper, an improved sliding mode control strategy is proposed based on the self-designed wall-climbing welding mobile manipulator. Firstly, a new adaptive sliding mode control strategy is proposed for the mobile platform based on the kinematic model. By introducing a new approach law, the controller is designed when the distance between the center of mass is unknown. Secondly, regarding the manipulator, we analyze simplified dynamic equations, extract uncertain components, and utilize a CNN for compensation. This compensation strategy is integrated into the sliding mode control law, achieving precise control over the manipulator and effectively resolving issues like slow tracking speeds, large errors, and chattering. The stability of the robot control system is proved by the Lyapunov function. Through simulation analysis and experimental validation, the proposed control method is confirmed to be feasible and superior.

Position control of input-delayed mobile manipulator

Position control of input-delayed mobile manipulator

Development of 3D printed mecanum mobile manipulator and task-space control

Development of 3D printed mecanum mobile manipulator and task-space control

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.

Simultaneous motion and shape control of redundant mobile manipulators

This work proposes the design of a simultaneous motion and shape control of a redundant mobile manipulator. The method is based on the construction of a variable weighting matrix that allows to control of specific sections of this type of robot composed of a mobile platform and manipulator arm. The weight matrix allows for prioritizing desired parts of the robot through cost functions that depend on an additional variable defined in this work, which can be changed automatically according to some pre-established criteria or by a human operator if the robot is teleoperated. In addition, simulations are included to show the usefulness of the proposal.

A Distributed Unscented Predictive Cooperation Approach for Networked Mobile Manipulators

Cooperation control of networked mobile manipulators is a challenging task to be met. This paper presents a distributed unscented model predictive control method to address the problem of cooperation control for networked mobile manipulators with uncertain parameters. Based on the Euler-Lagrange approach, an auxiliary model with uncertain parameters and disturbances is obtained via the nonlinear feedback technique. Under the framework of distributed predictive control, a local optimization problem with chance constraints is established. An equivalent optimization problem based on the probability distribution predictive model is derived using the unscented transform to approximate the probability distributions for nonlinear models. Moreover, a sufficient condition guaranteeing the overall system's stability is developed. The performance of the proposed distributed unscented predictive control strategy is validated by simulation examples.

Force/position Control of Constrained Mobile Manipulators with Fast Terminal Sliding Mode Control and Neural Network

Force/position Control of Constrained Mobile Manipulators with Fast Terminal Sliding Mode Control and Neural Network

Robust Sampling-Based Control of Mobile Manipulators for Interaction With Articulated Objects

In this article, we investigate and deploy sampling-based control techniques for the challenging task of the mobile manipulation of articulated objects. By their nature, manipulation tasks necessitate environment interactions, which require the handling of nondifferentiable switching contact dynamics. These dynamics represent a strong limitation for traditional gradient-based optimization methods, such as model-predictive control and differential dynamic programming, which often rely on heuristics for trajectory generation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sampling-based</i> techniques alleviate these constraints but do not ensure robots' stability and input/state constraints either. On the other hand, real-world applications in human environments require safety and robustness to unexpected events. For this reason, we propose a novel framework for safe robotic manipulation of movable articulated objects. The framework combines sampling-based control together with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">control barrier functions</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passivity theory</i> that, thanks to formal stability guarantees, enhance the safety and robustness of the method. We also provide the practical insights that enable robust deployment of stochastic control using a conventional central processing unit. We deploy the algorithm on a ten-degree-of-freedom mobile manipulator robot. Finally, we open source our generic and multithreaded implementation.

Adaptive sliding mode security control of wheeled mobile manipulators with Markov switching joints against adversarial attacks

This paper deals with the security control problem of a wheeled mobile manipulator that is subjected to various challenges, such as Markov switching parameters, actuator false data injection attacks, un-modeled nonlinearities, and deficient transition rates. As actuator attacks are often not fully known or predictable, an adaptive scheme is proposed to dynamically eliminate their negative effects. To achieve an ideal sliding motion, a smooth integral hyperplane is introduced. Since the transition rates of the joints may vary, it is important to design a controller that can maintain the expected sliding motion under different mode information. Additionally, stochastic stability analysis is conducted for the sliding motion considering different types of transition rates. Finally, simulation results are provided to demonstrate the effectiveness of the proposed method.

Improved sliding mode control for mobile manipulators based on an adaptive neural network

Improved sliding mode control for mobile manipulators based on an adaptive neural network

WebRTC-based MOSR remote control of mobile manipulators.

This article describes a contribution to the field of telerobotics via the Internet through the development of a web-based platform allowing the remote control of robots by multiple users, simultaneously. It also deals with minimizing the execution times of tasks by reducing connection and interaction delays. For this purpose, the Web Real-Time Communication (WebRTC) technology is utilized. The developed remote manipulation system allows the operators to visualize the robot, its surroundings and the data incoming from its sensors, and to carry out basic tasks, either independently by the manipulator or by the mobile robot, or jointly by both mechanical sub-systems. In addition, to ensure the control of the remote robot by several operators simultaneously, a priority system managing parallel tasks and a chat system between the operators have been proposed. Besides, many teams are able to exploit the robot, concurrently. The WebRTC-based Multiple Operator Single Robot (MOSR) telerobotic platform is validated on the emulator of the RobuTER/ULM mobile manipulator through various scenarios of primitive tasks over the Internet.

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

Potential Field Control of a Redundant Nonholonomic Mobile Manipulator with Corridor-Constrained Base Motion

This work proposes a solution for redundant nonholonomic mobile manipulator control with corridor constraints on base motion. The proposed control strategy applies an artificial potential field for base navigation to achieve joint control with desired trajectory tracking of the end effector. The overall kinematic model is created by describing the nonholonomic mobile platform and the kinematics of the manipulator. The objective function used consists of a primary control task that optimizes the joint variables to achieve the desired pose or trajectory of the end effector and a secondary control task that optimizes the joint variables for the base to support the arm and stay within the corridor. As a last priority, an additional optimization is introduced to optimize the maneuverability index. The proposed baseline navigation has global convergence without local minima and is computationally efficient. This is achieved by an optimal grid-based search on a coarse discrete grid and a bilinear interpolation to obtain a continuous potential function and its gradient. The performance of the proposed control algorithm is illustrated by several simulations of a mobile manipulator model derived for a Pal Tiago mobile base and an Emiko Franka Panda robotic manipulator.

Mixed reality-based brain computer interface system using an adaptive bandpass filter: Application to remote control of mobile manipulator

Brain-computer interface (BCI) systems based on mixed reality (MR) have promising applications in assisting people with disabilities to control manipulators. Using MR glasses instead of a computer screen to display visual stimulator can effectively avoid frequent switching of attention between the visual stimulator and the manipulator. When the manipulator moves out of the sight of the subject, the subject may not be able to control it accurately. Our system uses Microsoft Hololens2 as the display device to synchronize the command matrix with a live view of the mobile manipulator's position, thus tracking the position in real-time. Another problem in previous studies is that they have good accuracy in trained subjects, however, the accuracy drops dramatically when faced with untrained subjects, suggesting poor generalization capabilities. In our study, an adaptive filtering method combined with convolutional neural networks (CNN) is proposed, which has few learning parameters and fast convergence, and can improve the generalization ability of the system in the face of untrained subjects. When faced with untrained subjects, the average accuracy of our method was 93.04%, and the average ITR was 20.96 bits/min. All subjects can successfully complete the grasping task without colliding with obstacles. The results show that the BCI system developed in this study has strong practicability and high research significance.

Distributed Cooperative Control of Redundant Mobile Manipulators With Safety Constraints.

In this article, the distributed cooperative control problem of redundant mobile manipulators is investigated. A novel method is proposed to solve the problem by integrating formation control with constrained optimization, which not only transports the object along a reference trajectory in a distributed manner but also obtains the dexterous joint postures and end-effector displacements under safety constraints for collision avoidance. For the constrained optimization, the cost function and safety constraints are designed to quantify the mobility and manipulability of mobile manipulators, and collision-free working ranges with the object and obstacles, respectively. A discontinuous projected primal-dual algorithm with damping terms is proposed to solve the constrained optimization problem, providing the joint postures and end-effector displacements, which minimize the cost function and satisfy safety constraints. For the formation control, a finite-time control law, guided by end-effector displacements from the primal-dual algorithm, is developed in order to transport the object by establishing a prescribed formation and moving its centroid to track the reference trajectory. The cooperative manipulation is therefore achieved by the proposed method, which is further validated through numerical simulations.

Integrated Task Sensing and Whole Body Control for Mobile Manipulation With Series Elastic Actuators

In this paper, an integrated framework consisting of the sensing, navigation and control is proposed for an autonomous mobile manipulator driven by series elastic actuators (SEAs) to preform mobile manipulation tasks in unknown environments. First, ORB-SLAM2 technique is combined into the environment sensing by extracting the ORB features, automatic initialization, repositioning and loop detection for real-time posture estimation. Then, the navigation function is designed for generating collision-free trajectory in an environment with obstacles. To realize kinematic and dynamic control of the mobile manipulator with the developed SEA joints, the whole body dynamics is considered and described. And to handle dynamic uncertainties and the SEA inherent saturation limits, a novel adaptive neural network control considering the whole body dynamics is proposed. Without knowing the exact parameters of the whole body model, the designed controller merely requires the position and velocity of the actuators and links, which can make the tracking errors converge to zero and keep all signals uniformly bounded in the closed-loop system. The performance and efficiency of the proposed method are verified by extensive experiments. Note to Practitioners—This paper is motivated by issues of manipulation control of autonomous unmanned system. Traditional manipulation frameworks focus either on sensing or control by assuming that the environment is known, which would result in lacking of autonomy for a specified task. Since mobile manipulation tasks often consist of nonholonomic and holonomic constraints for wheeled mobile manipulators with differential steering. In addition, most current works for whole body control are based on the condition that robot dynamic parameters are known beforehand. Therefore, it is necessary to establish an enhanced framework to simultaneously deal with these problems. In this paper, an integrated navigation and control framework is proposed. To make the mobile manipulator work in the unknown environment, task sensing and whole body control for mobile manipulators are also developed. The framework is partitioned into the task sensing, navigation and control, where the mobile manipulator can fulfill the mobile manipulation tasks in the unstructured environments.