scholarly journals An Exponential Varying-Parameter Neural Network for Repetitive Tracking of Mobile Manipulators

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ying Kong ◽  
Qingqing Tang ◽  
Jingsheng Lei ◽  
Ruiyang Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Matrix Equation ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Time Varying ◽  
Repetitive Motion ◽  
End Effector ◽  
The Matrix ◽  
Simulation Results

A novel exponential varying-parameter neural network (EVPNN) is presented and investigated to solve the inverse redundancy scheme of the mobile manipulators via quadratic programming (QP). To suspend the phenomenon of drifting free joints and guarantee high convergent precision of the end effector, the EVPNN model is applied to trajectory planning of mobile manipulators. Firstly, the repetitive motion scheme for mobile manipulators is formulated into a QP index. Secondly, the QP index is transformed into a time-varying matrix equation. Finally, the proposed EVPNN method is used to solve the QP index via the matrix equation. Theoretical analysis and simulations illustrate that the EVPNN solver has an exponential convergent speed and strong robustness in mobile manipulator applications. Comparative simulation results demonstrate that the EVPNN possesses a superior convergent rate and accuracy than the traditional ZNN solver in repetitive trajectory planning with a mobile manipulator.

Task space control of mobile manipulators

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 221-232 ◽  
Author(s):  
Mirosław Galicki
Keyword(s):  
State Constraints ◽  
Lyapunov Stability Theory ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Minimal Energy ◽  
Task Space ◽  
End Effector ◽  
Task Space Control ◽  
Simulation Results ◽  
Penalty Function Approach

SUMMARYThis study offers the solution of the end-effector trajectory tracking problem subject to state constraints, suitably transformed into control-dependent ones, for mobile manipulators. Based on the Lyapunov stability theory, a class of controllers fulfilling the above constraints and generating the mobile manipulator trajectory with (instantaneous) minimal energy, is proposed. The problem of manipulability enforcement is solved here based on an exterior penalty function approach which results in continuous mobile manipulator controls even near boundaries of state constraints. The numerical simulation results carried out for a mobile manipulator consisting of a non-holonomic unicycle and a holonomic manipulator of two revolute kinematic pairs, operating in a two-dimensional task space, illustrate the performance of the proposed controllers.

Cooperation and Null-Space Control of Networked Omni-Directional Mobile Manipulators

2020 ◽  
Author(s):  
Michael John Chua ◽  
Yen-Chen Liu
Keyword(s):  
Degrees Of Freedom ◽  
Null Space ◽  
Kinematic Model ◽  
Mobile Manipulator ◽  
Main Task ◽  
Mobile Manipulators ◽  
Robot Arm ◽  
Task Space ◽  
End Effector ◽  
Mobile Base

Abstract This paper presents cooperation and null-space control for networked mobile manipulators with high degrees of freedom (DOFs). First, kinematic model and Euler-Lagrange dynamic model of the mobile manipulator, which has an articulated robot arm mounted on a mobile base with omni-directional wheels, have been presented. Then, the dynamic decoupling has been considered so that the task-space and the null-space can be controlled separately to accomplish different missions. The motion of the end-effector is controlled in the task-space, and the force control is implemented to make sure the cooperation of the mobile manipulators, as well as the transportation tasks. Also, the null-space control for the manipulator has been combined into the decoupling control. For the mobile base, it is controlled in the null-space to track the velocity of the end-effector, avoid other agents, avoid the obstacles, and move in a defined range based on the length of the manipulator without affecting the main task. Numerical simulations have been addressed to demonstrate the proposed methods.

New repetitive motion planning scheme with cube end-effector planning precision for redundant robotic manipulators

Robotica ◽  
2021 ◽  
pp. 1-22
Author(s):  
Limin Shen ◽  
Yuanmei Wen
Keyword(s):  
Theoretical Analysis ◽  
Motion Planning ◽  
Robotic Manipulators ◽  
Robotic Manipulator ◽  
Repetitive Motion ◽  
End Effector ◽  
Difference Formula ◽  
Planning Scheme ◽  
Simulation Results ◽  
The Difference

Abstract Repetitive motion planning (RMP) is important in operating redundant robotic manipulators. In this paper, a new RMP scheme that is based on the pseudoinverse formulation is proposed for redundant robotic manipulators. Such a scheme is derived from the discretization of an existing RMP scheme by utilizing the difference formula. Then, theoretical analysis and results are presented to show the characteristic of the proposed RMP scheme. That is, this scheme possesses the characteristic of cube pattern in the end-effector planning precision. The proposed RMP scheme is further extended and studied for redundant robotic manipulators under joint constraint. Based on a four-link robotic manipulator, simulation results substantiate the effectiveness and superiority of the proposed RMP scheme and its extended one.

Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

2011 ◽  
pp. 310-325
Author(s):  
Xiang-min Tan ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Dong Xu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Integrated Control ◽  
External Disturbance ◽  
Torque Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Modeling And Control ◽  
Control Approach ◽  
Proposed Model

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this paper, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

scholarly journals Type Synthesis, Modelling and Analysis of the Manipulator for Wheel-Legged Robot

2016 ◽  
Vol 10 (2) ◽  
pp. 87-91
Author(s):  
Jarosław Szrek ◽  
Artur Muraszkowski ◽  
Przemysław Sperzyński
Keyword(s):  
Simulation Software ◽  
Dynamics Simulation ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Legged Robot ◽  
Type Synthesis ◽  
Structural Synthesis ◽  
New Approach ◽  
Basic Dimension ◽  
Simulation Results

Abstract The aim of this article is to present the concept of wheel-legged mobile manipulator, which is a combination of mobile platform with specially selected suspension system and a manipulator. First, a literature review was performed and own solution proposed. The kinematic structure of manipulator, selected simulation results, physical model and the concept of the control system has been presented. Geometry synthesis was used to design basic dimension. Structural synthesis was performed according to the intermediate chain method. Simulations were performed using the multibody dynamics simulation software. New approach in the field of the mobile manipulators was presented as a result.

Trajectory planning of mobile manipulators using dynamic programming approach

Robotica ◽  
2012 ◽  
Vol 31 (4) ◽  
pp. 643-656 ◽  
Author(s):  
M. H. Korayem ◽  
M. Irani ◽  
A. Charesaz ◽  
A. H. Korayem ◽  
A. Hashemi
Keyword(s):  
Dynamic Programming ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Vision System ◽  
Optimal Path ◽  
Optimization Method ◽  
Mobile Manipulator ◽  
Programming Approach ◽  
Planning Problem ◽  
Mobile Manipulators

SUMMARYThis paper presents a solution for optimal trajectory planning problem of robotic manipulators with complicated dynamic equations. The main goal is to find the optimal path with maximum dynamic load carrying capacity (DLCC). Proposed method can be implemented to problems of both motion along a specified path and point-to-point motion. Dynamic Programming (DP) approach is applied to solve optimization problem and find the positions and velocities that minimize a pre-defined performance index. Unlike previous attempts, proposed method increases the speed of convergence by using the sequential quadratic programming (SQP) formulation. This formulation is used for solving problems with nonlinear constraints. Also, this paper proposes a new algorithm to design optimal trajectory with maximum DLCC for both fixed and mobile base mechanical manipulators. Algorithms for DLCC calculations in previous works were based on indirect optimization method or linear programming approach. The proposed trajectory planning method is applied to a linear tracked Puma and the mobile manipulator named Scout. Application of this algorithm is confirmed and simulation results are compared with experimental results for Scout robot. In experimental test, results are obtained using a new stereo vision system to determine the position of the robot end-effector.

scholarly journals Trajectory Planning with Pose Feedback for a Dual-Arm Space Robot

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yicheng Liu ◽  
Kedi Xie ◽  
Tao Zhang ◽  
Ning Cai
Keyword(s):  
Trajectory Planning ◽  
Path Tracking ◽  
Space Robot ◽  
End Effector ◽  
Planning Algorithm ◽  
Kinematic Models ◽  
Simulation Results ◽  
Singular Configuration ◽  
Theoretical Analyses ◽  
Dual Arm

In order to obtain high precision path tracking for a dual-arm space robot, a trajectory planning method with pose feedback is proposed to be introduced into the design process in this paper. Firstly, pose error kinematic models are derived from the related kinematics and desired pose command for the end-effector and the base, respectively. On this basis, trajectory planning with pose feedback is proposed from a control perspective. Theoretical analyses show that the proposed trajectory planning algorithm can guarantee that pose error converges to zero exponentially for both the end-effector and the base when the robot is out of singular configuration. Compared with the existing algorithms, the proposed algorithm can lead to higher precision path tracking for the end-effector. Furthermore, the algorithm renders the system good anti-interference property for the base. Simulation results demonstrate the effectiveness of the proposed trajectory planning algorithm.

Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

2009 ◽  
Vol 3 (4) ◽  
pp. 34-53 ◽  
Author(s):  
Xiang-min Tan ◽  
Dongbin Zhao ◽  
Jianqiang Yi ◽  
Dong Xu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Integrated Control ◽  
External Disturbance ◽  
Torque Control ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Modeling And Control ◽  
Control Approach ◽  
Proposed Model

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this article, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

scholarly journals TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators

2021 ◽  
Author(s):  
Shreshth Tuli ◽  
Rajas Bansal ◽  
Rohan Paul ◽  
Mausam .
Keyword(s):  
Neural Network ◽  
Neural Model ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Commonsense Knowledge ◽  
Symbolic Action ◽  
World State ◽  
The World ◽  
Human Teachers ◽  
High Level

Robots assisting us in factories or homes must learn to make use of objects as tools to perform tasks, e.g., a tray for carrying objects. We consider the problem of learning commonsense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. We introduce TANGO, a novel neural model for predicting task-specific tool interactions. TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator. TANGO encodes the world state consisting of objects and symbolic relationships between them using a graph neural network. The model learns to attend over the scene using knowledge of the goal and the action history, finally decoding the symbolic action to execute. Crucially, we address generalization to unseen environments where some known tools are missing, but alternative unseen tools are present. We show that by augmenting the representation of the environment with pre-trained embeddings derived from a knowledge-base, the model can generalize effectively to novel environments. Experimental results show a 60.5-78.9% improvement over the baseline in predicting successful symbolic plans in unseen settings for a simulated mobile manipulator.

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