Mutual-Collision-Avoidance Scheme Synthesized by Neural Networks for Dual Redundant Robot Manipulators Executing Cooperative Tasks

Zhang, Zhijun; Zheng, Lunan; Chen, Zhuoming; Kong, Lingdong; Karimi, Hamid Reza

doi:10.1109/tnnls.2020.2980038

Cited by 53 publications

(24 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using this method, multiple objectives, such as robot joint constraints, target tracking, and repetitive motion planning can be simultaneously achieved by describing them as attachment equality or inequality constraints. Apart from being applied to manipulators, the inequality collision-avoidance method was used for a wheeled mobile manipulator and dual redundant robot manipulators in [37] and [38], respectively. Compared to other collision-avoidance methods, the method is simple and easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Ли

et al. 2022

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collisionavoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collisionavoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free.

show abstract

Section: Introductionmentioning

confidence: 99%

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Ли

et al. 2022

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…It is known that dynamic mapping for modeling, classification, estimation, control, and motion planning can be tackled by recurrent neural network or long-short term memory model [25]- [30]. In [25], a recurrent neural network based multivariable adaptive control is designed to deal with a class of nonlinear dynamic systems with timevarying delay.…”

Section: Introductionmentioning

confidence: 99%

“…In [29], theoretical guidance and adaptive adjustment mechanism are established to set up the base width and central vector of the Gaussian function in the double hidden layer recurrent neural network with six sets of parameters to be adaptively stabilized. To avoid mutual collision of dual robot manipulators while doing collaboration tasks, a recurrent neural network based mutual-collision-avoidance scheme for solving the motion planning problem of dual manipulators is proposed and exploited [30]. Similarly, adaptive control for uncertain nonlinear systems can be achieved by barrier Lyapunov function [31].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network With Fractional Learning-Based Fixed-Time Formation Tracking Constrained Control for a Group of Quadrotors

et al. 2021

View full text Add to dashboard Cite

In this paper, each agent is modeled by the mechanical motion dynamics with the velocity transformation between quadrotor and world coordinates such that trajectory planning and obstacle avoidance are easily accomplished. It is assumed that at least one follower tracks the leader with a specific position, and the other followers maintain the relative position among each other or the leader. If obstacles hinder the motion of the original formation, a piecewise straight-line formation is employed to avoid these obstacles. To fulfill these tasks under the uncertain dynamics, the recurrent neural network with fractional learning-based fixed-time formation tracking constrained control (RNNFL-FTFTCC) is designed by nonlinear filtering error with dynamic fraction order, time-varying switching gain, and recurrent neural network learning compensation of dynamic lumped uncertainties in each quadrotor. The simulations with the initial formation error, the formation change in a narrow space, and the target point approach validate the effectiveness and robustness of the proposed formation control. Moreover, the comparisons among nonadaptive, RNN, and multilayer perceptron network (MLPN) compensations confirm the effectiveness and efficiency of fractional learning.

show abstract

“…Furthermore, it has been proven that their higher number DoFs in the joint space than in the workspace can also be utilized to guarantee or increase flexibility for complicated tasks by optimizing its manipulability 15 . An excellent movement ability and the possibility to reach difficult points using the surgical tip can be achieved with the optimization strategy 16 ,.…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of manipulability optimization control of a 7‐DoF serial manipulator for robot‐assisted surgery

Danioni

Mira

et al. 2020

Robotics Computer Surgery

View full text Add to dashboard Cite

Purpose Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot‐assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7‐degree of freedom (DoF) robot in a surgical operation. Methods Three different paths, a circle, a sinusoid and a spiral were chosen to simulate typical surgical tasks. The dynamic neural network‐based manipulability optimization control was implemented on a 7‐DoF robot manipulator. During the surgical operation procedures, the manipulability of the robot manipulator and the accuracy of the surgical operation are recorded for performance validation. Results By comparison, the dynamic neural network‐based manipulability optimization control achieved optimized manipulability but with a loss of the accuracy of trajectory tracking (the global error was 1 mm compare to the 0.5 mm error of non‐optimized method). Conclusions The method validated in this work achieved optimized manipulability with a loss of error. Future works should be introduced to improve the accuracy of the surgical operation.

show abstract

Mutual-Collision-Avoidance Scheme Synthesized by Neural Networks for Dual Redundant Robot Manipulators Executing Cooperative Tasks

Cited by 53 publications

References 36 publications

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Recurrent Neural Network With Fractional Learning-Based Fixed-Time Formation Tracking Constrained Control for a Group of Quadrotors

Experimental validation of manipulability optimization control of a 7‐DoF serial manipulator for robot‐assisted surgery

Contact Info

Product

Resources

About