Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints

Dual robotic manipulators are robotic systems that are developed to imitate human arms, which shows great potential in performing complex tasks. Collision-free motion planning in real time is still a challenging problem for controlling a dual robotic manipulator because of the overlap workspace. In this paper, a novel planning strategy under physical constraints of dual manipulators using dynamic neural networks is proposed, which can satisfy the collision avoidance and trajectory tracking. Particularly, the problem of collision avoidance is first formulated into a set of inequality formulas, whereas the robotic trajectory is then transformed into an equality constraint by introducing negative feedback in outer loop. The planning problem subsequently becomes a Quadratic Programming (QP) problem by considering the redundancy, the boundaries of joint angles and velocities of the system. The QP is solved using a convergent provable recurrent neural network that without calculating the pseudo-inversion of the Jacobian. Consequently, numerical experiments on 8-DoF modular robot and 14-DoF Baxter robot are conducted to show the superiority of the proposed strategy

Active Sensing of Robot Arms Based on Zeroing Neural Networks: A Biological-Heuristic Optimization Model

Conventional biological-heuristic solutions via zeroing neural network (ZNN) models have achieved preliminary efficiency on time-dependent nonlinear optimization problems handling. However, the investigation on finding a feasible ZNN model to solve the time-dependent nonlinear optimization problems with both inequality and equality constraints still remains stagnant because of the nonlinearity and complexity. To make new progresses on the ZNN for time-dependent nonlinear optimization problems solving, this paper proposes a biological-heuristic optimization model, i.e., inequality and equality constrained optimization ZNN (IECO-ZNN). Such a proposed IECO-ZNN breaks the conditionality that the solutions via ZNN for solving nonlinear optimization problems can not consider the inequality and equality constraints at the same time. The time-dependent nonlinear optimization problem subject to inequality and equality constraints is skillfully converted to a time-dependent equality system by exploiting the Lagrange multiplier rule. The design process for the IECO-ZNN model is presented together with its new architecture illustrated in details. In addition, the conversion equivalence, global stability as well as exponential convergence property are theoretically proven. Moreover, numerical studies, real-world applications to robot arm active sensing, and comparisons sufficiently verify the effectiveness and superiority of the proposed IECO-ZNN model for the time-dependent nonlinear optimization with inequality and equality constraints

Recent Advances in Robust Control

Robust control has been a topic of active research in the last three decades culminating in H_2/H_\infty and \mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein