Collision detection for multiple robot manipulatyors by using orthogonal neural networks

This article discusses the application of orthogonal neural networks to detect collisions between multiple robot manipulators that work in an overlapped space. By applying an expansion/shrinkage algorithm, the problem of collision detection between arms is transformed into that among cylinders (or rectangular solids) and line segments. This mapping simplifies the collision detection problem and thus neural networks can be applied to solve it. The property of parallel processing enables neural networks to detect collisions rapidly. A single-layer orthogonal neural network is developed to avert the problems of conventional multilayer feedforward neural networks such as initial weights and the number of layers and processing elements. This orthogonal neural network can approximate various functions and is used to calculate forward solution and to detect collisions. An efficient neural network system for collision detection is also developed. © 1995 John Wiley & Sons, Inc.     

Force control of a robotic manipulator by application of a neural network

In this paper, a force control method for robotic manipulators which utilize a neural network model is proposed with consideration of the dynamics of objects. The proposed system consists of a standard PID controller and a multilayered neural network model, which optimizes a set of controller's parameters via a process of learning. The neural network model has not yet been applied to force control problems, but the proposed method is shown to be applicable to force/compliance control problems. The stability of this system and a wider applicability are verified by simulation studies.     

Collision: Modeling,simulation and identification of robotic manipulators interacting with environments

The performance of many robotic tasks depends greatly on their dynamic collision behavior. This article presents a simple method for modeling and simulating collision behavior in manipulators. The main goal in this task is to provide informative contact models. The proposed models encompasscollision attributes which comprise not only (local) contact surface properties but also structural properties of the environmental object and the manipulator. With this method, the entire dynamic and interactive motion of the manipulator with the environmental object can be simulated effectively. This is verified by our simulation results. To facilitate our investigation, a 2 DOF planar elbow manipulator with PD control is considered in the simulations as well as theoretical analysis. The simulation results are used to highlight the collision attributes which affect collision behavior and to study the effects of these attributes on the manipulator-work environment safety and performance. On the other hand, the reliable operation of intelligent robotic systems in unstructured environments requires the estimation of collision attributes before the prediction of the collision behavior can be completed. For this purpose, we introduce the notion ofcollision identification. The present paper introduces a framework for collision identification in robotic tasks. The proposed framework is based on Artificial Neural Networks (ANNs) and provides fast and relatively reliable identification of the collision attributes. The simulation results are used to generate training data for the set of ANNs. A modularized ANN-based architecture is also developed to reduce the training effort and to increase the accuracy of ANNs. The test results indicate the satisfactory performance of the proposed collision identification system.     

Dynamics and Noncollocated Model‐Free Position Control for a Space Robot with Multi‐Link Flexible Manipulators

In this paper, both the dynamics and noncollocated model‐free position control (NMPC) for a space robot with multi‐link flexible manipulators are developed. Using assumed modes approach to describe the flexible deformation, the dynamic model of the flexible space robotic system is derived with Lagrangian method to represent the system dynamic behaviors. Based on Lyapunov's direct method, the robust model‐free position control with noncollocated feedback is designed for position regulation of the space robot and vibration suppression of the flexible manipulators. The closed‐loop stability of the space robotic system can be guaranteed and the guideline of choosing noncollocated feedback is analyzed. The proposed control is easily implementable for flexible space robot with both uncertain complicated dynamic model and unknown system parameters, and all the control signals can be measured by sensors directly or obtained by a backward difference algorithm. Numerical simulations on a two‐link flexible space robot are provided to demonstrate the effectiveness of the proposed control.     

An iterative learning scheme for motion control of robots using neural networks: A case study

In this paper, an iterative learning controller using neural networks has been studied for the motion control of robotic manipulators. Simulations of a two-link robot have demonstrated that the proposed control scheme for robotic manipulators can greatly reduce tracking errors after a few trials. Our modification of the original back-propagation algorithm is employed in the neural network, resulting in a much faster learning rate. The results of simulation have also shown that the proposed iterative learning controller has a faster rate of convergence and better robustness.     

Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.     

双臂空间机器人的避自碰轨迹规划研究*

本文针对自由漂浮的双臂空间机器人系统研究了一种基于危险域的避自碰轨迹规划方案。首先,引入危险域的概念,用来评估两个机械臂之间发生碰撞的危险程度。其次,在路径规划的基础之上,利用危险域的反馈信息,设计了一种安全避自碰的轨迹规划方案,用以保证两个机械臂可以运动在安全位型,从而避免发生自碰。最后,针对一个双臂冗余空间机器人系统进行运动仿真,仿真结果验证了本文方法的有效性。     

Neural network based dynamic modeling of flexible‐link manipulators with application to the SSRMS

This paper presents an approach for dynamic modeling of flexible‐link manipulators using artificial neural networks. A state‐space representation is considered for a neural identifier. A recurrent network configuration is obtained by a combination of feedforward network architectures with dynamical elements in the form of stable filters. To guarantee the boundedness of the states, a joint PD control is introduced in the system. The method can be considered both as an online identifier that can be used as a basis for designing neural network controllers as well as an offline learning scheme to compute deflections due to link flexibility for evaluating forward dynamics. Unlike many other methods, the proposed approach does not assume knowledge of the nonlinearities of the system nor that the nonlinear system is linear in parameters. The performance of the proposed neural identifier is evaluated by identifying the dynamics of different flexible‐link manipulators. To demonstrate the effectiveness of the algorithm, simulation results for a single‐link manipulator, a two‐link planar manipulator, and the Space Station Remote Manipulator System (SSRMS) are presented. ©2000 John Wiley & Sons, Inc.     

Neural network approaches to dynamic collision-free trajectorygeneration

In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.     

Adaptive neural network control of coordinated manipulators

In this article, adaptive neural network control of coordinated manipulators is considered in an effort to eliminate the time‐consuming and error prone dynamic modeling process which is necessary for the implementation of conventional adaptive control. After a concise dynamic model in the object coordinate space is developed for the coordinated manipulators, an adaptive neural network controller is presented by combining the techniques of neural network parameterization, adaptive control, and sliding mode control. It can be shown that the motion tracking errors converge to zero asymptotically whereas the internal force tracking error remains bounded and can be made arbitrarily small. Numerical simulations are conducted to show the effectiveness of the proposed method. ©1999 John Wiley & Sons, Inc.     

Dynamics modeling of robotic manipulators using an artificial neural network

Dynamics modeling is important for the design, analysis, simulation, and control of robotic and other computer-controlled mechanical systems. The complete dynamic modeling of such systems involves the computationally intensive solution of a set of non-linear, coupled differential equations. Artificial neural networks are well suited for this application due to their ability to represent complex functions and, potentially, to operate in real time. The application of an artificial neural network to dynamics modeling of robotic systems is investigated. The Cerebellar Model Arithmetic Computer (CMAC) is employed. A hybrid implementation of CMAC is proposed to allow use of the model for either simulation or control of robotic manipulators. The success of the simulated results and the accuracy of the generated outputs after a few training cycles demonstrate great promise for further development of the method and its implementation in control systems. © 1994 John Wiley & Sons, Inc.     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

Collision‐free control of robotic manipulators in the task space

This paper addresses the problem of position control of robotic manipulators in the task space with obstacles. A computationally simple class of task space regulators consisting of a transpose Jacobian controller plus an integral term including the task error and the gradient of a penalty function generated by obstacles is proposed. The Lyapunov stability theory is used to derive the control scheme. Through the use of the exterior penalty function approach, collision avoidance of the robot with obstacles is ensured. The performance of the proposed control strategy is illustrated through computer simulations for a direct‐drive arm of a SCARA type manipulator operating in both an obstacle‐free task space and a task space including obstacles. © 2005 Wiley Periodicals, Inc.     

A study of neural network based inverse kinematics solution for a three-joint robot

A neural network based inverse kinematics solution of a robotic manipulator is presented in this paper. Inverse kinematics problem is generally more complex for robotic manipulators. Many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. In this study, a three-joint robotic manipulator simulation software, developed in our previous studies, is used. Firstly, we have generated many initial and final points in the work volume of the robotic manipulator by using cubic trajectory planning. Then, all of the angles according to the real-world coordinates (x, y, z) are recorded in a file named as training set of neural network. Lastly, we have used a designed neural network to solve the inverse kinematics problem. The designed neural network has given the correct angles according to the given (x, y, z) cartesian coordinates. The online working feature of neural network makes it very successful and popular in this solution.     

Hierarchical intelligent control for robotic motion

This paper presents a new scheme for intelligent control of robotic manipulators. This scheme is a hierarchically integrated approach to neuromorphic and symbolic control of robotic manipulators. This includes an applied neural network for servo control and knowledge-based approximation. The neural network in the servo control level is based on a numerical manipulation, while the knowledge based part is symbolic manipulation. The knowledge base part develops control strategies symbolically for the servo level. The neural network compensates for vagueness in the control strategies, nonlinearities of the system and uncertainties in its environment using neuromorphic control.     

Dynamically consistent Jacobian inverse for mobile manipulators

By analogy to the definition of the dynamically consistent Jacobian inverse for robotic manipulators, we have designed a dynamically consistent Jacobian inverse for mobile manipulators built of a non-holonomic mobile platform and a holonomic on-board manipulator. The endogenous configuration space approach has been exploited as a source of conceptual guidelines. The new inverse guarantees a decoupling of the motion in the operational space from the forces exerted in the endogenous configuration space and annihilated by the dual Jacobian inverse. A performance study of the new Jacobian inverse as a tool for motion planning is presented.     

Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network

The aim of this paper was to propose a recurrent neural network-based predictive controller for robotic manipulators. A neural network controller for a six-joint Stanford robotic manipulator was designed using the generalized predictive control (GPC) and the Elman network. The GPC algorithm, which is a class of digital control method, requires long computational time. This is a disadvantage in real-time robot control; therefore, the Elman network controller was designed to reduce processing time by avoiding the highly mathematical and computational complexity of the GPC. The main reason for choosing the Elman network, amongst several neural network algorithms, was that the presence of feedback loops have a profound impact on the learning capability of the network. The designed neural network controller was able to recover quickly because of its significant generalization capability, which allowed it to adapt very rapidly to changes in inputs. The performance of the controller was also shown graphically using simulation software, including the dynamics and kinematics of the robot model.     

考虑驱动系统动态的机械手神经网络控制及应用

针对结构和参数均未知的机械手控制问题, 提出了考虑驱动系统动态的机械手神经网络控制方法, 采用稳定的径向基(Radial basis function, RBF)神经网络辨识机械手未知动态, 而附加的鲁棒控制可以保证存在神经网络的建模误差和外部干扰时系统的稳定性和性能, 并且该方法使机械手闭环系统一致最终有界. 同时开发了基于半实物仿真技术的机械手控制系统, 最后, 将本文方法与经典的PD控制器和自适应控制器在同一机械手平台上进行了实验验证与分析, 实验结果表明该方法具有良好的控制性能.     

空间机器人位姿空间分层量化建模方法

本文首先分析了空间机器人在位姿空间的不完全可控性及其给运动规划带来的困难．在此基础上，本文提出一种适用于空间机器人的位姿空间分层量化建模方法．此方法首先对位姿空间进行了分层量化处理，然后通过定义位姿间的相邻关系而建立了位姿空间图（ＣＳＧ），并对ＣＳＧ的顶点和边的碰撞情况做了规定．最后本文针对一个空间机器人模型给出一个位姿空间建模的实例和基于此建模方法的运动规划结果     

Neural-network-based robust fault diagnosis in robotic systems

Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators with modeling uncertainties. A learning architecture with sigmoidal neural networks is used to monitor the robotic system for any off-nominal behavior due to faults. The robustness and stability properties of the fault diagnosis scheme are rigorously established. Simulation examples are presented to illustrate the ability of the neural-network-based robust fault diagnosis scheme to detect and accommodate faults in a two-link robotic manipulator.