A motion control for a spherical robot with pendulum drive

Problems of controlling a spherical robot with pendulum drive are considered. A mathematical model of the dynamics of this robot is constructed and control laws in the form of state feedback that provide robot motion along a given trajectory are synthesized. Results of computer simulation that demonstrate efficiency of the proposed control laws are presented.     

Constrained motion control of flexible robot manipulators based on recurrent neural networks

In this paper, a neural network approach is presented for the motion control of constrained flexible manipulators, where both the contact force everted by the flexible manipulator and the position of the end-effector contacting with a surface are controlled. The dynamic equations for vibration of flexible link and constrained force are derived. The developed control, scheme can adaptively estimate the underlying dynamics of the manipulator using recurrent neural networks (RNNs). Based on the error dynamics of a feedback controller, a learning rule for updating the connection weights of the adaptive RNN model is obtained. Local stability properties of the control system are discussed. Simulation results are elaborated on for both position and force trajectory tracking tasks in the presence of varying parameters and unknown dynamics, which show that the designed controller performs remarkably well.     

Visual positioning system for small-scaled spherical robot in underwater environment

Microsystem Technologies - Aiming at application requirements of our small-scaled spherical amphibious robots, a visual positioning system adopting the model of depth image and feature fusion was...     

破拆机器人机械臂自主运动控制系统软件设计

针对现有破拆机器人手工定位时间长、定位不精确的问题,研发了一个基于激光定位的破拆机器人机械臂自主运动的控制系统软件。设计了运动学模块、闭环反馈调节模块、液压装置控制模块、手眼标定模块和运动控制模块。实际使用效果表明,该软件系统能完成控制系统设计指标,提高工作效率。     

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.     

Back-propagation neural networks for identification and control of a direct drive robot

A neural approach is proposed to estimate parameters in dynamics of a direct drive robot. Before the estimation, the input-output data for identification are generated in a sequential and term-by-term manner first. Then a two-layer neural network for parameter identification is proposed, in which the back-propagation training method is used to adjust the weights between neurons. The goal is to find the weights that minimize the root-mean-square error between the identification data and output of the network. With the estimated dynamics, existing trajectory-tracking algorithms, such as the well-known computed-torque method, can then be applied to make the robot move along a desired trajectory.     

Dynamical neural networks for planning and low-level robot control

We use dynamical neural networks based on the neural field formalism for the control of a mobile robot. The robot navigates in an open environment and is able to plan a path for reaching a particular goal. We will describe how this dynamical approach may be used by a high level system (planning) for controlling a low level behavior (speed of the robot). We give also results about the control of the orientation of a camera and a robot body.     

Underwater autonomous grasping robot based on multi-stage Cascade DetNet

Artificial Life and Robotics - At present, underwater exploration and salvage, underwater archaeology, and other underwater operations still mainly rely on professional underwater operators....     

Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot

An adaptive controller of nonlinear PID-based analog neural networks is developed for the velocity- and orientation-tracking control of a nonholonomic mobile robot. A superb mixture of a conventional PID controller and a neural network, which has powerful capability of continuously online learning, adaptation and tackling nonlinearity, brings us the novel nonlinear PID-based analog neural network controller. It is appropriate for a kind of plant with nonlinearity uncertainties and disturbances. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity- and orientation-tracking control of the nonholonomic mobile robot. The effectiveness of the proposed control algorithm is demonstrated through the simulation experiment, which shows its superior performance and disturbance rejection.     

Tracking control of a nonholonomic mobile robot using compound cosine function neural networks

The purpose of this paper is to propose a compound cosine function neural network with continuous learning algorithm for the velocity and orientation angle tracking control of a nonholonomic mobile robot with nonlinear disturbances. Herein, two neural network (NN) controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the adaptive control of the mobile robot. The neuron function of the hidden layer in the three-layer feed-forward network structure is on the basis of combining a cosine function with a unipolar sigmoid function. The developed neural network controllers have simple algorithm and fast learning convergence because the weight values are only adjusted between the nodes in hidden layer and the output nodes, while the weight values between the input layer and the hidden layer are one, i.e. constant, without the weight adjustment. Therefore, the main advantages of this control system are the real-time control capability and the robustness by use of the proposed neural network controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances which are considered as dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of nonholonomic mobile robots has real-time control capability, better robustness and higher control precision. The compound cosine function neural network provides us with a new way to solve tracking control problems for mobile robots.     

Design and motion planning of an autonomous climbing robot with claws

This paper presents the design of a novel robot capable of climbing on vertical and rough surfaces, such as stucco walls. Termed CLIBO (claw inspired robot), the robot can remain in position for a long period of time. Such a capability offers important civilian and military advantages such as surveillance, observation, search and rescue and even for entertainment and games. The robot’s kinematics and motion, is a combination between mimicking a technique commonly used in rock climbing using four limbs to climb and a method used by cats to climb on trees with their claws. It uses four legs, each with four-degrees-of-freedom (4-DOF) and specially designed claws attached to each leg that enable it to maneuver itself up the wall and to move in any direction. At the tip of each leg is a gripping device made of 12 fishing hooks and aligned in such a way that each hook can move independently on the wall’s surface. This design has the advantage of not requiring a tail-like structure that would press against the surface to balance its weight. A locomotion algorithm was developed to provide the robot with an autonomous capability for climbing along the pre-designed route. The algorithm takes into account the kinematics of the robot and the contact forces applied on the foot pads. In addition, the design provides the robot with the ability to review its gripping strength in order to achieve and maintain a high degree of reliability in its attachment to the wall. An experimental robot was built to validate the model and its motion algorithm. Experiments demonstrate the high reliability of the special gripping device and the efficiency of the motion planning algorithm.     

Adaptive learning with large variability of teaching signals for neural networks and its application to motion control of an industrial robot

Recently, various control methods represented by proportional-integral-derivative (PID) control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.     

Adaptive critic motion control design of autonomous wheeled mobile robot by dual heuristic programming

Autonomous wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis or by domain expert to build control rules. This paper presents an adaptive critic motion control design, which enables WMR to autonomously generate the control ability by learning through trials. The design consists of an adaptive critic velocity control loop and a self-learning posture control loop. The neural networks in the velocity neuro-controller (VNC) are corrected with the dual heuristic programming (DHP) adaptive critic method. Designer simply expresses the control objective by specifying the primary utility function then VNC will attempt to fulfill it through incremental optimization. The posture neuro-controller (PNC) learns by approximating the specialized inverse velocity model of WMR so as to map planned positions to suitable velocity commands. Supervised drive supplies variant velocity commands for PNC and VNC to set up their neural weights. During autonomous drive, while PNC halts learning VNC keeps on correcting its neural weights to optimize the control performance. The proposed design is evaluated on an experimental WMR. The results show that the DHP adaptive critic design is a useful base of autonomous control.     

机械臂轨迹跟踪控制--基于EC-RBF神经网络的机械臂模型参考自适应控制

针对机械臂运动轨迹控制中存在的跟踪精度不高的问题,采用了一种基于EC-RBF神经网络的模型参考自适应控制方案对机械臂进行模型辨识与轨迹跟踪控制。该方案采用了两个RBF神经网络,运用EC-RBF学习算法,采用离线与在线相结合的方法来训练神经网络,一个用来实现对机械臂进行模型辨识,一个用来实现对机械臂轨迹跟踪控制。对二自由度机械臂进行仿真,结果表明,使用该控制方案对机械臂进行轨迹跟踪控制具有较高的控制精度,且因采用EC-RBF学习算法使网络具有更快的训练速度,从而使得控制过程较迅速。     

Cooperative control of multiple neural networks for an indoor blimp robot

We report on the cooperative control of multiple neural networks for an indoor blimp robot. In our research group, the indoor blimp robot has been studied to achieve various flying robot applications. The objective of this article is to propose a robust controller that can adapt to mechanical accidents such as the breakdown of propellers. In our proposed method, each propeller thrust is independently calculated by a small neural network. We confirm the advantage of the proposed method against the control by a single large neural network. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot

The purpose of this paper is to propose a hybrid trigonometric compound function neural network (NN) to improve the NN-based tracking control performance of a nonholonomic mobile robot with nonlinear disturbances. In the mobile robot control system, two NN controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the tracking control of the mobile robot. The neuron functions of the hidden layer in the three-layer feedforward network structure consist of the compound cosine function and the compound sine function combining a cosine or a sine function with a unipolar sigmoid function. The main advantages of this NN-based mobile robot control system are better real-time control capability and control accuracy by use of the proposed NN controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances of dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of a nonholonomic mobile robot has better real-time control capability and control accuracy than the compound cosine function NN control system of a nonholonomic mobile robot and then verify the effectiveness of the proposed hybrid trigonometric compound function NN controller for improving the tracking control performance of a nonholonomic mobile robot with nonlinear disturbances.     

Underwater motion characteristics evaluation of multi amphibious spherical robots

Microsystem Technologies - Information exchanges and cooperative movements of multi robots have become a hot topic in robotics. To improve the performance and working efficiency of our amphibious...     

Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence

In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.

     

Control of a nonholonomic mobile robot using neural networks

A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. Online NN weight tuning algorithms do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.     

Decentralized autonomous control of a quadrupedal locomotion robot using oscillators

This article deals with the design of a control system for a quadrupedal locomotion robot. The proposed control system is composed of a leg motion controller and a gait pattern controller within a hierarchical architecture. The leg controller drives actuators at the joints of the legs using a high-gain local feedback control. It receives the command signal from the gait pattern controller. The gait pattern controller, on the other hand, involves nonlinear oscillators. These oscillators interact with each other through signals from the touch sensors located at the tips of the legs. Various gait patterns emerge through the mutual entrainment of these oscillators. As a result, the system walks stably in a wide velocity range by changing its gait patterns and limiting the increase in energy consumption of the actuators. The performance of the proposed control system is verified by numerical simulations. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000