Simulation of football based on PID controller and BP neural network

Wheel robot soccer speed control system using a ball object detection method and PID controller. A control system is based on the object detection system's behavior based on the robot position's orientation to the target position. PIDs are instruments, pressure, speed, and other operational factors used in control, temperature adjustment flow, and industrial control applications. The PID controller uses control loop feedback dynamics to control functional variables and is the most accurate and stable controller. The robot position is held by placing the ball vertically. When the robot's work is perpendicular to the ball, the robot moves with a certain speed controlled by the PIT controller based on the robot's distance and the ball. Standard conditions (standard ball) test results show that the robot can detect the ball material while in the vertical position, whether on the robot's right or left. In the random test that changes direction, the robot can move more dynamically as the ball's change in place.     

基于D-H法的农业采摘机器人运动协作控制系统设计

为提升农业采摘机器人运动协作控制性能,降低机器人碰撞概率,利用D-H法优化设计机器人运动协作控制系统。改装位置、力矩以及碰撞传感器设备,优化运动协作控制器与驱动器,调整系统通信模块结构,完成硬件系统的优化。利用D-H法构建农业采摘机器人数学模型,在该模型下,利用传感器设备实现机器人实时位姿的量化描述,通过机器人采摘流程的模拟,分配机器人运动协作任务,从位置和姿态等多个方面,确定运动协作控制目标,经过受力分析求解机器人实际作用力,最终通过控制量的计算,实现农业采摘机器人的运动协作控制功能。通过系统测试实验得出结论：与传统控制系统相比,机器人位置、姿态角和作用力的控制误差分别降低了约40mm、0.2°和1.2N,在优化设计系统控制下,机器人的碰撞次数得到明显降低。     

Learning to Walk by Imitation in Low-Dimensional Subspaces

In this paper, we provide the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data without any prior information of its dynamics model. Programming a humanoid robot to perform an action (such as walking) that takes into account the robot's complex dynamics is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot's dynamics and environment in order to devise complex (and often brittle) control algorithms for generating a stable dynamic motion. Training using human mocap is an intuitive and flexible approach to programming a robot, but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using high-dimensional mocap data in the humanoid full-body joint space is typically intractable. We propose a new approach to tractable imitation-based learning in humanoids without a robot's dynamic model. We represent kinematic information from human mocap in a low-dimensional subspace and map motor commands in this low-dimensional space to sensory feedback to learn a predictive dynamic model. This model is used within an optimization framework to estimate optimal motor commands that satisfy the initial kinematic constraints as best as possible while generating dynamically stable motion. We demonstrate the viability of our approach by providing examples of dynamically stable walking learned from mocap data using both a simulator and a real humanoid robot.     

Planning and controlling cooperating robots through distributed impedance

This article presents distributed impedance as a new approach for multiple robot system control. In this approach, each cooperating manipulator is controlled by an independent impedance controller. In addition, along selected degrees of freedom, force control is achieved through an external loop, to improve control of the object's internal loading. Extensive stability analysis is performed, based on a realistic model that includes robot impedance and object dynamics. Experiments are performed using two cooperating industrial robots holding an object through point contacts. Force and position control actions are suitably dispatched to achieve both internal loading control and object position control. Individual impedance parameters are specified according to the theoritical stability criterion. The performance of the system is demonstrated for transportation and contact tasks. © 2002 Wiley Periodicals, Inc.     

On the decoupling of position and force controllers in constrained robotic tasks

In hybrid control of robot manipulators separate controllers are designed for force and position errors control. Controllers are designed either in task or joint space and their outputs combine to provide input torque to the manipulator. Position and force controllers performance in a constrained robotic task is affected by their interaction to a degree dependent on the controller's ability to reject disturbances. Ideally, decoupling of the two control loops is desired to achieve the best performance in position and force directions. In this article, analysis of control loop interactions is performed for contact and noncontact phases, and controller design requirements are developed to achieve maximum decoupling. Design requirements involve output subspace of each controller leading to control discontinuities for contact and noncontact phases. In the noncontact phase, satisfaction of design requirements leads to a fully linearized and decoupled system. When in contact with the constraining surface, design requirements eliminate disturbances in the force loop, but minimize disturbances in the position loop to an extent dependent on force loop performance. Known hybrid control schemes analysis is performed to reveal existence of control loop interactions in these schemes. Confirmation of theoretical analysis is done through simulation of a three revolute planar manipulator. © 1998 John Wiley & Sons, Inc.     

Manipulating deformable linear objects: Sensor‐based skills of adjustment motions for vibration reduction

The vibration of a deformable object is often problematic during automatic handling by robot manipulators. However, humans can often handle and damp the vibration of deformable objects with ease. This paper presents force/torque sensor‐based skills for handling deformable linear objects in a manner suitable to reduce acute vibration with simple human skill inspired strategies that consist of one or two adjustment motions. The adjustment motion is a simple open‐loop motion that can be attached to the end of any arbitrary end‐effector's trajectory. As an ordinary industrial robot's simple action, it has three periods, i.e., acceleration, constant speed, and deceleration period; it starts from a predicted time tightly close to a force/moment maximum. The predicted time for the adjustment action is generated automatically on‐line based on the vibration rhythm and the data sensed by a force/torque sensor mounted on the robot's wrist. To find the matching point between the vibrational signal of the deformable object and a template, template matching techniques including cross‐correlation and minimum squared error methods are used and compared. Experiments are conducted with an industrial robot to test the new skills under various conditions. The results demonstrate that an industrial robot could perform effective vibration reduction skills with simple strategies. © 2005 Wiley Periodicals, Inc.     

Position/torque hybrid control of a rigid,high-gear ratio quadruped robot

In this study, we introduce position/torque hybrid control for a newly designed rigid and high-gear ratio quadruped robot. The Experimental results indicated that the use of this control strategy allows the quadruped robot to maintain its stability while walking, and foot contact can be stabilized with only knee torque control and other joints are position controlled, without contact force feedback. Additionally, we suggested a smooth pattern connection method within or from preview control to the center of mass natural dynamics, and vice versa. We validated the proposed control strategies by conducting experiments.     

爆炸箔起爆器参数对电爆性能的影响

为了避免机器人坡面行进姿态与平坦地形直行姿态出现较大偏差,保证多关节机器人运动稳定性,研究基于激光雷达的多关节机器人姿态自动控制方法。结合激光雷达定位导航技术,构建CPG单元振荡器模型,根据运动步态生成原则优化处理足结构参数,完成多关节机器人的运动姿态参数设定。根据姿态参数设定结果实现运动坐标转换,利用动力学方程的简化与分解表达式,确定非线性耦合项参数化处理结果,整合所得变量数据建立反馈控制器连接闭环,利用反馈控制器连接闭环自动控制多关节机器人姿态。对比实验结果表明,在激光雷达技术作用下,机器人上、下坡步长与平坦直行步长之间的误差最大值仅为10%,机器人行进过程中不会出现明显晃动情况,多关节机器人运动稳定性较高。     

基于分层学习的四足机器人运动自适应控制模型

针对四足机器人面对腿部损伤无法继续有效自主运作的问题,提出一种基于分层学习的自适应控制模型。该模型结构由上层状态策略控制器(SDC)和下层基础运动控制器(BDC)组成。SDC对机器人腿部及姿态进行决策并选择运动子策略,BDC子运动策略表达该状态下机器人的运动行为。在Unity3D中构建反关节多自由度的四足机器人,训练多种腿部受损状况的BDC子运动策略,BDC成熟后20s周期随机腿部受损并训练SDC。该模型控制流程为SDC监测机器人状态,激活BDC策略,BDC输出期望关节角度,最后由PD控制器进行速度控制。其实现机器人在腿部受损后自我适应继续保持运作。仿真与实验结果表明,该控制模型能在机器人损伤后能自我快速、稳定调整运动策略,并保证运动的连贯性及柔和性。     

Set point trajectory and internal force control in redundant dual-arm manipulation

A nonlinear decoupling and linearizing feedback control is considered for dynamic coordination of two planar robot arms manipulating an object. A general inverse dynamics-based method is presented that assures an exact feedback linearization for simultaneous control of the object trajectory on the plane and internal efforts transmitted from the robot end-effectors to the object. The method takes the manipulator dynamics and object dynamics into consideration. A method for parameterizing the grip matrix null space is proposed, which has formed a basis for developing a new method for calculating the internal efforts. The procedure is invariant with respect to the change of the torque origin and units of length, and provides the force distribution without internal squeezing effects. A comparison between the approaches known so far and the new method is presented. No previously published method assures noninvariance and nonsqueezing properties for all possible contact configurations. Control algorithms are developed for a system of robotic arms that has more degrees of freedom than necessary for given tasks, exhibiting both actuation and kinematic redundancy. The implementation of this method is demonstrated for the case of a system of two planar three-link arms with the end-effectors manipulating an object, with different constrained task configurations. Practical aspects of discrete-time inverse dynamics control, such as influence of the computational time delay and robustness to model imperfections, are discussed. It is demonstrated that it is possible to achieve high-precision tracking of object position and internal force profiles, even if a system imperfect model is used for controller design. © 1993 John Wiley & Sons, Inc.     

Self-organization of Dynamic Object Features Based on Bidirectional Training

This paper presents a method to self-organize object features that describe object dynamics using bidirectional training. The model is composed of a dynamics learning module and a feature extraction module. Recurrent Neural Network with Parametric Bias (RNNPB) is utilized for the dynamics learning module, learning and self-organizing the sequences of robot and object motions. A hierarchical neural network is linked to the input of RNNPB as the feature extraction module for self-organizing object features that describe the object motions. The two modules are simultaneously trained through bidirectional training using image and motion sequences acquired from the robot's active sensing with objects. Experiments are performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing and rolling motions. The results have shown that the model is capable of self-organizing object dynamics based on the self-organized features.     

Feedforward operational stiffness modulation and external force estimation of planar robots equipped with variable stiffness actuators

A variable stiffness actuator (VSA) is considered a promising mechanism-based approach for realizing compliant robotic manipulators. By changing the stiffness of each joint, the robot can modulate the stiffness of the entire system to enhance safety and efficiency during physical interaction with other systems. This paper presents a feedforward method to modulate the operational stiffness of a parallel planar robot with multiple VSAs. A VSA utilizing a lever mechanism was developed, clearly presenting its mechanical design and kinematic model details. A computational model of joint-restoring torque was developed based on deformation measurements and hysteresis loop geometry to estimate the applied torque of each joint in real-time. An algorithm was proposed to compute the joint stiffness solution using the robot's kinematic model for modulating the operational stiffness of the parallel robot. Experiments were performed to evaluate the proposed method by comparing the performances of two DOF serial and parallel robot systems. The results demonstrated the capability of the VSA in both feedforward stiffness modulation and external force estimation.

     

Cooperative Control of a 3D Dual-Flexible-Arm Robot

This paper discusses cooperative control of a dual-flexible-arm robot to handle a rigid object in three-dimensional space. The proposed control scheme integrates hybrid position/force control and vibration suppression control. To derive the control scheme, kinematics and dynamics of the robot when it forms a closed kinematic chain is discussed. Kinematics is described using workspace force, velocity and position vectors, and hybrid position/force control is extended from that on dual-rigid-arm robots. Dynamics is derived from constraint conditions and the lumped-mass-spring model of the flexible robots and an object. The vibration suppression control is calculated from the deflections of the flexible links and the dynamics. Experiments on cooperative control are performed. The absolute positions/orientations and internal forces/moments are controlled using the robot, each arm of which has two flexible links, seven joints and a force/torque sensor. The results illustrate that the robot handled the rigid object damping links' vibration successfully in three-dimensional space.     

A geometric approach to solving the stable workspace of quadruped bionic robot with hand–foot-integrated function

This paper discusses stable workspace of a hand–foot-integrated quadruped walking robot, which is an important issue for stable operation of the robot. This robot was provided with combined structure of parallel and serial mechanisms, whose stable workspace was the subspace of the workspace in which the system was considered stable. The reachable region was formed under structural conditions, while the stable space was formed by the overall conditions of stability which changed with the robot's pose and the mass of grabbed object. In this paper, based on the robot's main structure, the key issues in solving the robot's workspace are discussed in detail, including searching steady conditions of operation of the robot. To research the robot's workspace, working leg's motion curve needed to be solved by kinematics analysis. Due to the redundant drive, it was problematic to deal analytically with the kinematics of the quadruped walking robot. A geometric method of kinematic analysis was proposed as well. Based on the geometric method, the workspace of the robot under varying postures was analyzed by the method of grid partition and in combination with Matlab, VB and Solidworks software programs. An automated computational system of the stable workspace was developed and an example was given to illustrate the whole process in detail. The theory and analysis procedures were also verified by simulation of the robot and its actual grabbing of an object.     

Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object

In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.     

On-line exploration of an unknown surface by a robotic probe

A control strategy is developed for a robotic probe with tactile sensing to explore an unknown surface without losing contact. Digital computer simulations are performed of a three-link, planar manipulator exploring an ellipsoidal surface in order to test the control strategy. The equations of motion are written and linear, time-varying, state-variable feedback is applied to stabilize and decouple the system. A tactile sensor is simulated to supply the normal force between the robot and the surface. From the magnitude and direction of this force, the desired trajectory is determined. An inverse plant and force feedback are implemented to provide the required input torques to the robot's actuators.     

A novel path planning and control framework for passive resistance therapy with a robot manipulator

In this article, we present a paradigm for safe path generation and control for a robotic manipulator such that it provides programmable passive resistance therapy to patients with deficits in the upper extremities. When the patient applies an interaction force at the robot's end-effector, a dynamic path generator time parameterises any therapist-specified contour in the robot's workspace–thus, the robot mimics the dynamics of a passive impedance whose anisotropy vector can be continuously reconfigured. The proposed algorithm is easily implementable because it is robust to uncertainty in the robot dynamics. Moreover, the proposed strategy also guarantees user safety by maintaining the net flow of energy during the human robot interaction from the user towards the manipulator.     

Direct adaptive impedance control of robot manipulators

This article presents an adaptive scheme for controlling the end-effector impedance of robot manipulators. The proposed control system consists of three subsystems: a simple “filter” that characterizes the desired dynamic relationship between the end-effector position error and the end-effector/environment contact force, an adaptive controller that produces the Cartesian-space control input required to provide this desired dynamic relationship, and an algorithm for mapping the Cartesian-space control input to a physically realizable joint-space control torque. The controller does not require knowledge of either the structure or the parameter values of the robot dynamics and is implemented without calculation of the robot inverse kinematic transformation. As a result, the scheme represents a general and computationally efficient approach to controlling the impedance of both nonredundant and redundant manipulators. Furthermore, the method can be applied directly to trajectory tracking in free-space motion by removing the impedance filter. Computer simulation results are given for a planar four degree-of-freedom redundant robot under adaptive impedance control. These results demonstrate that accurate end-effector impedance control and effective redundancy utilization can be achieved simultaneously by using the proposed controller.     

一种通过模糊自调整应用于机械手广义力/位置控制的稳定系统研究

提出一种基于模糊自调整的机械手控制结构,并针对机械手与外界环境接触时产生的作用力,定义了一种广义力,它是机械手执行机构输出力与机械手末端受到外界力的合力。那么,就可以用类似于机械手位置控制的方法达到力/位置控制目的,通过模糊自调整方法实现。在机械手受到的外力是有界限的前提下,考虑机械手非线性、耦合和多变量的动态特征,证明了整个闭环系统是全局稳定的。     

Distributed object transportation on a desired path based on Constrain and Move strategy

In this paper, a distributed strategy to move objects on different arbitrary paths in a 2D plane is proposed and analyzed. This algorithm which is based on Constrain and Move strategy [M.N. Ahmadabadi, E. Nakano, A Constrain and Move approach to distributed object manipulation, IEEE Trans. Robotics Automation 17 (2) (2001) 157], organizes the robots in two groups. The object manipulation task also is decomposed to two different tasks. The task given to one group is control of linear velocity and that assigned to the other group is control of angular velocity of the object. The independence of these tasks makes the design of the distributed architecture of the team possible. To calculate each robot's desired velocity, a simple method using Constrain and Move strategy and robot's local sensors is developed. To prevent small errors in the robot sensory system from affecting the system performance, limited compliance is assumed in robot arms. The basic behaviors of the robots are presented. Moreover, simulation results are given to verify the proposed strategy.