Identification of a golf swing robot using soft computing approach

Golf swing robots have been recently developed in an attempt to simulate the ultra high-speed swing motions of golfers. Accurate identification of a golf swing robot is an important and challenging research topic, which has been regarded as a fundamental basis in the motion analysis and control of the robots. But there have been few studies conducted on the golf swing robot identification, and comparative analyses using different kinds of soft computing methodologies have not been found in the literature. This paper investigates the identification of a golf swing robot based on four kinds of soft computing methods, including feedforward neural networks (FFNN), dynamic recurrent neural networks (DRNN), fuzzy neural networks (FNN) and dynamic recurrent fuzzy neural networks (DRFNN). The performance comparison is evaluated based on three sets of swing trajectory data with different boundary conditions. The sensitivity of the results to the changes in system structure and learning rate is also investigated. The results suggest that both FNN and DRFNN can be used as a soft computing method to identify a golf robot more accurately than FFNN and DRNN, which can be used in the motion control of the robot.     

未知动态环境下非完整移动群机器人围捕

针对未知动态障碍物环境下非完整移动群机器人围捕,提出了一种基于简化虚拟受力模型的自组织方法.首先给出了个体机器人的运动方程,然后给出了未知动态环境下目标和动态障碍物的运动模型.通过对复杂环境下围捕行为的分解,抽象出简化虚拟受力模型,基于此受力模型,设计了个体运动控制方法,接着证明了系统的稳定性并给出了参数设置范围.不同情况下的仿真结果表明,本文给出的围捕方法可以使群机器人在未知动态障碍物环境下保持较好的围捕队形,并具有良好的避障性能和灵活性.最后分析了本文与基于松散偏好规则的围捕方法相比的优势.     

INTELLIGENT MOTION CONTROL FOR OMNIDIRECTIONAL MOBILE ROBOTS USING ANT COLONY OPTIMIZATION

This article presents an intelligent system-on-a-programmable-chip-based (SoPC) ant colony optimization (ACO) motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array (FPGA) chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm (GA) optimal controllers.     

Motion planning for cooperating mobile manipulators

We address the problem of motion planning for nonholonomic cooperating mobile robots manipulating and transporting objects while holding them in a stable grasp. We present a general approach for solving optimal control problems based on the calculus of variations. We specialize this approach to solving the motion planning problem and obtaining trajectories and actuator forces/torques for any maneuver in the presence of obstacles. The approach allows geometric constraints such as joint limits, kinematic constraints such as nonholonomic velocity constraints, and dynamic constraints such as frictional constraints and contact force constraints to be incorporated into the planning scheme. The application of this method is illustrated by computing motion plans for several examples, and these motions plans are implemented on an experimental testbed. ©1999 John Wiley & Sons, Inc.     

移动机器人运动规划中的深度强化学习方法

随着移动机器人作业环境复杂度的提高、随机性的增强、信息量的减少,移动机器人的运动规划能力受到了严峻的挑战.研究移动机器人高效自主的运动规划理论与方法,使其在长期任务中始终保持良好的复杂环境适应能力,对保障工作安全和提升任务效率具有重要意义.对此,从移动机器人运动规划典型应用出发,重点综述了更加适应于机器人动态复杂环境的运动规划方法——深度强化学习方法.分别从基于价值、基于策略和基于行动者-评论家三类强化学习运动规划方法入手,深入分析深度强化学习规划方法的特点和实际应用场景,对比了它们的优势和不足.进而对此类算法的改进和优化方向进行分类归纳,提出了目前深度强化学习运动规划方法所面临的挑战和亟待解决的问题,并展望了未来的发展方向,为机器人智能化的发展提供参考.     

Design of robust knowledge bases of fuzzy controllers for intelligent control of substantially nonlinear dynamic systems: II. A soft computing optimizer and robustness of intelligent control systems

The structure of intelligent control system (ICS) is analyzed, and the interrelations with conventional problems of the theory and practice of application of control systems are described. The analysis of the results of simulation of typical structures of intelligent control systems has allowed us to establish the following fact. The application of the technique of designing (presented in Part I), which is based on a fuzzy neural network (FNN), does not guarantee in general that the required accuracy of approximation of the training signal (TS) will be reached. As a result, under an essential change of external conditions, the sensitivity level of the controlled plant (CP) increases, which, on the whole, leads to a decrease in the robustness of the intelligent control system, and, as a consequence, to a loss of reliability (accuracy) of achieving the control goal. To eliminate the specified drawback of the neural network, a soft computing optimizer (SCO), which uses the technique of soft computing and allows one to eliminate the drawback, is applied, which results in an increase in the robustness level of the structure of the intelligent control system. The structure of the soft computing optimizer, which contains as a particular case the required configuration of an optimal fuzzy neural network, is considered. The main specific features of the functional operation of the soft computing optimizer and the stages of the process of designing robust knowledge bases (KB) of fuzzy controllers (FC) are described. The methodology of joint stochastic and fuzzy simulation of automatic control system based on the developed tool of the soft computing optimizer is discussed in order to test the robustness and to estimate the limiting structural capabilities of intelligent control systems. The efficiency of the control processes with application of the soft computing optimizer is demonstrated by particular typical examples (benchmarks) of models of dynamic controlled plants under the conditions of incomplete information about the parameters of the structure of the controlled plant and under the presence of unpredicted (abnormal) control situations. Examples of industrial application of robust intelligent control systems in actual control systems designed based on the soft computing optimizer are presented. Practical recommendations for improving the robustness level of intelligent control systems by using new types of computations and simulation are given     

多移动机器人实时最优运动规划

研究多移动机器人的实时运动规划问题，提出了运动规划问题的体系结构，并将最优控制与智能决策相结合，建立实时专家系统，在其支持下，使机器人在时间—能量最优情况下完成规划策略。仿真结果表明该方法具有很强的实时性。     

一种新的位置数据融合方法的研究

给出了一新的基于团队一致法的多传感器位置数据融合方法，该方法按传感器队中的每个成员的测量不确定性，构造团队期望效用函数（或密度），并基于该期望效用函数求得位置参数估计，其优点是可消除失效传感器和测量值为野值的传感器的影响，本文给出了仿真结果。     

分布式多机器人运动控制的离散事件系统方法

传统多机器人系统的运动控制主要依赖于机器人的动力学方程或运动学方程,通过求解微分方程组来获得机器人的输入控制信号.随着系统中机器人数量的增加和运行环境的复杂化,动力学方程很难描述多机器人系统的运动行为,且无法很好地解决诸如死锁等逻辑故障.本文简略综述了国内外的研究现状,重点介绍笔者所在研究组开展的关于离散事件系统方法在多机器人运动控制方面的应用性研究工作.其动机在于:1)基于离散事件系统方法的运动控制能够有效地解决系统运行过程中产生的诸如死锁等逻辑故障.首先,利用离散事件系统模型对多机器人系统的运动进行建模,从而降低计算复杂性;其次,基于所得离散事件系统模型,设计分布式安全运动控制算法,使各个机器人可以自主地、无碰撞地、无死锁地运动;设计分布式鲁棒运动控制算法,使得失效的机器人对系统的影响最小.2)基于离散事件系统方法的运动控制策略可以结合传统的基于运动学方程的运动控制方法,从而使系统不但能够避免顶层的逻辑故障,而且能够确定机器人执行器的输入信号.     

Motion coordination and reactive control of autonomous multi-manipulator systems

The emerging field of service robots demands new systems with increased flexibility. The flexibility of a robot system can be increased in many different ways. Mobile manipulation—the coordinated use of manipulation capabilities and mobility—is an approach to increase robots flexibility with regard to their motion capabilities. Most mobile manipulators that are currently under development use a single arm on a mobile platform. The use of a two-arm manipulator system allows increased manipulation capabilities, especially when large, heavy, or non-rigid objects must be manipulated. This article is concerned with motion control for mobile two-arm systems. These systems require new schemes for motion coordination and control. A coordination scheme called transparent coordination is presented that allows for an arbitrary number of manipulators on a mobile platform. Furthermore, a reactive control scheme is proposed to enable the platform to support sensor-guided manipulator motion. Finally, this article introduces a collision avoidance scheme for mobile two-arm robots. This scheme surveys the vehicle motion to avoid platform collisions and arm collisions caused by self-motion of the robot. © 1996 John Wiley & Sons, Inc.     

A novel soft computing architecture for the control of autonomous walking robots

An integration of concepts from neurobiology, applied psychology, insect physiology and behaviour based robotics has led us to propose a novel generic systems architecture for the intelligent control of mobile robots and in particular, autonomous walking machines. (We define what we mean by “autonomy”.) The control architecture is hierarchical and will be described from a top-down perspective. Level one consists of interpreting a motivation and translating this into high-level commands. Once a high-level command is generated, a range of internal representations or “cognitive maps” may be employed at level two to help provide body-centred motion. At level three of the hierarchy kinematic planning is performed. The fourth level – dynamic compensation – requires feedback from the actuators and compensates for errors in the target vectors provided by the kinematic level and caused by systematic dynamic uncertainties or environmental disturbances. This is implemented using adaptive neural controllers. The interfaces will be described and results from simulation and implementation of levels 2–4 on a hexapod robot will be presented. The hierarchy employs the following soft computing techniques: evolution strategies, cognitive maps, adaptive heuristic critics, temporal difference learning and adaptive neural control using linear-equivalent neural networks.     

Design and implementation of a novel hybrid quadruped spherical mobile robot

Spherical mobile robots are a novel type of mobile robots having some advantages in motion over other ordinary mobile robots. The advantages can be related to their symmetric spherical shape. Despite many works being conducted in recent years on spherical mobile robots, it seems that finding the best driving mechanism with higher efficiency still needs much research. In this article, a novel type of spherical mobile robot is introduced. This robot has a hybrid structure of the spherical robots and ordinary four legged or quadruped robots. Adding legs to the spherical robot reduces some disadvantages of its behavior. After introduction of the mentioned robot, its dynamic model based on Lagrange equations is obtained. The accuracy of the developed dynamic model in tracking a trajectory is verified through a dynamic simulation. Experimental results in tracking a square trajectory is presented to show the verification.     

复杂环境下群机器人自组织协同多目标围捕

针对动态多目标围捕,提出了一种复杂环境下协同自组织多目标围捕方法.首先设计了多目标在复杂环境下的运动模型,然后通过对生物群体围捕行为的研究,构建了多目标简化虚拟受力模型.基于此受力模型和提出的动态多目标自组织任务分配算法,提出了群机器人协同自组织动态多目标围捕算法,这两个算法只需多目标和个体两最近邻位置信息以及个体面向多目标中心方向的两最近邻任务信息,计算简单高效,易于实现.接着获得了系统稳定时参数的设置范围.由仿真可知,所提的方法具有较好的灵活性、可扩展性和鲁棒性.最后给出了所提方法相较于其它方法的优势.     

Small robot agents with on-board vision and local intelligence

We designed a family of completely autonomous mobile robots with local intelligence. We developed a controller with a variety of digital and analog I/O facilities and the operating system RoBIOS, which allows maximum flexibility. The robots have a number of on-board sensors, including vision, and do not rely on global sensor systems. The on-board computing power is sufficient to analyze several color images per second. This enables the robots to perform several different task such as navigation, map generation or intelligent group behavior and does not limit them to the game of robot soccer.     

基于Fuzzy-PID的移动机器人运动控制

移动机器人涉及到许多研究方向,运动控制是其中的基础。通过对移动机器人运动学模型进行分析,以足球机器人系统为实验平台,论证了Fuzzy-PID技术应用于移动机器人运动控制的可行性。将传统的PID控制与模糊控制相结合,通过PID控制实现控制的准确性,利用模糊控制提高控制的快速性。针对移动机器人运动控制中的实际问题,着重提出了基于误差分区的PID控制器和模糊控制器的设计方法。实验证明该方法不仅增强了控制器的调节能力,还在一定程度上简化了控制器的设计。     

基于认知模型的室内移动服务机器人人机耦合协同作业机制

针对老年人和残疾人这类特殊用户群体与服务机器人构成的人机智能系统,提出了基于ACT-R(理性思维的适应性控制)认知架构模型的室内移动服务机器人人机耦合协同作业机制.基于ACT-R认知架构对人机一体化室内移动服务机器人人机协同作业系统进行了总体设计,利用简单自然的人机效应通道,设计了基于ACT-R认知架构的人机耦合界面;通过人-机-环境空间感知耦合,提出并建立了室内移动服务机器人人机一体化协同决策作业机制.最后在室内环境下进行移动服务机器人人机协同作业实验,系统安全高效地完成了作业任务,验证了该机制的有效性.     

Motion control algorithms and simulation of the dynamics of multilink robots moving based on the principles of a traveling wave

Multilink robots that simulate the motion of snakes and worms are considered. The motion is a result of the interaction with a supporting surface when the configuration of the robot is changed. Supervisor control algorithms are proposed for which a user (an operator) controls the motion velocity and direction, while the robot configuration that implements the desired motion is changed automatically. Controlled motions of the considered robots were simulated based on their dynamic models. The motions that simulated movements of snakes and worms are compared. Robots of this type can be used as mobile robots.