集控式足球机器人系统关键技术研究

足球机器人比赛是机器人研究的一个新热点,它为人工智能理论和算法的研究提供了一个实验平台,其研究的领域涵盖了人工智能、自动控制、机器人视觉、无线通信、机器学习和多智能体合作与协调等。集控式足球机器人系统通常可以划分为4个子系统,即视觉、决策、通信和车型机器人。结合研究经验,介绍了集控式足球机器人各个子系统的关键技术。     

基于超声影像导航的肝癌消融机器人系统的误差传递

建立了基于超声影像导航的肝癌消融机器人系统误差传递模型．该系统主要由超声影像设备、导航 软件子系统、定位装置和穿刺机器人四个模块组成．机器人系统首先通过三维超声重建、术前模型和术中实体的 配准以及定位装置和机器人之间的坐标转换将肿瘤的目标靶点转化到机器人坐标系中,然后再控制机器人运动到 指定的靶点位置进行治疗．首先分析上述流程,指出误差源．然后,利用齐次变换矩阵的微分矩阵建立靶点映射 误差传递模型,并通过仿真实验验证了靶点误差模型的正确性．最后,对系统进行了精度测试实验,实验结果表 明该系统的总体误差小于5 mm,满足消融治疗肝癌的需求,能有效地提高肝癌的治疗效果．     

冗余度机器人运动模型的神经网络辨识及实现

为提高网络学习速度,提出了一种新的动态神经网络结构——状态延迟输入动态递归神经网络.以德国PowerCube^TM模块化机器人为研究对象,将机器人系统返回的关节位置信息和OPTOTRAK30203维运动测量系统测得的机器人末端位置信息作为神经网络的学习样本,对包含各种影响因素的机器人运动模型进行了辨识,所得结果及误差分析,说明了SDIDRNN在学习能力上的优越性.     

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster–Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robot’s trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.     

一个先进的工业机器人计算机控制系统—ARCS

随着工业机器人及其应用的不断发展,要求一个强有力的计算机系统来控制它的工作,并具有灵活、方便的机器人编程语言.本文系统地介绍了我们自行设计并实现的一个先进的机器人控制系统——ARCS.该系统主要包括两部分:(1)一个实时多任务的机器人控制软件SVAL系统,该系统支持一种通用性较强的机器人编程语言——SVAL语言.(2)一个支持该软件系统工作的、具有开放式结构的硬件环境.ARCS系统具有良好的实时性、可扩展性及基于外部传感器信号进行控制的能力.由于该系统的开放式结构.使其根据不同要求可方便地增删其功能,并可控制不同类型的机器人.我们已成功地实现对PUMA760机器人的控制,并在其上引入了力觉与接近觉的传感器,采样时间可缩短到16ms.一年多的运行结果证明,该系统稳定可靠,性能良好,现在正向产品转化.     

Learning sensor-based navigation of a real mobile robot in unknownworlds

In this paper, we address the problem of navigating an autonomous mobile robot in an unknown indoor environment. The parti-game multiresolution learning approach is applied for simultaneous and cooperative construction of a world model, and learning to navigate through an obstacle-free path from a starting position to a known goal region. The paper introduces a new approach, based on the application of the fuzzy ART neural architecture, for on-line map building from actual sensor data. This method is then integrated, as a complement, on the parti-game world model, allowing the system to make a more efficient use of collected sensor information. Then, a predictive on-line trajectory filtering method, is introduced in the learning approach. Instead of having a mechanical device moving to search the world, the idea is to have the system analyzing trajectories in a predictive mode, by taking advantage of the improved world model. The real robot will only move to try trajectories that have been predicted to be successful, allowing lower exploration costs. This results in an overall improved new method for goal-oriented navigation. It is assumed that the robot knows its own current world location-a simple dead-reckoning method is used for localization in our experiments. It is also assumed that the robot is able to perform sensor-based obstacle detection (not avoidance) and straight-line motions. Results of experiments with a real Nomad 200 mobile robot are presented, demonstrating the effectiveness of the discussed methods.     

Development of CAM system based on industrial robotic servo controller without using robot language

This paper describes the development of a robotic CAM system for an articulated industrial robot RV1A from the view point of robotic servo controller. It is defined here that the CAM system includes an important function which allows an industrial robot to move along cutter location data (CL data) consisting of position and orientation components. In addition, the developed CAM system has a high applicability to other industrial robots whose servo systems are technically opened to end-users. The developed robotic CAM system works as a straightforward interface between a general CAD/CAM and an industrial robot. At the present stage, the relationship between CAD/CAM and industrial robots is not well established compared to NC machine tools that are widely spread in manufacturing industries. The CAM systems for NC machine tools are already established, however, the CAM system for industrial robots has not been sufficiently considered and developed yet. A teaching pendant is generally used to obtain position and orientation data of the arm tip before an industrial robot works. Here, in order to enhance the relationship between a conventional CAD/CAM system and an industrial robot, a simple and straightforward CAM system without using any robot language is developed and implemented. The basic design of the robotic CAM system and the experimental results are presented in this paper.     

受限机器人神经形态的变结构位置/力控制

本文基于Ｊｅａｎ和Ｆｕ（１９９３）建立的受限机器人模型的降型阶形式，利用变结构系统理论，设计了具有未知动态的受限机器人轨道／力追踪控制，提出的学习方法仅仅利用了机器人动态模型的一般结构，不需要其精确信息，计算迅速，易于实现，仿真结果验证了提出的方法的有效性。     

Emergent synthesis of motion patterns for locomotion robots

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance-based reinforcement-learning scheme, is used for the sensory-motor control of an eight-legged mobile robot and for the synthesis of the robot gaits. The robot does not have a priori knowledge of the environment and its own internal model. It is only assumed that the robot can acquire stable gaits by learning how to reach a goal area. During the learning process the control system is self-organized by reinforcement signals. Reaching the goal area defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. As learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns. Feasibility of the proposed self-organized system is tested under simulation and experiment. A minimal simulation model that does not require sophisticated computational schemes is constructed and used in simulations. The simulation data, evolved on the minimal model of the robot, is downloaded to the control system of the real robot. Overall, of 10 simulation data seven are successful in running the real robot.     

基于力反馈的脊柱外科机器人系统的设计与实现

本文针对脊柱椎管狭窄症减压手术中椎管壁磨削不安全这一问题,介绍一种基于力反馈控制策略的脊柱外科机器人系统,包括监控磨削过程的检测子系统、完成磨削手术操作的运动驱动子系统和再现磨削信息状况的控制显示子系统。利用脊柱磨削手术过程中磨削力的变化特点,提出基于力反馈的脊柱外科机器人控制策略,辅助医生实现安全的脊柱手术操作。最后通过仿真实验和模拟骨实验,验证了此基于力反馈控制策略的脊柱外科机器人系统的可行性。     

Control of multi-axis robots with elastic joints via cascade compensation using the method of exact linearization

This paper presents a new method for the position control of industrial robots with elastic joints and where the dynamic of each actuator is described by a simplified model. The inverse dynamic of the system is computed and used to compensate the nonlinear terms and decouple the system through a coordinate transformation and nonlinear feedback (exact linearization). To simplify the algorithm for the inverse dynamic and hence reduce its computation, each actuator-link pair of the robot is considered as a 2-input 2-output nonlinear system, a link subsystem and an actuator subsystem. A cascade compensation using the exact linearization is then applied to each subsystem, thereby avoiding the computation of the first and second partial derivatives of the inverse of the inertia matrix and the vector of the coriolis and centrifugal forces. This gives a formalism that is relatively simple and efficient for symbolical computation, which is very important for the maintenance of accuracy. Similarly, a cascade linear controller is constructed for each subsystem of the resulting linear decoupled 2-input 2-output system. The basis vector functions for the coordinate transformation are so chosen that only one state of the link subsystem can theoretically not be measured directly or indirectly. To estimate this state, an observer with linear error dynamic is constructed. The applicability of this observer to this general case is also proved. Simulation results using the first three links of Puma 560 are finally presented.     

Multisensor 3D Posture Determination of a Mobile Robot Using Inertial and Ultrasonic Sensors

This paper presents methodologies and techniques for fusing inertial and ultrasonic sensors to estimate the current posture of a mobile robot navigating over indoor uneven terrain. This new type of pose tracking system is developed by means of fusing an inertial navigation subsystem (INS) and an ultrasonic localization subsystem. Extended Kalman filtering (EKF)-based algorithm for integrating both the subsystems is proposed to obtain reliable attitude and position estimates of the vehicle and to eliminate the accumulation errors caused by wheel slippage and surface roughness. Experimental results are conducted to illustrate feasibility and effectiveness of the proposed system and method.     

基于神经网络的进化机器人组合行为方法研究

为了克服传统机器人设计方法存在的局限性,提高机器人的自适应能力,采用神经网络方法实现了进化机器人避碰、趋近及其组合行为学习,首先,提出了新的机器人模拟环境和机器人模型,结合了采用神经网络实现进化学习系统的方法。其次,对具有进化学习机制的机器人基本行为和组合行为学习系统进行了仿真,并通过仿真证明了新模型不要求环境知识的完备性,机器人具有环境自适应学习能力,还具有结构简洁、易扩展等特点,最后,对仿真结果进行分析与讨论,并提出了进一步研究方向。     

Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot

The control of a robot system using camera information is a challenging task regarding unpredictable conditions, such as feature point mismatch and changing scene illumination. This paper presents a solution for the visual control of a nonholonomic mobile robot in demanding real world circumstances based on machine learning techniques. A novel intelligent approach for mobile robots using neural networks (NNs), learning from demonstration (LfD) framework, and epipolar geometry between two views is proposed and evaluated in a series of experiments. A direct mapping from the image space to the actuator command is conducted using two phases. In an offline phase, NN–LfD approach is employed in order to relate the feature position in the image plane with the angular velocity for lateral motion correction. An online phase refers to a switching vision based scheme between the epipole based linear velocity controller and NN–LfD based angular velocity controller, which selection depends on the feature distance from the pre-defined interest area in the image. In total, 18 architectures and 6 learning algorithms are tested in order to find optimal solution for robot control. The best training outcomes for each learning algorithms are then employed in real time so as to discover optimal NN configuration for robot orientation correction. Experiments conducted on a nonholonomic mobile robot in a structured indoor environment confirm an excellent performance with respect to the system robustness and positioning accuracy in the desired location.     

Reactive navigation of underwater mobile robot using ANFIS approach in a manifold manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot’s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system (ANFIS) has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace. An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.     

基于ADAMS和Vega的地面机动武器仿真系统的研究

结合数值仿真和视景仿真技术,建立了地面机动武器系统的分布式交互仿真平台,该集成仿真系统具备多种仿真功能,能够用于新型武器装备的研制和大规模军事演练。按照该仿真系统结构,实现了某地面移动机器人的仿真系统。其动力学仿真子系统用ADAMS的履带工具箱（ATV）建立了该机器人的动力学模型。并对起步工况、最高直驶速度和转向特性进行了动力学仿真分析,得到该数字系统较为完整的虚拟实验数据,并通过交互接口将仿真数据用于视景驱动。视景仿真子系统采用MultiGen Creator／Vega实现,分析了视景模型的数据组织结构和简化处理,基于Vega编制了视景仿真程序。     

Maintaining a common co-ordinate system for a group of robots based on vision

This work presents a novel approach to the problem of establishing and maintaining a common co-ordinate system for a group of robots. A camera system mounted on top of a robot and vision algorithms are used to calculate the relative position of each surrounding robot. The watched movement of each robot is compared to the reported movement which is sent over some communication link. From this comparison a co-ordinate transformation is calculated. The algorithm was tested in simulation and is at the moment being implemented on a real robot system. Preliminary results of real world experiments are being presented.     

A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation.

Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.