Data Communications for Internet Robots

For Internet robots, the most challenging and distinct difficulties are well recognized to be associated with Internet transmission delays, delay jitter and not-guaranteed bandwidth, which might degrade system performance dramatically or even lead to instability. Current approaches to data communication between the remote robot and the human operator employ directly one of the two currently available protocols, i.e. TCP and UPD. In this paper, a teleoperation-oriented data transmission mechanism is implemented. Compared to TCP, the presented scheme provides minimized transmission delays and delay jitter; in the steady state, its transmission rate is much smoother; when available network bandwidth changes, it adapts to the variation quickly without large overshoot. Compared to UDP, it is inter-protocol fairness convergent, intro-protocol convergent, and efficient convergent. The presented mechanism is deployed in a mobile robot teleoperation system developed. In the experiments, users successfully guided a Pioneer-2 mobile robot through a laboratory environment remotely over the Internet via a web browser.     

双机器人协调作业下碰撞算法及仿真研究

本文立足于关节水平，从双机器人碰撞研究入手，提出了机器人协同作业下碰撞检测 、干涉空间的算法，解决了机器人与周边环境任意障碍物碰撞问题．建立了连杆与连杆，连 杆与平面，连杆与任意三维实体避障理论模型，给出了相应的计算方法及计算机仿真结果．     

一种基于概率方法的车型机器人路径规划方法

考虑车体外形和前轮方向角限制,车型移动机器人路径的空间维数明显增加,常规的规划方法难以获得理想的速度。论文报告了概率方法在车型机器人路径规划上的研究结果,共研究了机器人约束、全局、局部概率路径规划和启发式结点生成等问题。模拟表明,该方法可获得足够好的适合车型机器人移动的次优路径。     

基于专家系统的多移动机器人模糊自校正协调系统

研究多移动机器人的运动规划问题，在实时运动规划专家系统的基础上提出了一种串级模糊控制器，以校正实际工作环境下各机器人的运动状态与理想情况下可能产生的误差，使各机器人正确调整各自运动状态，达到协调工作的目的。     

博士后简介

博士后简介陈树强陈树强男，１９６０年出生于福建省福州市．１９８２年毕业于厦门大学物理系，获理学学士学位；１９８２～１９８４年在化工部晨光化工研究院从事塑料测试技术研究；１９８７年在西北工业大学获工学硕士学位；１９８７～１９８９年在南昌航空工业学院任教...     

孔柱型装配机器人的关键技术探讨

本文针对孔柱型装配机器人现存的问题，研究孔柱型机器人装配技术，探讨其内在规律，研究出一套提高装配效率，具有广泛适应能力的装配技术的设计理论和方法．运用本方法可降低对机器人的重复定位精度的要求，使机器人装配技术在高质量、小批量、多品种的产品的装配中得到广泛应用．     

Training a Vision Guided Mobile Robot

This paper presents the design, implementation and evaluation of a trainable vision guided mobile robot. The robot, CORGI, has a CCD camera as its only sensor which it is trained to use for a variety of tasks. The techniques used for training and the choice of natural light vision as the primary sensor makes the methodology immediately applicable to tasks such as trash collection or fruit picking. For example, the robot is readily trained to perform a ball finding task which involves avoiding obstacles and aligning with tennis balls. The robot is able to move at speeds up to 0.8 ms^-1 while performing this task, and has never had a collision in the trained environment. It can process video and update the actuators at 11 Hz using a single $20 microprocessor to perform all computation. Further results are shown to evaluate the system for generalization across unseen domains, fault tolerance and dynamic environments.     

Contact Friction Compensation for Robots Using Genetic Learning Algorithms

In this paper, the issues of contact friction compensation for constrained robots are presented. The proposed design consists of two loops. The inner loop is for the inverse dynamics control which linearizes the system by canceling nonlinear dynamics, while the outer loop is for friction compensation. Although various models of friction have been proposed in many engineering applications, frictional force can be modeled by the Coulomb friction plus the viscous force. Based on such a model, an on-line genetic algorithm is proposed to learn the friction coefficients for friction model. The friction compensation control input is also implemented in terms of the friction coefficients to cancel the effect of unknown friction. By the guidance of the fitness function, the genetic learning algorithm searches for the best-fit value in a way like the natural surviving laws. Simulation results demonstrate that the proposed on-line genetic algorithm can achieve good friction compensation even under the conditions of measurement noise and system uncertainty. Moreover, the proposed control scheme is also found to be feasible for friction compensation of friction model with Stribeck effect and position-dependent friction model.     

An Autonomous Spacecraft Agent Prototype

This paper describes the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. The architecture supports challenging requirements of the autonomous spacecraft domain not usually addressed in mobile robot architectures, including highly reliable autonomous operations over extended time periods in the presence of tight resource constraints, hard deadlines, limited observability, and concurrent activity. A hybrid architecture, NMRA integrates traditional real-time monitoring and control with heterogeneous components for constraint-based planning and scheduling, robust multi-threaded execution, and model-based diagnosis and reconfiguration. Novel features of this integrated architecture include support for robust closed-loop generation and execution of concurrent temporal plans and a hybrid procedural/deductive executive.We implemented a prototype autonomous spacecraft agent within the architecture and successfully demonstrated the prototype in the context of a challenging autonomous mission scenario on a simulated spacecraft. As a result of this success, the integrated architecture has been selected to fly as an autonomy experiment on Deep Space One (DS-1), the first flight of NASA';s New Millennium Program (NMP), which will launch in 1998. It will be the first AI system to autonomously control an actual spacecraft.