仿生机器鱼运动学参数优化选择的试验方法研究

建立了仿生机器鱼在一维稳态游动时的运动学数学模型；运用遗传算法对该模型中的运动学参数如何进行优化选择和组合进行了试验研究，并提出了试验的模型和方法．该模型对于开展包括鱼雷在内的无人水下航行体的推进技术研究有较大的参考意义．     

A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations

This paper presents a distributed smooth time-varying feedback control law for coordinating motions of multiple nonholonomic mobile robots of the Hilare-type to capture/enclose a target by making troop formations. This motion coordination is a cooperative behavior for security against invaders in surveillance areas. Each robot in this control law has its own coordinate system and it senses a target/invader, other robots and obstacles, to achieve this cooperative behavior without making any collision. Each robot especially has a two-dimensional control input referred to as a “formation vector” and the formation is controllable by the vectors. The validity of this control law is supported by computer simulations.     

Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process

This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.     

多机器人任意队形分布式控制研究

本文针对多智能体协作完成特定任务时难以在全自主控制的前提下协作形成任意队 形和队形向量不易确定的问题，通过由各智能体自主简单的确定自己的队形向量，从理论上 扩展基于队形向量的队形控制原理以生成任意队形，改进机器人的运动方式以提高收敛速度 ，提出一种快速收敛的机器人部队任意队形分布式控制算法．为了解决智能体机器人之间的 冲突问题，提出了一个通信协调模型．仿真实验和实际机器人实验均表明了算法的可行性和 有效性．     

网络传感器在机器人感知系统中的应用研究

本文针对网络环境下机器人感知系统信息获取技术，结合IEEE1451.2网络传感器设计 思想，提出一种即插即用机器人感知系统节点的设计方法，并针对具体感知节点提出一种在 线编程的方法，即可在线修改感知系统节点中具体的传感器／执行器的有关参数，如：生产 厂家、日期、校准参数等；并给出了网络环境下机器人感知系统的网络拓扑结构以及数字接 口．     

Sensor Fusion and Planning with Perception–Action Network

Robot intelligence requires a real-time connection between sensing and action. A new computation principle of robotics that efficiently implements such a connection is utmost important for the new generation of robotics. In this paper, a perception–action network is presented as a means of efficiently integrating sensing, knowledge, and action for sensor fusion and planning. The network consists of a number of heterogeneous computational units, representing feature transformation and decision-making for action, which are interconnected as a dynamic system. New input stimuli to the network invoke the evolution of network states to a new equilibrium, through which a real-time integration of sensing, knowledge, and action can be accomplished. The network provides a formal, yet general and efficient, method of achieving sensor fusion and planning. This is because the uncertainties of signals, propagated in the network, can be controlled by modifying sensing parameters and robot actions. Algorithms for sensor planning based on the proposed network are established and applied to robot self-localization. Simulation and experimental results are shown.     

柔性机器人的动力学建模及其控制*

本文首先综述了近年来在柔性机器人动力学建模方面所取得的进展，着重介绍了目前较常见的几种建模方法和模型，同时对柔性机器人的控制问题进行了评述，指出了今后研究的方向。     

Interleaving Planning and Robot Execution for Asynchronous User Requests

ROGUE is an architecture built on a real robot which provides algorithms for the integration of high-level planning, low-level robotic execution, and learning. ROGUE addresses successfully several of the challenges of a dynamic office gopher environment. This article presents the techniques for the integration of planning and execution.ROGUE uses and extends a classical planning algorithm to create plans for multiple interacting goals introduced by asynchronous user requests. ROGUE translates the planner';s actions to robot execution actions and monitors real world execution. ROGUE is currently implemented using the PRODIGY4.0 planner and the Xavier robot. This article describes how plans are created for multiple asynchronous goals, and how task priority and compatibility information are used to achieve appropriate efficient execution. We describe how ROGUE communicates with the planner and the robot to interleave planning with execution so that the planner can replan for failed actions, identify the actual outcome of an action with multiple possible outcomes, and take opportunities from changes in the environment.ROGUE represents a successful integration of a classical artificial intelligence planner with a real mobile robot.     

A note on productivity gains in flexible robotic cells

Flexible robotic cells combine the capabilities of robotic flow shops with those of flexible manufacturing systems. In an m-machine flexible cell, each part visits each machine in the same order. However, the m operations can be performed in any order, and each machine can be configured to perform any operation. We derive the maximum percentage increase in throughput that can be achieved by changing the assignment of operations to machines and then keeping that assignment constant throughout a lot's processing. We find that no increase can be gained in two-machine cells, and that the gain in three- and four-machine cells each is at most 14 %.     

面向复杂曲面的机器人砂带磨抛路径规划及后处理研究

针对机器人砂带磨抛复杂曲面叶片问题,对叶片内外型面和进排气边的磨抛路径规划及后处理技术进行了研究,对复杂曲面叶片的机器人砂带磨抛路径规划的计算效率及加工效率进行了分析,提出了一种将基于等残留高度法的笛卡尔空间计算的磨抛行距转化为参数域空间的磨抛行距的方法,并将机器人砂带磨抛复杂曲面叶片接触轮的中心坐标位置、支撑轴矢量以及轴线矢量数据后处理为机器人的位姿信息,利用机器人砂带磨抛系统装备实验平台对复杂曲面叶片进行了实际的加工实验。研究结果表明:所提出的刀路规划和后处理技术能够有效地解决机器人砂带磨抛复杂曲面叶片的问题,具有加工路径总长短以及路径条数少的特点,计算简单、加工效率高、加工表面质量好。