模糊环境的表示及机器人轨迹规划

本文利用模糊集理论，提出一种非确定环境的描述方法，并且针对机器人在该类环境中的避障轨迹规划，建立了相应的决策函数和规划策略，最后，给出在ＰＣ－３８６计算机上的仿真试验结果。     

有环境约束时完成一族跟踪任务的力控制

本文为工业机器人提出了一种极点配置控制法，这种控制方法的优点有：一是它的积分作用消除了机器人的微小扰动和稳态误差；二是能任意设置系统的极点，因此能保证闭环系统的稳定性和规定状态变量的暂态响应；三是加入了加速度反馈，抑制了由电枢电感所引起的机械手的振动，最后，给出了ＰＵＭＡ５６２机器人的计算机仿真和实验结果验证了此控制法的有效性。     

用奇异模型描述的受限机器人的学习控制

本文了一类受限机器人的迭代学习控制方案，控制器的设计是基于机器人模型的降阶形式。在存在有界未知干扰的情况下，对末端操纵器受线性、无摩擦约束面的受限机器人。本文给出的控制方案保证了机器人系统的完全运动跟踪，同时保证了力跟踪误差有界的，且界的大小是可调节的。     

管内机器人研究中的几项新技术

介绍了我们研制的管内机器人中应用的具有创新性的三项技术：１．轮式全主动新型行走机构；２．内壁环带视觉装置；３．新型离心喷涂器。     

锡矿山重复水利用的措施及效果

本介绍了锡矿山前闪星锑业有限责任公司如何加强对关键耗水工序的管理及提高重复水利用率的措施，并对所取得的效果进行了阐述，可供用水大户如何节水以借鉴。     

HTR-10燃料元件装卸系统冷态调试

10MW高温气冷实验堆（HTR－10）是一座球床堆，由燃料元件装卸系统实现燃料元件的装卸和循环，且不需要停堆，为保证HTR－10的正常运行，燃料元件装卸系统必须安全，可靠，为此，必须对燃料元件装卸系统进行周密，细致的调试试验和验证，本文介绍了燃料元件装卸系统冷调试的主要调试项目，调试方法和调试结果。     

An on-line microcomputer-based respiratory monitor for manual materials handling

The main objective of the present study is to develop a low cost microcomputer-based respiratory system that is capable of computing the oxygen consumption and minute ventilation of individuals engaged in manual materials handling on an on-line basis. The design, specifications, and merits of the system are given. The software package developed was written in assembly language for the IBM AT personal computer.     

Kanga(ROO): Handling the micro-DST of the BaBar experiment with ROOT

A system based on ROOT for handling the micro-DST of the BaBar experiment is described. The purpose of the Kanga system is to have micro-DST data available in a format well suited for data distribution within a world-wide collaboration with many small sites. The design requirements, implementation and experience in practice after three years of data taking by the BaBar experiment are presented.     

Minimum-Energy Translational Trajectory Generation for Differential-Driven Wheeled Mobile Robots

Mobile robots can be used in many applications, such as exploration, search and rescue, reconnaissance, security, and cleaning. Mobile robots usually carry batteries as their energy source and their operational time is restricted by the finite energy available from the batteries. Therefore, energy constraints are critical to the service time of mobile robots. This paper investigates the minimum-energy control problem for translational trajectory generation, which minimizes the energy drawn from the batteries. Optimal control theory is used to find the optimal velocity trajectory in analytic form. To demonstrate energy efficiency obtainable, we performed simulations of minimum-energy velocity control and compared the results with loss-minimization control and energy-optimal trapezoidal velocity profiles. Simulation results showed that significant energy savings can be achieved, of up to 9% compared with loss-minimization control and up to 10% compared with energy-optimal trapezoidal velocity profile. We also performed an actual robot experiment using Pioneer 3-AT platform to show the validity of the proposed minimum-energy velocity control. The experimental results revealed that the proposed minimum-energy velocity control can save the battery energy up to 10% compared with loss-minimization control. Categories (3): Robot control, (5): RobotMotion Planning     

Global Level Path Planning for Mobile Robots in Dynamic Environments

This paper presents a self-adapting approach to global level path planning in dynamic environments. The aim of this work is to minimize risk and delays in possible applications of mobile robots (e.g., in industrial processes). We introduce a hybrid system that uses case-based reasoning as well as grid-based maps for decision-making. Maps are used to suggest several alternative paths between specific start and goal point. The casebase stores these solutions and remembers their characteristics. Environment representation and casebase design are discussed. To solve the problem of exploration vs. exploitation, a decision-making strategy is proposed that is based on the irreversibility of decisions. Forgetting strategies are discussed and evaluated in the context of case-based maintenance. The adaptability of the system is evaluated in a domain based on real sensor data with simulated occupancy probabilities. Forgetting strategies and decision-making strategies are evaluated in simulated environments. Experiments show that a robot is able to adapt in dynamic environments and can learn to use paths that are less risky to follow.