Integration of Knowledge-Based Systems and Neural Networks for Intellegent Sensorimotor Control

基于知识的系统和神经网络的集成用于智能感觉运动控制

• 批准号: 8960548
• 负责人: David Handelman
• 金额: $ 4.99万
• 依托单位: Robicon Systems Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-01-01 至 1990-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8960548&HistoricalAwards=false
• 关键词: Integration; Knowledge; Based; Systems; Neural

This research will develop a hybrid technique for the intelligent control of dexterous robotic manipulators. Tasks initially specified in a high-level task language will be encoded and refined in a neural-nework form. Phase I will determine the feasibility of applying this technique to the difficult problem of learning to position the loaded endpoint of a redundant manipulator subject to multiple goals and constraints. Rule- based control components will train, then shift control to, neural network components. Network performance will then be refined through reinforcement learning and on-line optimization. This technique, combining design convenience with implementation efficiency, will lay the foundation for robotic systems able to acquire skills for performing complex tasks in unstructured environment.

这项研究将开发一种混合技术， 灵巧机械手的控制。 最初的任务 在高级任务语言中指定的任务将被编码， 以神经网络的形式进行提炼。 第一阶段将确定 将该技术应用于难题的可行性 学习定位冗余的加载端点 操纵器受到多个目标和约束。 规则- 基于控制组件将训练，然后将控制转移到， 神经网络组件 网络性能将 通过强化学习和在线优化进行优化。 该技术结合了设计方便性和实现 将为机器人系统奠定基础， 获得在非结构化环境中执行复杂任务的技能 环境