Expedited Award for Novel Research: A Neural Network Model for Adaptive Control of Multiple Mobile Robots

新颖研究加急奖：用于多个移动机器人自适应控制的神经网络模型

• 批准号: 8719846
• 负责人: George Bekey
• 金额: $ 3万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-09-01 至 1989-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8719846&HistoricalAwards=false
• 关键词: Expedited; Award; Novel; Research; Neural

A neural network model for task allocation among multiple mobile robots is proposed. The goal of the system is to allocated tasks adaptively while optimizing an overall performance index and avoiding conflicts among the robots. Robots will be allocated to work locations according to the activation levels of corresponding units in the network. Adaptiveness to environmental or internal system changes will be achieved through a process of constraint propagation and relaxation. It is proposed to resolve potential collisions among the moving robots by analyzing a global path planning problem followed by a local path replanning problem. Structural changes in the connectionist network will provide the information for conflict resolution in replanning to avoid collisions. The usefulness of the framework for controlling mobile robots will be demonstrated by simulation on a TI Explorer Lisp machine, using an object-oriented simulation language being developed in PI's laboratory.

多任务分配的神经网络模型 提出了移动的机器人。 该系统的目标是分配 自适应地执行任务，同时优化整体性能指标， 避免机器人之间的冲突。 机器人将被分配到 工作地点根据相应的激活级别 网络中的单位。 对环境或内部的适应性 制度变革将通过一个约束过程来实现 传播和松弛。 建议解决潜在的 通过分析全局路径， 规划问题之后是局部路径重新规划问题。 连接主义网络的结构变化将提供 在重新规划中解决冲突的信息， 碰撞 控制移动的的框架的有用性 机器人将通过TI Explorer Lisp上的模拟进行演示 机器，使用面向对象的仿真语言， 在PI的实验室里。