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

提出了一种多移动机器人任务分配的神经网络模型。该系统的目标是自适应地分配任务，同时优化整体性能指标，避免机器人之间的冲突。机器人将根据网络中相应单元的激活程度被分配到工作地点。对环境或内部系统变化的适应性将通过约束的传播和放松过程来实现。提出通过分析全局路径规划问题和局部路径重规划问题来解决运动机器人之间的潜在碰撞问题。连接主义网络的结构变化将为重新规划以避免冲突的冲突解决提供信息。通过在TI Explorer Lisp机器上使用PI实验室开发的面向对象模拟语言进行仿真，可以证明该框架对控制移动机器人的有用性。

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.