机器人任务排序问题的神经网络方法

O. Maimon , D. Braha , V. Seth
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引用次数: 25

摘要

针对机器人任务排序问题,提出了一种成功实现的神经网络方法。该问题解决了由物料搬运机器人从机器上装卸零件的任务顺序。性能标准是最小化机器人完成一组任务的总行程时间和被排序任务的延迟时间的加权目标。本文还介绍了神经网络算法在思维机器公司的CM-5并行计算机上的三阶段并行实现,从而大大提高了求解速度。为了评估神经网络方法的性能,提出了分支定界法和启发式方法。神经网络方法得到了很好的结果,特别适用于在并行计算平台上求解大型问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neural network approach for a robot task sequencing problem

This paper presents a neural network approach with successful implementation for the robot task-sequencing problem. The problem addresses the sequencing of tasks comprising loading and unloading of parts into and from the machines by a material-handling robot. The performance criterion is to minimize a weighted objective of the total robot travel time for a set of tasks and the tardiness of the tasks being sequenced. A three-phased parallel implementation of the neural network algorithm on Thinking Machine's CM-5 parallel computer is also presented which resulted in a dramatic increase in the speed of finding solutions. To evaluate the performance of the neural network approach, a branch-and-bound method and a heuristic procedure have been developed for the problem. The neural network method is shown to give good results and is especially useful for solving large problems on a parallel-computing platform.

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