Multi-robot Exploration Based on Market Approach and Immune Optimizing Strategy

Guang-ming Xiong, Jian-wei Gong, Huiyan Chen, Zhibao Su
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引用次数: 8

Abstract

A multi-robot system is used to explore an environment and create a map based on market approach. Data fusion is performed using Bayes theorem and then the local maps are updated. A diffusivity concept is defined to describe the robots' extent apart from one another. The immune optimizing strategy is introduced to select goal points since it is a problem of optimized combination. In order to minimize repeated coverage and improve the exploration efficiency, the evaluation function considers the cost, revenue and diffusivity. Simulation examples show that the proposed method is effective for the stated problem and the immune optimizing strategy is more efficient than other strategies.
基于市场方法和免疫优化策略的多机器人探索
一个多机器人系统被用来探索一个环境,并基于市场方法创建一个地图。利用贝叶斯定理进行数据融合,更新局部地图。定义了一个扩散系数概念来描述机器人彼此之间的距离。由于目标点选择是一个优化组合问题,引入了免疫优化策略。为了最大限度地减少重复覆盖,提高勘探效率,该评价函数考虑了成本、收益和扩散系数。仿真实例表明,该方法对所述问题是有效的,且免疫优化策略比其他策略更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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