Exploration strategies based on multi-criteria decision making for searching environments in rescue operations
Exploration strategies based on multi-criteria decision making for searching environments in rescue operations
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DOI：
10.1007/s10514-011-9249-9
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发表时间：
2011-11-01
影响因子：
3.5
通讯作者：
Amigoni, Francesco
中科院分区：
文献类型：
--
作者：
Basilico, Nicola;Amigoni, Francesco
一些应用要求自主机器人在最初未知的环境中搜索静态目标，而无需任何有关环境结构和目标位置的先验信息。目标可以是搜救中的人类受害者或觅食中的材料。在这些场景中，机器人利用探索策略以自主且有效的方式移动，逐渐发现环境。文献中提出的大多数策略都是基于根据结合不同标准的临时效用函数来评估环境的已知部分和未知部分之间的边界上的多个候选位置的想法。在本文中，我们展示了使用基于多标准决策（MCDM）的更有理论依据的方法来定义搜索和救援应用中使用的机器人的探索策略的一些优点。我们在现有的机器人控制器中实施了一些基于 MCDM 的探索策略，并在模拟环境中评估了它们的性能。
Some applications require autonomous robots to search an initially unknown environment for static targets, without any a priori information about environment structure and target locations. Targets can be human victims in search and rescue or materials in foraging. In these scenarios, the environment is incrementally discovered by the robots exploiting exploration strategies to move around in an autonomous and effective way. Most of the strategies proposed in literature are based on the idea of evaluating a number of candidate locations on the frontier between the known and the unknown portions of the environment according to ad hoc utility functions that combine different criteria. In this paper, we show some of the advantages of using a more theoretically-grounded approach, based on Multi-Criteria Decision Making (MCDM), to define exploration strategies for robots employed in search and rescue applications. We implemented some MCDM-based exploration strategies within an existing robot controller and we evaluated their performance in a simulated environment.