Cellular ANTomata: Food-Finding and Maze-Threading
Cellular ANTomata: Food-Finding and Maze-Threading
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细胞 ANTomata：寻找食物和走迷宫
DOI：
10.1109/icpp.2008.13
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
A. Rosenberg
中科院分区：
文献类型：
--
作者：
A. Rosenberg
提出并说明了在固定的地理约束环境中实现机器人协调的蚁群算法的模型。这个模型被称为细胞自动机（cellular automata），它颠倒了反机器人与它们所处环境之间的关系：智能现在存在于环境中，而不是蚂蚁身上。元胞自动机模型通过三个概念验证问题来说明：让蚂蚁“停”在最近的角落；让蚂蚁寻找“食物”（有或没有难以穿透的障碍物）；有一条蚂蚁线的迷宫。在所有情况下，“非智能”的基于元胞自动机的反机器人比传统的“智能”反机器人更有效地完成目标；事实上，“智能”反机器人根本不会停车！所有提出的算法都是可伸缩的：它们可以在任何有限大小的环境中工作。
A model for realizing ant-inspired algorithms that coordinate robots within a fixed, geographically constrained environment is proposed and illustrated. The model, dubbed cellular ANTomata, inverts the relationship between ant-robots and the environment that they navigate: intelligence now resides in the environment rather than in the ants. The cellular ANTomaton model is illustrated via three proof-of-concept problems: having ants "park" in the nearest corner; having ants seek "food items" (both with and without impenetrable obstacles); having a single ant thread a maze. In all cases, "unintelligent" cellular-ANTomata-based ant-robots accomplish goals provably more efficiently than traditional "intelligent" ant-robots can; indeed, "intelligent" ant-robots cannot park at all! All of the presented algorithms are scalable: they provably work within any finite-size environment.