School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
PLoS Comput Biol. 2011 Sep;7(9):e1002177. doi: 10.1371/journal.pcbi.1002177. Epub 2011 Sep 29.
Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment--by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots.
群体导航和群集行为在动物群体中得到了研究，如鱼群、鸟群、细菌和黏菌。计算机模型表明，简单个体的集体行为可以源自个体之间的简单相互作用，这些相互作用包括短程排斥、中程对齐和远程吸引。在这里，我们研究了受细菌启发的智能体在复杂地形中的集体导航，其中相互作用是自适应的，取决于性能。更具体地说，每个智能体根据其局部环境调整与其他智能体的相互作用——在有益的方向上导航时会降低同伴的影响，否则会增加影响。我们表明，包含这种性能相关的自适应相互作用可显著提高集体群集性能，从而实现高效导航，尤其是在复杂地形中。值得注意的是，为了实现这种自适应相互作用，每个建模的智能体只需要具有短期记忆的简单计算能力，这可以很容易地在简单的群集机器人中实现。
