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首页> 外文期刊>Applied Mathematical Modelling >A cellular automata ant memory model of foraging in a swarm of robots
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A cellular automata ant memory model of foraging in a swarm of robots

机译：机器人群中觅食的元胞自动机记忆模型

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摘要

This study proposes a cellular automata ant memory model (CAAM) that controls a robot swarm when undertaking the foraging task in a previously known environment with nests. The floor field is well known to all robots, which share the same environment, communicating through the inverted pheromone. This substance is deposited by each swarm robot over every step in searching, which results in a repulsive force between team members. Besides, a short-term memory inspired by Tabu Search is applied to enable robots to remember their last positions and to avoid useless explorations. On the other hand, homing is based on the behavior observed in pedestrian evacuation, resulting in an attractive force through the nests. Moreover, a dynamic information is used to avoid queues of robots and bottlenecks next to the nests. Each robot step is a first choice movement with a stochastic conflict solver, which results in a non-deterministic characteristic to the model. The proposed model was implemented and submitted to several simulations to evaluate its resultant behavior. Different environmental conditions were employed to refine its intrinsic parameters. The results shown that the proposed model is able to perform the foraging task in a competitive way: in searching the swarm perform a good environment coverage and in homing robots are able to find the most adequate nests.
机译：这项研究提出了一种细胞自动记忆模型（CAAM），该模型在先前已知的带巢的环境中进行觅食任务时控制机器人群。地板场对于共享相同环境并通过倒置信息素进行通信的所有机器人都是众所周知的。该物质由每个群机器人在搜索的每个步骤中沉积，从而导致团队成员之间产生排斥力。此外，受禁忌搜索启发的短期记忆可用于使机器人记住他们的最后位置并避免无用的探索。另一方面，归巢基于行人疏散中观察到的行为，从而导致通过巢穴的吸引力。此外，使用动态信息来避免机器人排队和嵌套旁边的瓶颈。每个机器人步骤都是带有随机冲突求解器的第一选择运动，这会导致模型具有不确定性。所提出的模型已实施并提交给多个模拟以评估其结果行为。采用不同的环境条件来完善其固有参数。结果表明，所提出的模型能够以竞争性方式执行觅食任务：在搜索群时，具有良好的环境覆盖范围；在归巢中，机器人能够找到最合适的巢。

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