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首页> 外文会议>Evolutionary and Bio-inspired Computation: Theory and Applications II >A Bio-Inspired Swarm Robot Coordination Algorithm for MultipleTarget Searching
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A Bio-Inspired Swarm Robot Coordination Algorithm for MultipleTarget Searching

机译：多目标搜索的生物启发式群体机器人协调算法

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The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group competence (agents need to know how to work together well). In our previous proposed bio-inspired coordination method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot movement during coordination, which may lead to more power consumption and longer searching time. To address these issues, an adaptive LIVS (ALIVS) method is proposed in this paper, which not only considers the travel cost and target weight, but also predicting the target/robot ratio and potential robot redundancy with respect to the detected targets. Furthermore, a dynamic weight adjustment is also applied to improve the searching performance. This new method a truly distributed method where each robot makes its own decision based on its local sensing information and the information from its neighbors. Basically, each robot only communicates with its neighbors through a virtual stigmergy mechanism and makes its local movement decision based on a Particle Swarm Optimization (PSO) algorithm. The proposed ALIVS algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching target. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.
机译：在动态环境下，要寻找多个目标的多机器人系统的协作是具有挑战性的，因为多机器人系统需要团队凝聚力（座席需要忠实地合作的动力）和团队能力（代理商需要知道如何一起工作）好）。在我们先前提出的受生物启发的协调方法中，通过虚拟Stigmergy（LIVS）进行本地交互，一个问题是协调过程中机器人运动的随机性很大，这可能导致更多的功耗和更长的搜索时间。为了解决这些问题，本文提出了一种自适应LIVS（ALIVS）方法，该方法不仅考虑了旅行成本和目标重量，而且还预测了目标/机器人比率以及相对于所检测目标的潜在机器人冗余。此外，还应用了动态权重调整来提高搜索性能。这种新方法是真正的分布式方法，其中每个机器人都基于其本地感测信息和来自其邻居的信息做出自己的决定。基本上，每个机器人都仅通过虚拟的tig气机制与其邻居进行通信，并基于粒子群优化（PSO）算法做出其局部运动决策。拟议的ALIVS算法已在搜索目标中的嵌入式机器人模拟器Player / Stage上实现。仿真结果证明了在实际约束下以省电方式实现的效率和鲁棒性。

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