Broadening applicability of swarm-robotic foraging through constraint relaxation
Broadening applicability of swarm-robotic foraging through constraint relaxation
复制标题
通过约束松弛拓宽群体机器人觅食的适用性
DOI：
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复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
Maria L. Gini
中科院分区：
文献类型：
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作者：
John Harwell;Maria L. Gini
群机器人（SR）为现实世界的问题提供了有前途的解决方案，这些问题可以建模为觅食任务，例如灾难/垃圾清理或建筑物收集。然而，目前的SR觅食的方法，限制其适用性选择的现实世界的环境中的假设。提出了一种改进的基于任务划分的自组织任务分配方法，该方法消除了以下限制：（1）觅食环境的先验知识，以及（2）对中间丢弃/拾取站点行为的严格限制。通过仿真实验，我们表明，在所提出的约束放松，我们的方法仍然提供了性能的提高相比，一个未分区的策略在一些组合的群体规模，机器人的能力，和环境条件。这项工作拓宽了SR觅食方法的适用性，表明它们在理想条件下可以有效，同时在更不稳定/更具挑战性的环境中继续稳健地执行。
Swarm robotics (SR) offers promising solutions to real-world problems that can be modeled as foraging tasks, e.g. disaster/trash cleanup or object gathering for construction. Yet current SR foraging approaches make limiting assumptions that restrict their applicability to selected real-world environments. We propose an improved self-organized task allocation method based on task partitioning that removes restrictions such as: (1) a priori knowledge of foraging environment, and (2) strict limitations on intermediate drop/pickup site behavior. With experiments in simulation, we show that under the proposed constraint relaxation, our approach still provides performance increases when compared to an unpartitioned strategy within some combinations of swarm sizes, robot capabilities, and environmental conditions. This work broadens the applicability of SR foraging approaches, showing that they can be effective under ideal conditions while continuing to perform robustly in more volatile/challenging environments.