QED：使用质量-环境-多样性来进化具有韧性的机器人集群
QED: using Quality-Environment-Diversity to evolve resilient robot swarms
AI总结：
针对集群机器人故障恢复问题，提出基于环境多样性的新型行为描述符QED，结合质量-多样性算法构建无模型恢复方法，使故障影响降低2-3倍并能有效应对高影响故障。
摘要：
在集群机器人学中，集群中的任何机器人都可能受到不同故障的影响，导致显著的性能下降。为了允许从随机注入到集群中不同机器人的故障中进行故障恢复，无模型方法可能更可取，这是由于模型中故障的累积以及预测邻近机器人行为的困难。一种用于故障恢复的无模型方法包括两个阶段：在仿真期间，质量-多样性算法进化出一个行为上多样化的控制器存档；在目标应用期间，在故障注入后启动对最佳控制器的搜索。在质量-多样性算法中，行为描述符的选择是一个关键的设计选择，它决定了进化存档的质量，从而决定了故障恢复性能。尽管环境是行为的重要决定因素，但在选择合适的行为描述符时，环境多样性的影响往往被忽视。本研究比较了不同的行为描述符，包括两个适用于广泛任务范围的通用描述符，一个适合感兴趣领域的人工编码描述符，以及一种基于环境多样性的新型描述符，我们称之为质量-环境-多样性（QED）。结果证明，上述用于故障恢复的无模型方法在集群机器人学背景下是可行的，将故障影响降低了2到3倍。此外，QED获得的环境多样性产生了一种独特的行为多样性特征，使其能够从高影响故障中恢复。
英文摘要：
In swarm robotics, any of the robots in a swarm may be affected by different faults, resulting in significant performance declines. To allow fault recovery from randomly injected faults to different robots in a swarm, a model-free approach may be preferable due to the accumulation of faults in models and the difficulty to predict the behaviour of neighbouring robots. One model-free approach to fault recovery involves two phases: during simulation, a quality-diversity algorithm evolves a behaviourally diverse archive of controllers; during the target application, a search for the best controller is initiated after fault injection. In quality-diversity algorithms, the choice of the behavioural descriptor is a key design choice that determines the quality of the evolved archives, and therefore the fault recovery performance. Although the environment is an important determinant of behaviour, the impact of environmental diversity is often ignored in the choice of a suitable behavioural descriptor. This study compares different behavioural descriptors, including two generic descriptors that work on a wide range of tasks, one hand-coded descriptor which fits the domain of interest, and one novel type of descriptor based on environmental diversity, which we call Quality-Environment-Diversity (QED). Results demonstrate that the above-mentioned model-free approach to fault recovery is feasible in the context of swarm robotics, reducing the fault impact by a factor 2-3. Further, the environmental diversity obtained with QED yields a unique behavioural diversity profile that allows it to recover from high-impact faults.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
