论文标题
无人空中系统形态的同时设计及其集体搜索行为
Efficient Concurrent Design of the Morphology of Unmanned Aerial Systems and their Collective-Search Behavior
论文作者
论文摘要
机器人的集体操作，例如以团队或群为单位运行的无人机（UAV），受其个人功能的影响，这又取决于其物理设计，又称其形态。但是，除了少数（尽管临时）进化机器人技术方法外，在理解形态学和集体行为的相互作用方面几乎没有工作。特别缺乏计算框架来同时寻找机器人形态和其行为模型的超参数，这些模型共同优化了集体（团队）绩效。为了解决这一差距，本文提出了一个新的共同设计框架。 Here the exploding computational cost of an otherwise nested morphology/behavior co-design is effectively alleviated through the novel concept of ``talent" metrics; while also allowing significantly better solutions compared to the typically sub-optimal sequential morphology$\to$behavior design approach. This framework comprises four major steps: talent metrics selection, talent Pareto exploration (a multi-objective morphology optimization process), behavior optimization,形态和最终确定概念是通过将其作为一个团队进行的无人机来证明，例如，在受害者搜索和危害的本地化中，集体行为是由最近报道的贝叶斯搜索算法驱动的。此外，具有6到15个无人机的环境和团队，与预计的嵌套设计方法相比，该共同设计的过程可减少计算时间的两种数量级。
The collective operation of robots, such as unmanned aerial vehicles (UAVs) operating as a team or swarm, is affected by their individual capabilities, which in turn is dependent on their physical design, aka morphology. However, with the exception of a few (albeit ad hoc) evolutionary robotics methods, there has been very little work on understanding the interplay of morphology and collective behavior. There is especially a lack of computational frameworks to concurrently search for the robot morphology and the hyper-parameters of their behavior model that jointly optimize the collective (team) performance. To address this gap, this paper proposes a new co-design framework. Here the exploding computational cost of an otherwise nested morphology/behavior co-design is effectively alleviated through the novel concept of ``talent" metrics; while also allowing significantly better solutions compared to the typically sub-optimal sequential morphology$\to$behavior design approach. This framework comprises four major steps: talent metrics selection, talent Pareto exploration (a multi-objective morphology optimization process), behavior optimization, and morphology finalization. This co-design concept is demonstrated by applying it to design UAVs that operate as a team to localize signal sources, e.g., in victim search and hazard localization. Here, the collective behavior is driven by a recently reported batch Bayesian search algorithm called Bayes-Swarm. Our case studies show that the outcome of co-design provides significantly higher success rates in signal source localization compared to a baseline design, across a variety of signal environments and teams with 6 to 15 UAVs. Moreover, this co-design process provides two orders of magnitude reduction in computing time compared to a projected nested design approach.
