论文标题
数据驱动的自适应任务分配用于异质多机器人团队，使用强大的控制屏障功能
Data-Driven Adaptive Task Allocation for Heterogeneous Multi-Robot Teams Using Robust Control Barrier Functions
论文作者
论文摘要
多机器人任务分配是机器人技术中无处不在的问题，因为它在各种方案中的适用性。自适应任务分配算法在部署机器人以执行任务的环境中说明了未知的干扰和未预测的现象。但是，这种适应性通常以需要机器人模型的精确知识来评估分配效率并在线调整任务分配的成本。因此，环境干扰会大大降低模型的准确性，而模型又会对任务分配的质量产生负面影响。在本文中，我们利用高斯流程，差分包含和强大的控制屏障功能来学习环境干扰，以确保强大的任务执行。我们在实际的多机器人系统上展示了所提出的框架的实现和有效性。
Multi-robot task allocation is a ubiquitous problem in robotics due to its applicability in a variety of scenarios. Adaptive task-allocation algorithms account for unknown disturbances and unpredicted phenomena in the environment where robots are deployed to execute tasks. However, this adaptivity typically comes at the cost of requiring precise knowledge of robot models in order to evaluate the allocation effectiveness and to adjust the task assignment online. As such, environmental disturbances can significantly degrade the accuracy of the models which in turn negatively affects the quality of the task allocation. In this paper, we leverage Gaussian processes, differential inclusions, and robust control barrier functions to learn environmental disturbances in order to guarantee robust task execution. We show the implementation and the effectiveness of the proposed framework on a real multi-robot system.
