Optimal task allocation in multi-human multi-robot interaction
Optimal task allocation in multi-human multi-robot interaction
复制标题
多人多机器人交互中的最优任务分配
作者：
Monali S. Malvankar;S. Mehta
多人多机器人交互是一个复杂的系统，其中机器人，例如，无人驾驶飞行器可以与一组人类操作员共享信息，以在指定时间内执行地理上分散的基于优先级的任务。在这个复杂的系统中，关键是在多个级别上优化分配由高风险和低风险信息组成的任务，以便在资源有限的情况下最大限度地提高整个系统的效率。一个多层次的编程模型中，代理分配从多个机器人接收到的信息，以多个团队的领导者谁又分发信息到他们的团队内的操作员。代理的目标是将任务优化分配给多个团队负责人，以最大限度地提高整体系统性能，并在考虑人为因素的同时最大限度地减少处理成本和时间。所开发的模型使用逆向归纳法求解，并以逆时间序列给出详细信息。如果将人的因素沿着包括在生产力度量中，则可以提高多人多机器人交互系统的性能。
Multi-human multi-robot interaction is a complex system in which robots, e.g., unmanned aerial vehicles, may share information with a group of human operators to perform geographically-dispersed priority-based tasks within a specified time. In this complex system, the key is to optimally allocate tasks comprising of high-risk and low-risk information at multiple-levels in order to maximize effectiveness of the entire system given the limited resources. A multi-level programming model is developed in which an agent allocates information received from multiple robots to multiple team leaders who in turn distribute information to operators within their teams. The objective of the agent is to optimally allocate tasks to multiple team leaders to maximize the overall system performance and to minimize the processing cost and time while considering human factors. The developed model is solved using backward induction and details are presented in reverse time sequence. If human factors are included along with the productivity metrics then the performance of the multi-human multi-robot interaction systems can be improved.