Task Switching for Supervisory Control of Multi-Robot Teams

Human multi-robot control is a complex process: an operator’s attention constantly shifts among various tasks to maintain the necessary awareness of a given situation, such as exploring environments and locating targets simultaneously. To minimize the laborious monitoring, if a robot can self-reflect its needs for interaction and alert an operator to any abnormalities, the operator’s attention could be appropriately directed to urgent events in a timely manner. This type of human-robot interaction can be viewed as a queuing system, in which an operator acts as a server, and robots’ requests for services are the jobs. This paper presents two experiments that investigated scheduling interventions. In the first experiment, participants performed search and rescue tasks while assisted by an alarmed system: Open-queue, in which all the alarms are displayed at once, or FIFO-queue (first-in-first-out approach), which shows only a single alarm at a time. The second study compared the FIFO and Open conditions from the first study with a shortest-job-first (SJF) queue system. The results show that operator attention can be effectively directed toward interaction with needed robots without degrading performance, which suggest the uses of the queueing disciplines could lead to superior performance while choosing between robot requests becomes more challenging. Additionally, to better understand operators’ intention and resulting behaviors, a Hidden Markov Model (HMM) was applied to investigate human control processes in interactions with multi-robot teams. The HMM results demonstrated fundamental differences among queuing mechanisms, which are difficult to observe through conventional approaches.