How search and its subtasks scale in N robots

Abstract

The present study investigates the effect of the number of controlled robots on performance of an urban search and rescue (USAR) task using a realistic simulation. Participants controlled either 4, 8, or 12 robots. In the fulltask control condition participants both dictated the robots' paths and controlled their cameras to search for victims. In the exploration condition, participants directed the team of robots in order to explore as wide an area as possible. In the perceptual search condition, participants searched for victims by controlling cameras mounted on robots following predetermined paths selected to match characteristics of paths generated under the other two conditions. By decomposing the search and rescue task into exploration and perceptual search subtasks the experiment allows the determination of their scaling characteristics in order to provide a basis for tentative task allocations among humans and automation for controlling larger robot teams. In the fulltask control condition task performance increased in going from four to eight controlled robots but deteriorated in moving from eight to twelve. Workload increased monotonically with number of robots. Performance per robot decreased with increases in team size. Results are consistent with earlier studies suggesting a limit of between 8-12 robots for direct human control.

Scheduling operator attention for Multi-Robot Control

“…Unmarked victims that had appeared within a robot's FOV (field of view) without being marked were counted as false negatives (misses). Operators in the Open-queue condition missed the most victims (15) and the FIFO-queue was the lowest (11) with the Priority-queue falling in between (13), as shown on figure 5. A repeated measures ANOVA shows a main effect among queue conditions, F(2,58)=20.5, p<.001.…”

“…Because the laser map is built up slowly as the environment is explored and the office like environment provides few distinctive landmarks, there was little opportunity for participants to benefit from prior exposure to the environment. A team size of 8 robots found by [13] to yield the highest performance at the USAR task was used in the experiment. Robots followed predefined paths of waypoints, similar to paths generated by an autonomous path planner [14] to explore the map.…”

Workload, awareness and automation in multiple-robot supervision

“…17 In the study where robots were deployed to search for victims in an urban search and rescue scenario, the number of victims has been found to decrease as the workload increased. 31 The causes of workload in multiple-robot supervision A number of factors have been suggested to affect supervisor workload within the domain of human-supervised robot deployment. These include task complexity, interface design and the levels of available autonomy.…”

“…Furthermore, the rise in workload when group size increases is expected to be exponential especially when it is necessary for the human to coordinate robot activities. 31 These studies are just a few examples of the works comparing workload levels as the human supervisor is tasked to deploy various numbers of robots. Many other research efforts 15,18,31,32,36,37 have reported similar results to the ones presented above, reinforcing the link between increased group size and heightened workload.…”

Predictive Planning for Heterogeneous Human-Robot Teams

“…10-12, 19, 20 Other HRI research involves exploring the effects of changing different human-robot team parameters, such as varying vehicle autonomy levels or varying the number of vehicles controlled by each operator. 15,23 These previous approaches attempt to improve the performance of one type of agent (either human or robotic) given expected plans for the other agents they are interacting with, but do not consider optimization over both human and robotic agents simultaneously. In addition, this previous work focuses predominantly on the case of a single operator controlling multiple vehicles but does not consider coordinated tasks involving multiple operators and multiple unmanned vehicles.…”