Task Switching and Multi-Robot Teams

Goodrich, Michael A.; Quigley, Morgan; Cosenzo, Keryl

doi:10.1007/1-4020-3389-3_15

Cited by 39 publications

(25 citation statements)

References 6 publications

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“…In both the manual and mixed initiative conditions participants divided their attention approximately equally among the robots but in the mixed initiative mode they switched among robots more rapidly. Psychologists [12] have found task switching to impose cognitive costs and switching costs have previously been reported [7,20] for multirobot control. Higher switching costs might be expected to degrade performance, however in this study, more rapid switching was associated with improved performance in both manual and mixed initiative conditions.…”

Section: Resultsmentioning

confidence: 99%

Human control for cooperating robot teams

Wang

Lewis

2007

Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction

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Human control of multiple robots has been characterized by the average demand of single robots on human attention or the distribution of demands from multiple robots. When robots are allowed to cooperate autonomously, however, demands on the operator should be reduced by the amount previously required to coordinate their actions. The present experiment compares control of small robot teams in which cooperating robots explored autonomously, were controlled independently by an operator or through mixed initiative as a cooperating team. Mixed initiative teams found more victims and searched wider areas than either fully autonomous or manually controlled teams. Operators who switched attention between robots more frequently were found to perform better in both manual and mixed initiative conditions.

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Section: Resultsmentioning

confidence: 99%

Human control for cooperating robot teams

Wang

Lewis

2007

Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction

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“…Switching strategy affects the total time tasks spend waiting for service not only because of the ordering of the tasks (queuing policy), but also because of the time required for the mental model change of the operator (switching cost) if the tasks are dissimilar [8]. It has been demonstrated that for operators of multiple, unmanned vehicles, the switching cost can be substantial [9].…”

Section: Switching Strategymentioning

confidence: 99%

Modeling multiple human operators in the supervisory control of heterogeneous unmanned vehicles

Mekdeci

Cummings

2009

Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems

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In the near future, large, complex, time-critical missions, such as disaster relief, will likely require multiple unmanned vehicle (UV) operators, each controlling multiple vehicles, to combine their efforts as a team. However, is the effort of the team equal to the sum of the operator's individual efforts? To help answer this question, a discrete event simulation model of a team of human operators, each performing supervisory control of multiple unmanned vehicles, was developed. The model consists of exogenous and internal inputs, operator servers, and a task allocation mechanism that disseminates events to the operators according to the team structure and state of the system. To generate the data necessary for model building and validation, an experimental test-bed was developed where teams of three operators controlled multiple UVs by using a simulated ground control station software interface. The team structure and interarrival time of exogenous events were both varied in a 2x2 full factorial design to gather data on the impact on system performance that occurs as a result of changing both exogenous and internal inputs. From the data that was gathered, the model was able to replicate the empirical results within a 95% confidence interval for all four treatments, however more empirical data is needed to build confidence in the model's predictive ability.

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“…There are two plausible explanations for this finding: 1) despite the apparent equivalence of repairs, operators were able to take advantage of situational differences involving things such as proximity to targets or other robots to choose robots for repair that led to higher overall performance or 2) the system's designation of the robot to be serviced next seemed too restrictive to operators who therefore did not consistently follow its advice. Direction of operators at this level of specificity has often met with resistance [2], [6]. Because the FIFO queue always presented a robot in an appropriately failed state, however, the aid was errorless and not subject to the usual manipulations of trust [7].…”

Section: Introductionmentioning

confidence: 99%

Scheduling operator attention for Multi-Robot Control

Chien¹,

Lewis²,

Mehrotra³

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-A wide class of multirobot control tasks involves operator interactions with individual robots. Where the robots' actions are independent, as for example in some foraging tasks, the operator can interact with robots sequentially in a round robin fashion. If the need for interaction can be detected by the robot through selfreflection, the robot could communicate its need for interaction to the operator. The resulting human-robot system would form a queuing system in which the operator is the server and the queue of robots requesting interaction, the jobs. As a queuing system, performance could be optimized using standard techniques, providing the operator's attention could be appropriately directed. An earlier study found that HumanRobot Interaction (HRI) performance was improved by communicating requests for interaction to the operator, however, a first-in-first-out (FIFO) aid showing a single request at a time led to poorer performance than one showing the entire (Open) queue. The current experiment compared Open-queue and FIFO conditions from the first experiment with a Priority-queue using a shortest job first (SJF) discipline known to maximize throughput. Performance in the Priorityqueue condition was statistically indistinguishable from the best performance for all measures except those for missed victims where it was intermediate between FIFO (best) and Open-queue. Both of the other conditions produced poorest performance on some measures. The results suggest that operator attention can be effectively scheduled allowing the use of scheduling algorithms to improve the efficiency of HRI.

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Task Switching and Multi-Robot Teams

Cited by 39 publications

References 6 publications

Human control for cooperating robot teams

Human control for cooperating robot teams

Modeling multiple human operators in the supervisory control of heterogeneous unmanned vehicles

Scheduling operator attention for Multi-Robot Control

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