Abstract
One of the active challenges in multi-robot missions is related to managing operator workload and situational awareness. Currently, the operators are trained to use interfaces, but in the near future this can be turned inside out: the interfaces will adapt to operators so as to facilitate their tasks. To this end, the interfaces should manage models of operators and adapt the information to their states and preferences. This work proposes a videogame-based approach to classify operator behavior and predict their actions in order to improve teleoperated multi-robot missions. First, groups of operators are generated according to their strategies by means of clustering algorithms. Second, the operators’ strategies are predicted, taking into account their models. Multiple information sources and modeling methods are used to determine the approach that maximizes the mission goal. The results demonstrate that predictions based on previous data from single operators increase the probability of success in teleoperated multi-robot missions by 19%, whereas predictions based on operator clusters increase this probability of success by 28%.
Highlights
The use of a robot team or fleet to execute time critical or modular missions, such as search and rescue operations, can offer more possibilities than using a single robot, including more robustness and adaptability
The results demonstrate that predictions based on previous data from single operators increase the probability of success in teleoperated multi-robot missions by 19%, whereas predictions based on operator clusters increase this probability of success by 28%
In [10], Cummings et al studied the capacity of operators to supervise multiple robots, concluding that there is an optimal number of agents depending on the mission: while too few imply a lack of resources to accomplish the task, too many robots might present an excessive workload for operators
Summary
The use of a robot team or fleet to execute time critical or modular missions, such as search and rescue operations, can offer more possibilities than using a single robot, including more robustness and adaptability. Yanco et al [9] analyzed the human–robot interaction in the Defense Advanced Research Projects Agency (DARPA) Robotics Challenge, where eight teams with different numbers of operators using various types of interfaces controlled humanoid robots in a complex mission. As a conclusion, they recommend reducing the number of operators, developing more integrated and immersive interfaces, decreasing the required interactions between operators and robots, and adapting the interfaces to the intended users. The positive impact of immersion and prediction on operator workload, situational awareness, and performance was demonstrated in a mission with multiple drones [6] and another mission with ground, aerial, and manipulator robots [12]
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