Objective: The aim of this study was to evaluate two models for adjusting autonomy in mobile robots to find out the best way for the operator to interact with the system with as many robots as possible. The first model is the most used in mobile robots; the second proposes a flexible autonomy management.

Background: There are different ways of adjusting the autonomy level in man-machine systems: adjustable autonomy, in which the operator has the initiative over the autonomy level; adaptive autonomy, in which the autonomy level is adjusted depending on the task and context; and mixed initiatives. One of the drawbacks of using adjustable autonomy is that it is claimed not to be flexible enough, resulting in a high operator workload. We propose and evaluate a flexible adjustable autonomy model for robot-team supervision.

Method: Two experiments were designed to test the scalability and performance of the man-machine system with two alternative configurations for the autonomy management. The independent variable is the number of robots, and the measured variable is the man-machine system performance. The experiments are between subjects. We have used ANOVA and Bonferroni post hoc analysis for analyzing the results.

Results: On the basis of these analyses, we conclude that a flexible adjustable autonomy model results in better performance than the classic, rigid one, in which the operator directly chooses the autonomy level.

Conclusion: Flexible autonomy adjustment permits one operator to control a team of robots with better results in terms of performance and robot use, as he or she can directly act at the error level, leaving the responsibility of readjusting and resuming the task to the system and hence reducing the operator’s workload.

Application: The results can be applied to exploration robotics, mainly, in which one operator controls a team of robots. In general, these principles can be extended to other single-man/multiple-machine systems.

Search results

Impact of Two Adjustable-Autonomy Models on the Scalability of Single-Human/Multiple-Robot Teams for Exploration Missions