Influence of human reaction time in human-robot collaborative target recognition systems
A reaction time model, based on Murdock (1985), is incorporated into Bechar’s model and analyzed.
The analysis reveals new collaboration levels that are preferable when human reaction time cost is high. In these collaboration levels, the human concentrates only on objects that the robot recommended, or in other cases, only on objects that the robot did not mark. Since the human ignores one type of objects, the system reduces the total human reaction time cost resulting in better performance.
The human ignores objects by setting his cutoff point to an extreme value. The analysis shows how the system type, the human sensitivity, the probability of an object to be a target, and the time cost, all influence the phenomena of extreme cutoff point selection.
When human sensitivity is low, the human badly discriminates between targets and other objects. When the system gives high priority for not causing false alarms, the human prefers an extreme positive cutoff point, resulting in no objects marked as targets, and no false alarms. For systems that give high priority for not missing targets, an extreme negative cutoff point was preferred; resulting in all objects marked as targets and no misses. The analysis shows that the time costs affect the position of the optimal cutoff point. The phenomenon, introduced above, arises for higher human sensitivities as the time cost is higher. Furthermore, the analysis shows that collaboration with a human is less profitable in cases when the time cost is high.