Coordination Demand in Human Control of Heterogeneous Robot

Abstract

The performance of human-robot teams is complex and multifaceted reflecting the capabilities of the robots, the operator(s), and the quality of their interactions. Recent efforts to define common metrics for human-robot interaction (Steinfeld et al., 2006) have favored sets of metric classes to measure the effectiveness of the system’s constituents and their interactions as well as the system’s overall performance. In this chapter we follow this approach to develop measures characterizing the demand imposed by tasks requiring cooperation among heterogeneous robots. Applications for multirobot systems (MRS) such as interplanetary construction or cooperating uninhabited aerial vehicles will require close coordination and control between human operator(s) and teams of robots in uncertain environments. Human supervision will be needed because humans must supply the perhaps changing goals that direct MRS activity. Robot autonomy will be needed because the aggregate decision making demands of a MRS are likely to exceed the cognitive capabilities of a human operator. Autonomous cooperation among robots, in particular, will likely be needed because it is these activities (Gerkey & Mataric, 2004) that theoretically impose the greatest decision making load. Controlling multiple robots substantially increases the complexity of the operator’s task because attention must constantly be shifted among robots in order to maintain situation awareness (SA) and exert control. In the simplest case an operator controls multiple independent robots interacting with each as needed. A search task in which each robot searches its own region would be of this category although minimal coordination might be required to avoid overlaps and prevent gaps in coverage. Control performance at such tasks can be characterized by the average demand of each robot on human attention (Crandal et al., 2005). Under these conditions increasing robot autonomy should allow robots to be neglected for longer periods of time making it possible for a single operator to control more robots. Because of the need to share attention between robots in MRS, teloperation can only be used for one robot out of a team (Nielsen et al., 2003) or as a selectable mode (Parasuraman et al., 2005). Some variant of waypoint control has been used in most of the MRS studies we have reviewed (Crandal et al., 2005, Nielsen et al., 2003, Parasuraman et al., 2005, Trouvain & Wolf, 2002) with differences arising primarily in behavior upon reaching a waypoint. A more fully autonomous mode has typically been included involving things such as search of