Human Interaction With Multiple Remote Robots

Abstract

In this chapter, I review research involving remote human supervision of multiple unmanned vehicles (UVs) using command complexity as an organizing construct. Multi-UV tasks range from foraging, requiring little coordination among UVs, to formation following, in which UVs must function as a cohesive unit. Command complexity, the degree to which operator effort increases with the number of supervised UVs, is used to categorize human interaction with multiple UVs. For systems in which each UV requires the same form of attention (O(n)), effort increases linearly with the number of UVs. For systems in which the control of one UV is dependent upon another (O(>n)), additional UVs impose greater than linear increases due to the expense of coordination. For other systems, an operator interacts with an autonomously coordinating group, and effort is unaffected by group size (O(1)). Studies of human/multi-UV interaction can be roughly grouped into O(n) supervision, involving one-to-one control of individual UVs, or O(1) commanding, in which higherlevel commands are directed to a group. Research in O(n) command has centered on roundrobin control, neglect tolerance, and attention switching. Approaches to O(1) command are divided into systems using autonomous path planning only, plan libraries, human-steered planners, and swarms. Each type of system has its advantages. Less complete work in scalable displays for multiple UVs is reviewed. Mixing levels of command is probably necessary to supervise multiple UVs performing realistic tasks. Research in O(n) control is mature and can provide quantitative and qualitative guidance for design. Interaction with planners and swarms is less mature but more critical to developing effective multi-UV systems capable of performing complex tasks. IntRoductIon In recent decades, the number of mobile unmanned vehicles (UVs) deployed in field applications has risen dramatically. Their usage offers obvious advantages of reduced costs, removing humans from harm's way, or enabling entirely new applications that were previously impossible, especially when combining many such UVs into a comprehensive system. The domains of potential application are equally diverse and range from low-cost warehouse security to interplanetary exploration. New developments in commodity hardware, which serve as low-cost replacements for otherwise expensive sensing or motion capabilities, promise to further accelerate the trend toward deploying large teams of mobile UVs. This trend, however, poses a challenge for the control of such systems.

Improving Task Performance through High Level Shared Control of Multiple Robots with a Context Aware Human-Robot Interface

“…The first control method used was alternated between participants. We decided to limit the number of robots under control to three as previous research found that operator performance dropped off when controlling more the three robots using coordinated control [9].…”

“…Coordinated control approaches also have waypoint based inputs for both wheelchairs [10] and telepresence robots [3]. Coordinated control for multiple robots has typically involved serial command of robots through teleoperation, assigning waypoints, or monitoring and prosecuting targets using a single robot at a time [9]. In [11] the operator was able to control multiple robots using either low level waypoint commands or a more automated control border command in a simulated capture the flag multi-robot game.…”

“…Coordinated control is also referred to as supervisory control in the literature where robots are controlled through manual waypoints or automated path planning [9].…”

Command and Control Systems for Search and Rescue Robots

“…Extending the requirements, this means that the C2I system must include a subsystem that allows SAR mission planners to create mission plans, monitor missions and make decisions to update or abort missions [19]. This subsystem is titled as the mission planning and coordination subsystem (MPCS).…”

The effect of display type on operator prediction of future swarm states

“…Swarms typically have remarkable robustness to the failure of individual members, and are scalable to large teams. Multi-robot systems, on the other hand, typically have explicitly-represented goals, have heterogeneous capabilities, and assume different roles [1], [2]. This means that the entire mission could suffer if an individual member fails.…”