Human Interaction With Multiple Remote Robots

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 higher-level commands are directed to a group. Research in O( n) command has centered on round-robin 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.

Imaging tasks scheduling for high-altitude airship in emergency condition based on energy-aware strategy

Aiming to the imaging tasks scheduling problem on high-altitude airship in emergency condition, the programming models are constructed by analyzing the main constraints, which take the maximum task benefit and the minimum energy consumption as two optimization objectives. Firstly, the hierarchy architecture is adopted to convert this scheduling problem into three subproblems, that is, the task ranking, value task detecting, and energy conservation optimization. Then, the algorithms are designed for the sub-problems, and the solving results are corresponding to feasible solution, efficient solution, and optimization solution of original problem, respectively. This paper makes detailed introduction to the energy-aware optimization strategy, which can rationally adjust airship's cruising speed based on the distribution of task's deadline, so as to decrease the total energy consumption caused by cruising activities. Finally, the application results and comparison analysis show that the proposed strategy and algorithm are effective and feasible.