Human Machine Collaborative Support Scheduling System of Intelligence Information from Multiple Unmanned Aerial Vehicles Based on Eye Tracker

Many human-machine collaborative support scheduling systems are used to aid human decision making by providing several optimal scheduling algorithms that do not take operator’s attention into consideration.However, the current systems should take advantage of the operator’s attention to obtain the o...

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Bibliographic Details
Published in:	Shanghai jiao tong da xue xue bao Vol. 22; no. 3; pp. 322 - 328
Main Author:	简立轩尹栋沈林成牛轶峰
Format:	Journal Article
Language:	English
Published:	Shanghai Shanghai Jiaotong University Press 01-06-2017 Springer Nature B.V
Subjects:	Algorithms Architecture Collaboration Completion time Computer applications Computer Science Decision making Electrical Engineering Engineering Eye Eye movements Heuristic Heuristic methods Heuristic scheduling Information systems Intelligence Life Sciences Lower bounds Materials Science Object recognition Production scheduling Scheduling algorithms Support systems Target recognition Unmanned aerial vehicles Upper bounds human machine interaction collaborative support scheduling system TP 391.4 polynomial time heuristics receding horizon optimization policy eye tracker
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Summary:	Many human-machine collaborative support scheduling systems are used to aid human decision making by providing several optimal scheduling algorithms that do not take operator’s attention into consideration.However, the current systems should take advantage of the operator’s attention to obtain the optimal solution.In this paper, we innovatively propose a human-machine collaborative support scheduling system of intelligence information from multi-UAVs based on eye-tracker. Firstly, the target recognition algorithm is applied to the images from the multiple unmanned aerial vehicles(multi-UAVs) to recognize the targets in the images. Then,the support system utilizes the eye tracker to gain the eye-gaze points which are intended to obtain the focused targets in the images. Finally, the heuristic scheduling algorithms take both the attributes of targets and the operator’s attention into consideration to obtain the sequence of the images. As the processing time of the images collected by the multi-UAVs is uncertain, however the upper bounds and lower bounds of the processing time are known before. So the processing time of the images is modeled by the interval processing time. The objective of the scheduling problem is to minimize mean weighted completion time. This paper proposes some new polynomial time heuristic scheduling algorithms which firstly schedule the images including the focused targets. We conduct the scheduling experiments under six different distributions. The results indicate that the proposed algorithm is not sensitive to the different distributions of the processing time and has a negligible computational time. The absolute error of the best performing heuristic solution is only about 1%. Then, we incorporate the best performing heuristic algorithm into the human-machine collaborative support systems to verify the performance of the system.
Bibliography:	31-1943/U
ISSN:	1007-1172 1995-8188
DOI:	10.1007/s12204-017-1838-0