DocumentCode
3601561
Title
Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning
Author
Haibin Duan ; Pei Li ; Yuhui Shi ; Xiangyin Zhang ; Changhao Sun
Author_Institution
State Key Lab. of Virtual Reality Technol. & Syst., Beihang Univ., Beijing, China
Volume
58
Issue
4
fYear
2015
Firstpage
276
Lastpage
281
Abstract
This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the concepts taught in the course and motivate them to explore relevant issues of bio-inspired optimization algorithms through interactive and collaborative learning processes. BOLE differs from other similar tools in that it places greater emphasis on fundamental concepts than on complex mathematical equations. The learning tasks using BOLE can be classified into four steps: introduction, recognition, practice, and collaboration, according to task complexity. It complements traditional classroom teaching, enhancing learning efficiency and facilitating the assessment of student achievement, as verified by its practical application in an undergraduate course “Bio-Inspired Computing.” Both objective and subjective measures were evaluated to assess the learning effectiveness.
Keywords
aerospace control; autonomous aerial vehicles; learning (artificial intelligence); mobile robots; path planning; telerobotics; MATLAB; UAV path planning; bioinspired optimization algorithms; collaborative learning processes; complex mathematical equations; interactive learning environment; interactive learning processes; unmanned aerial vehicle path planning; Algorithm design and analysis; Educational institutions; Heuristic algorithms; Mathematical model; Optimization; Path planning; Unmanned aerial vehicles; Ant colony optimization; artificial bee colony; bio-inspired optimization; particle swarm optimization; path planning; unmanned aerial vehicles (UAVs);
fLanguage
English
Journal_Title
Education, IEEE Transactions on
Publisher
ieee
ISSN
0018-9359
Type
jour
DOI
10.1109/TE.2015.2402196
Filename
7057693
Link To Document