• 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