Open AccessPath planning for UAV/UGV collaborative systems in intelligent manufacturing
Author(s) -
Wang Qin,
Chen Hua,
Qiao Lu,
Tian Junwei,
Su Yu
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0688
Subject(s) - computer science , drone , unmanned ground vehicle , scheduling (production processes) , real time computing , transformation (genetics) , path (computing) , travelling salesman problem , job shop scheduling , graph , embedded system , artificial intelligence , algorithm , computer network , mathematical optimization , routing (electronic design automation) , mathematics , theoretical computer science , biochemistry , chemistry , genetics , gene , biology
Due to the development needs of the intelligent manufacturing industry, the use of drones is expanding indefinitely, and at the same time, extremely high requirements are imposed on the autonomous navigation of drones to reduce human intervention. This paper presents an UAV/ UGV scheduling problem in which the UAV needs to be recharged by the UGV in order to working in persistent tasks, and meanwhile, the UGV need to visits UGV working depots. Two different problem are presented: fixed charging sets problem (FCSP) and discrete charging sets problem (DCSP). In FCSP, charging sets are fixed, and a two‐stage travelling salesman problem method is proposed to solve the problem. DCSP is a modification of FCSP while charging segments are discrete into serval segments, an graph transformation approach was proposed to transform DCSP into GTSP, so DCSP can be resolved by using GTSP solvers. Simulation results shows that both DCSP and FCSP can ensure UAV/UGV work in persistent tasks, and the graph transformation algorithm can efficiently transform DCSP into GTSP.