Open AccessDistributed multi‐vehicle task assignment in a time‐invariant drift field with obstacles
Author(s) -
Bai Xiaoshan,
Yan Weisheng,
Cao Ming,
Xue Dong
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.6125
Subject(s) - obstacle , motion planning , task (project management) , computer science , travel time , path (computing) , mathematical optimization , real time computing , invariant (physics) , algorithm , simulation , engineering , mathematics , artificial intelligence , robot , transport engineering , geography , archaeology , systems engineering , mathematical physics , programming language
This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time‐invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP‐hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal.