TY - JOUR

T1 - Event- and time-triggered dynamic task assignments for multiple vehicles

T2 - Special Issue on Multi-Robot and Multi-Agent Systems

AU - Bai, Xiaoshan

AU - Cao, Ming

AU - Yan, Weisheng

PY - 2020/5

Y1 - 2020/5

N2 - We study the dynamic task assignment problem in which multiple dispersed vehicles are employed to visit a set of targets. Some targets’ locations are initially known and the others are dynamically randomly generated during a finite time horizon. The objective is to visit all the target locations while trying to minimize the vehicles’ total travel time. Based on existing algorithms used to deal with static multi-vehicle task assignment, two types of dynamic task assignments, namely event-triggered and time-triggered, are studied to investigate what the appropriate time instants should be to change in real time the assignment of the target locations in response to the newly generated target locations. Furthermore, for both the event- and time-triggered assignments, we propose several algorithms to investigate how to distribute the newly generated target locations to the vehicles. Extensive numerical simulations are carried out which show better performance of the event-triggered task assignment algorithms over the time-triggered algorithms under different arrival rates of the newly generated target locations.

AB - We study the dynamic task assignment problem in which multiple dispersed vehicles are employed to visit a set of targets. Some targets’ locations are initially known and the others are dynamically randomly generated during a finite time horizon. The objective is to visit all the target locations while trying to minimize the vehicles’ total travel time. Based on existing algorithms used to deal with static multi-vehicle task assignment, two types of dynamic task assignments, namely event-triggered and time-triggered, are studied to investigate what the appropriate time instants should be to change in real time the assignment of the target locations in response to the newly generated target locations. Furthermore, for both the event- and time-triggered assignments, we propose several algorithms to investigate how to distribute the newly generated target locations to the vehicles. Extensive numerical simulations are carried out which show better performance of the event-triggered task assignment algorithms over the time-triggered algorithms under different arrival rates of the newly generated target locations.

KW - TARGET ASSIGNMENT

KW - ALLOCATION

KW - ALGORITHM

KW - TAXONOMY

U2 - 10.1007/s10514-020-09912-1

DO - 10.1007/s10514-020-09912-1

M3 - Article

VL - 44

SP - 877

EP - 888

JO - Autonomous Robots

JF - Autonomous Robots

SN - 0929-5593

IS - 5

ER -