A cross-landscape evaluation of multi-robot team performance in static task-allocation domains

TY - JOUR

T1 - A cross-landscape evaluation of multi-robot team performance in static task-allocation domains

AU - Zhang, Dingdian

AU - Schneider, Eric

AU - Sklar, Elizabeth

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The performance of a multi-robot team varies when certain environmental parameters change. The study presented here examines the performance of four task allocation mechanisms, compared across a mission landscape that is defined by a set of environmental conditions. The landscape is categorised by three dimensions: (1) single-robot versus multi-robot tasks; (2) independent versus constrained task correspondence; and (3) static versus dynamic allocation of tasks with respect to mission execution. Two different task scenarios and two different starting formations were implemented with each environmental condition. Experiments were conducted on teams of simulated and physical robots, to demonstrate the portability of the results. This paper investigates the "static allocation" portion of the mission landscape, filling in a gap that has not been investigated previously. Experimental results are presented which confirm that the previous conclusion still holds: there is no single task allocation mechanism that consistently ranks best in performance when tasks are executed.

AB - The performance of a multi-robot team varies when certain environmental parameters change. The study presented here examines the performance of four task allocation mechanisms, compared across a mission landscape that is defined by a set of environmental conditions. The landscape is categorised by three dimensions: (1) single-robot versus multi-robot tasks; (2) independent versus constrained task correspondence; and (3) static versus dynamic allocation of tasks with respect to mission execution. Two different task scenarios and two different starting formations were implemented with each environmental condition. Experiments were conducted on teams of simulated and physical robots, to demonstrate the portability of the results. This paper investigates the "static allocation" portion of the mission landscape, filling in a gap that has not been investigated previously. Experimental results are presented which confirm that the previous conclusion still holds: there is no single task allocation mechanism that consistently ranks best in performance when tasks are executed.

KW - Auction mechanism

KW - Multi-robot team

KW - Task allocation

UR - http://www.scopus.com/inward/record.url?scp=85073909098&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-25332-5_23

DO - 10.1007/978-3-030-25332-5_23

M3 - Conference paper

AN - SCOPUS:85073909098

SN - 0302-9743

SP - 261

EP - 272

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 20th Towards Autonomous Robotic Systems Conference, TAROS 2019

Y2 - 3 July 2019 through 5 July 2019

ER -