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

Dingdian Zhang*, Eric Schneider, Elizabeth Sklar

*Corresponding author for this work

Research output: Contribution to journalConference paperpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)261-272
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOIs
Publication statusPublished - 1 Jan 2019
Event20th Towards Autonomous Robotic Systems Conference, TAROS 2019 - London, United Kingdom
Duration: 3 Jul 20195 Jul 2019

Keywords

  • Auction mechanism
  • Multi-robot team
  • Task allocation

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