Market-Based Multi-robot Coalition Formation

2007 ◽  
pp. 227-236 ◽  
Author(s):  
Lovekesh Vig ◽  
Julie A. Adams
Keyword(s):  
Coalition Formation ◽  
Multi Robot

Distributed Hedonic Coalition Formation for Multi-Robot Task Allocation

2021 ◽  
Author(s):  
Ayan Dutta ◽  
Vladimir Ufimtsev ◽  
Tuffa Said ◽  
Inmo Jang ◽  
Roger Eggen
Keyword(s):  
Coalition Formation ◽  
Task Allocation ◽  
Robot Task ◽  
Hedonic Coalition Formation ◽  
Multi Robot

Multi-robot coalition formation in real-time scenarios

2012 ◽  
Vol 60 (10) ◽  
pp. 1295-1307 ◽  
Author(s):  
José Guerrero ◽  
Gabriel Oliver
Keyword(s):  
Real Time ◽  
Coalition Formation ◽  
Multi Robot

Robot coalition formation against time-extended multi-robot tasks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Usman Arif
Keyword(s):  
Coalition Formation ◽  
Design Methodology ◽  
Directed Acyclic Graph ◽  
Deadlock Detection ◽  
Solution Quality ◽  
Content Type ◽  
Hard Time ◽  
Robot Tasks ◽  
Multi Robot

PurposeMulti-robot coalition formation (MRCF) refers to the formation of robot coalitions against complex tasks requiring multiple robots for execution. Situations, where the robots have to participate in multiple coalitions over time due to a large number of tasks, are called Time-extended MRCF. While being NP-hard, time-extended MRCF also holds the possibility of resource deadlocks due to any cyclic hold-and-wait conditions among the coalitions. Existing schemes compromise on solution quality to form workable, deadlock-free coalitions through instantaneous or incremental allocations.Design/methodology/approachThis paper presents an evolutionary algorithm (EA)-based task allocation framework for improved, deadlock-free solutions against time-extended MRCF. The framework simultaneously allocates multiple tasks, allowing the robots to participate in multiple coalitions within their schedule. A directed acyclic graph–based representation of robot plans is used for deadlock detection and avoidance.FindingsAllowing the robots to participate in multiple coalitions within their schedule, significantly improves the allocation quality. The improved allocation quality of the EA is validated against two auction schemes inspired by the literature.Originality/valueTo the best of the author's knowledge, this is the first framework which simultaneously considers multiple MR tasks for deadlock-free allocation while allowing the robots to participate in multiple coalitions within their plans.

Parallel multi-objective multi-robot coalition formation

2015 ◽  
Vol 42 (21) ◽  
pp. 7797-7811 ◽  
Author(s):  
Manoj Agarwal ◽  
Nitin Agrawal ◽  
Shikhar Sharma ◽  
Lovekesh Vig ◽  
Naveen Kumar
Keyword(s):  
Coalition Formation ◽  
Multi Objective ◽  
Multi Robot
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