Task allocation and trajectory planning for multiple agents in the presence of obstacle and connectivity constraints with mixed‐integer linear programming

Summary This article addresses the problem of maneuvering multiple agents that must visit a number of target sets, while enforcing connectivity constraints and avoiding obstacle as well as interagent collisions. The tool to cope with the problem is a formulation of model predictive control including...

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Bibliographic Details
Published in:International journal of robust and nonlinear control Vol. 30; no. 14; pp. 5464 - 5491
Main Authors: Afonso, Rubens J.M., Maximo, Marcos R.O.A., Galvão, Roberto K.H.
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 25.09.2020
Subjects:
Computer simulation
connectivity maintenance
cooperative multiple agents
Integer programming
Integers
Linear programming
mixed‐integer linear programming
Multiagent systems
obstacle avoidance
Predictive control
Trajectory planning
ISSN:1049-8923, 1099-1239
Online Access:Get full text
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Summary:Summary This article addresses the problem of maneuvering multiple agents that must visit a number of target sets, while enforcing connectivity constraints and avoiding obstacle as well as interagent collisions. The tool to cope with the problem is a formulation of model predictive control including binary decision variables. In this regard, two mixed‐integer linear programming formulations are presented, considering a trade‐off between optimality and scalability between them. Simulation results are also shown to illustrate the main features of the proposed approaches.
Bibliography:Funding information
Alexander von Humboldt‐Stiftung, Conselho Nacional de Desenvolvimento Científico e Tecnológico, 303393/2018‐1; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, 88881.145490/2017‐01; Fundação de Amparo à Pesquisa do Estado de São Paulo, 2016/03647‐3
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.5092