Trajectory planning with a dynamic obstacle clustering strategy using Mixed-Integer Linear Programming

Autor(es)

Battagello, Vinicius Antonio ; Soma, Nei Yoshihiro ; Magalhaes Afonso, Rubens Junqueira ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2021 AMERICAN CONTROL CONFERENCE (ACC); v. N/A, p. 6-pg., 2021-01-01.

Resumo

In this paper we propose a technique that assigns obstacles to clusters used for collision avoidance via Mixed-Integer Programming. This strategy enables a reduction in the number of binary variables used for collision avoidance, thus entailing a decrease in computational cost, which has been a hindrance to the application of Model Predictive Control approaches with Mixed-Integer Programming formulations in real-time. Moreover, the assignment of obstacles to clusters and the sizes of the clusters are decided within the same optimization problem that performs the trajectory planning, thus yielding optimal cluster choices. Simulation results are presented to illustrate an application of the proposal. (AU)

• Processo FAPESP: 16/01860-1 - Problemas de corte, empacotamento, dimensionamento de lotes, programação da produção, roteamento, localização e suas integrações em contextos industriais e logísticos
• Beneficiário: Reinaldo Morabito Neto
• Modalidade de apoio: Auxílio à Pesquisa - Temático