Monte Carlo algorithm for trajectory optimization based on Markovian readings

Autor(es)

Dias, Ronaldo ^[1] ; Garcia, Nancy L. ^[1] ; Zambom, Adriano Z. ^[1]

Afiliação do(s) autor(es)

^[1] IMECC UNICAMP, Dept Estat, BR-13081970 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS; v. 51, n. 1, p. 305-321, JAN 2012.
• Citações Web of Science: 2

Resumo

This paper describes an efficient algorithm to find a smooth trajectory joining two points A and B with minimum length constrained to avoid fixed subsets. The basic assumption is that the locations of the obstacles are measured several times through a mechanism that corrects the sensors at each reading using the previous observation. The proposed algorithm is based on the penalized nonparametric method previously introduced that uses confidence ellipses as a fattening of the avoidance set. In this paper we obtain consistent estimates of the best trajectory using Monte Carlo construction of the confidence ellipse. (AU)

• Processo FAPESP: 06/02095-5 - Estimacao nao parametrica da trajetoria percorrida por um veiculo autonomo.
• Beneficiário: Adriano Zanin Zambom
• Linha de fomento: Bolsas no Brasil - Mestrado