Banca de DEFESA: JOAO LUIZ CARNEIRO CARVALHO

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : JOAO LUIZ CARNEIRO CARVALHO
DATE: 31/05/2023
TIME: 08:30
LOCAL: https://conferenciaweb.rnp.br/webconf/lar-laboratorio-de-robotica-ufba
TITLE:

CONTRIBUTIONS TO THE LOCALIZATION PROBLEM FOR UNMANNED GROUND VEHICLES

KEY WORDS:

Localization

Navigation

Computational intelligence

PAGES: 180
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Medidas Elétricas, Magnéticas e Eletrônicas; Instrumentação
SPECIALTY: Sistemas Eletrônicos de Medida e de Controle
SUMMARY:

Mobile robot localization is a complex task, specially in unstructured indoor en-
vironments, due to measurement noises and wrong scan-to-map association. There-
fore, the quantification of uncertainty constitutes a important part of localization
methods. The localization procedure becomes critical when the vehicle has low
confidence about its last pose estimate, situation that requires a global localization
procedure. An intuitive approach to solve the Global Localization Problem (GLP) is
to distribute several pose hypotheses all over the map and select the most likely one
according to an optimization heuristic such as Monte Carlo, Swarm Intelligence or
Evolutionary Algorithm. However, hardware limitations and environment charac-
teristics may affect the localization efficacy. In addition, the recent literature has few
studies exploring the effectiveness and computing cost of different location methods
under distinct scenarios, such as offices, corridors and large warehouses, for example.
In this context, this work proposes two contributions to the Perfect Match (PM) lo-
calization algorithm: improvement of the uncertainty estimation about the pose and
incorporation of the GLP. PM is a pose tracking algorithm that uses the scan-to-
map maching approach and stands out for its cost-effectiveness, as it presents high
accuracy and low computational cost. However, due to the kind of the algorithm,
the global localization does not perform as well as the pose tracking. Furthermore,
the estimation of the pose uncertainty could be improved, since it is based only on
map features. The magnitude of the matching error, relevant information to indicate
the quality of the estimated pose, is not taken into account by the PM implementa-
tions available in the literature. Therefore, the results presented in this work show
that, in the selected scenarios, the quantification of the uncertainty about the pose
by the proposed method suggests to be more adequate than the PM in its original
form. Regarding the GLP, different optimization heuristics based on Evolutionary
Algorithms and Swarm Intelligence were used collaboratively with the PM, such
as: Particle Swarm Optimization (PSO), Differential Evolution (DE) e Genetic Al-
goritm (GA). Using simulations and real experiments, success rate and computing
cost using different population sizes were measured. Results show that the proposed
methods present different performances for different scenarios, but those based on
Genetic Algorithm and Particle Swarm Optimization presented an average success
rate above 83%, while other methods did not reach 80%.

COMMITTEE MEMBERS:
Presidente - 1856678 - PAULO CESAR MACHADO DE ABREU FARIAS
Interno - 1938914 - TIAGO TRINDADE RIBEIRO
Interno - 2530359 - ANTONIO CARLOS LOPES FERNANDES JUNIOR
Externo à Instituição - EDUARDO OLIVEIRA FREIRE - UFS
Externo à Instituição - GUILHERME DE ALENCAR BARRETO - UFC