Master’s Dissertation Defense, Jhielson Pimentel
We would like to congratulate Jhielson Pimentel for his new achievement, Master in Computer Science, at the UFMG.
Title: Autonomous exploration of information-based environments
Abstract
The exploration of unknown environments using autonomous mobile robots is essen-
tial for different applications, for example, search and rescue missions after natural
disasters. This is due to the performance capability of these robots in unknown envi-
ronments even when the human intervention is deprived. The main objective of this
task is to efficiently transverse the environment and build a complete and accurate map.
However, different applications may demand different exploration strategies. The sim-
plest strategy is a greedy approach which visits the closest frontier without considering
whether it will yield a significant reduction in map uncertainty. In this dissertation, we
proposed two main contributions. First, we elaborated a novel method to predict infor-
mation beyond the candidate frontiers by analysing the local structures in a building
map. In this way, it turns out possible to estimate the information gain of each frontier
candidate to be the next destination using Shanon entropy. Afterwards, the second
contribution was developed with the intention to create a unified planning which allows
the robot to identify the best destinations and, at the same time, its own paths. The
methodology was evaluated through several experiments in a simulated environment,
showing that our exploration approach is better suited for rapid exploration than the
classic Near-Frontier Exploration (NFE).
tial for different applications, for example, search and rescue missions after natural
disasters. This is due to the performance capability of these robots in unknown envi-
ronments even when the human intervention is deprived. The main objective of this
task is to efficiently transverse the environment and build a complete and accurate map.
However, different applications may demand different exploration strategies. The sim-
plest strategy is a greedy approach which visits the closest frontier without considering
whether it will yield a significant reduction in map uncertainty. In this dissertation, we
proposed two main contributions. First, we elaborated a novel method to predict infor-
mation beyond the candidate frontiers by analysing the local structures in a building
map. In this way, it turns out possible to estimate the information gain of each frontier
candidate to be the next destination using Shanon entropy. Afterwards, the second
contribution was developed with the intention to create a unified planning which allows
the robot to identify the best destinations and, at the same time, its own paths. The
methodology was evaluated through several experiments in a simulated environment,
showing that our exploration approach is better suited for rapid exploration than the
classic Near-Frontier Exploration (NFE).
Committee
Prof. Douglas Guimarães Macharet – Advisor (DCC – UFMG)
Prof. Mario Fernando Montenegro Campos – Co-Advisor (DCC – UFMG)
Prof. Armando Alves Neto (DELT – UFMG)
Prof. Luiz Chaimowicz (DCC – UFMG)
Prof. Mario Sérgio Ferreira Alvim Júnior (DCC – UFMG)
Prof. Mario Fernando Montenegro Campos – Co-Advisor (DCC – UFMG)
Prof. Armando Alves Neto (DELT – UFMG)
Prof. Luiz Chaimowicz (DCC – UFMG)
Prof. Mario Sérgio Ferreira Alvim Júnior (DCC – UFMG)