Enhancing Exploration Efficiency using Uncertainty-Aware Information Prediction

Autor:	Kim, Seunghwan, Shin, Heejung, Yim, Gaeun, Kim, Changseung, Oh, Hyondong
Rok vydání:	2024
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	Autonomous exploration is a crucial aspect of robotics, enabling robots to explore unknown environments and generate maps without prior knowledge. This paper proposes a method to enhance exploration efficiency by integrating neural network-based occupancy grid map prediction with uncertainty-aware Bayesian neural network. Uncertainty from neural network-based occupancy grid map prediction is probabilistically integrated into mutual information for exploration. To demonstrate the effectiveness of the proposed method, we conducted comparative simulations within a frontier exploration framework in a realistic simulator environment against various information metrics. The proposed method showed superior performance in terms of exploration efficiency. Comment: 7pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.12825 Zobrazit plný text záznamu View this record from Arxiv