Enhancing Exploration Efficiency using Uncertainty-Aware Information Prediction

2024/12/17 by Seunghwan Kim, Heejung Shin, Kim, Seunghwan +7
Engineering · #Advanced Data Processing Techniques #Drilling and Well Engineering #FOS: Computer and information sciences #Reservoir Engineering and Simulation Methods #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2412.12825

openalex publication_date 2024/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Related

• SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation
• 4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration
• SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas
• Occupancy Map Building through Bayesian Exploration
• Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs