用不确定性感知预测提升机器人探索效率
Enhancing Exploration Efficiency using Uncertainty-Aware Information Prediction
- 结合神经网络预测与贝叶斯不确定性，优化探索决策
- 在真实模拟环境中探索效率优于多种信息度量方法
- 适合需高效自主探索的移动机器人应用
自主探索是机器人技术的关键环节，使机器人能够在无先验知识的情况下探索未知环境并生成地图。本文提出一种方法，通过将基于神经网络的占据栅格地图预测中的不确定性，以概率方式融入互信息计算，从而提升探索效率。为验证该方法的有效性，我们在真实模拟器环境中，基于前缘探索框架与多种信息度量进行了对比仿真。结果表明，所提方法在探索效率方面表现更优。
原文摘要 · Abstract (English)
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.
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