论文标题
通过蒙特卡洛视图质量渲染的协调空中机器人勘探
Coordinated Aerial-Ground Robot Exploration via Monte-Carlo View Quality Rendering
论文作者
论文摘要
我们为地面机器人团队提供了一个框架，以探索大型，非结构化和未知的环境。在这样的探索问题中，现有的促进启发式方法的有效性通常会随着环境的大小和复杂性而缩小。这项工作提出了一个新颖的框架，结合了增量边界分布，目标选择与蒙特 - 卡洛视图质量渲染以及自动不同的信息增益度量，以提高勘探效率。通过多个复杂的环境进行模拟，我们证明了所提出的方法有效地利用了协作的空中和地面机器人，一贯指导代理来提供信息的观点，改善了勘探路径的信息增长并减少了计划时间。
We present a framework for a ground-aerial robotic team to explore large, unstructured, and unknown environments. In such exploration problems, the effectiveness of existing exploration-boosting heuristics often scales poorly with the environments' size and complexity. This work proposes a novel framework combining incremental frontier distribution, goal selection with Monte-Carlo view quality rendering, and an automatic-differentiable information gain measure to improve exploration efficiency. Simulated with multiple complex environments, we demonstrate that the proposed method effectively utilizes collaborative aerial and ground robots, consistently guides agents to informative viewpoints, improves exploration paths' information gain, and reduces planning time.
