.png){kind=icon}
返回
A Common Optimization Framework for Multi-Robot Exploration and Coverage in 3D Environments
.png){kind=icon}
OA
AI
DOI：10.1007/s10846-020-01255-4.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives.
Keyword:
Multi-Robot systems
Cooperative exploration
Optimal coverage
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
2.8
论文数:
3.9K
被引数:
6.9K
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容