Title: Receding Horizon "Next-Best-View" Planner for 3D Exploration
Abstract:This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” sch...This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed at every planning step, while repetition of this procedure leads to complete exploration results. The proposed planner is capable of running online, onboard a robot with limited resources. Its high performance is evaluated in detailed simulation studies as well as in a challenging real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational complexity of the algorithm is provided and its good scaling properties enable the handling of large scale and complex problem setups.Read More
Publication Year: 2016
Publication Date: 2016-05-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 470
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot