Invited Talk: UCRL and Autonomous Exploration

2012 ◽  
pp. 1-1
Author(s):  
Peter Auer
Keyword(s):  
Autonomous Exploration

Autonomous Exploration and Mapping of Flooded Sinkholes

2009 ◽  
Vol 29 (6) ◽  
pp. 748-774 ◽  
Author(s):  
Nathaniel Fairfield ◽  
George Kantor ◽  
Dominic Jonak ◽  
David Wettergreen
Keyword(s):  
Autonomous Exploration

A Simple buoyant craft for autonomous exploration

2002 ◽  
Vol 50 (3) ◽  
pp. 167-172
Author(s):  
M.Dale Stokes
Keyword(s):  
Autonomous Exploration

Science in the Classroom: Finding a Balance Between Autonomous Exploration and Teacher-Led Instruction in Preschool Settings

2011 ◽  
Vol 22 (6) ◽  
pp. 970-988 ◽  
Author(s):  
Irena Nayfeld ◽  
Kimberly Brenneman ◽  
Rochel Gelman
Keyword(s):  
Autonomous Exploration

scholarly journals Autonomous exploration with prediction of the quality of vision-based localization

2017 ◽  
Vol 50 (1) ◽  
pp. 10274-10279 ◽  
Author(s):  
Hélène Roggeman ◽  
Julien Marzat ◽  
Anthelme Bernard-Brunel ◽  
Guy Le Besnerais
Keyword(s):  
Autonomous Exploration ◽  
Quality Of Vision ◽  
Vision Based Localization

Information-driven Fast Marching Autonomous Exploration with Aerial Robots

2021 ◽  
pp. 1-1
Author(s):  
Ping Zhong ◽  
Bolei Chen ◽  
Siyi Lu ◽  
Xiaoxi Meng ◽  
Yixiong Liang
Keyword(s):  
Fast Marching ◽  
Autonomous Exploration ◽  
Aerial Robots

Research on autonomous exploration motion planning method of mobile robots for unstructured scenarios

2021 ◽  
Author(s):  
Xuehao Sun ◽  
Shuchao Deng ◽  
Baohong Tong ◽  
Shuang Wang ◽  
Shuai Ma ◽  
...  
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Planning Method ◽  
Autonomous Exploration

Three-dimensional Terrain Aware Autonomous Exploration for Subterranean and Confined Spaces

2021 ◽  
Author(s):  
Hector Azpurna ◽  
Mario F. M. Campos ◽  
Douglas G. Macharet
Keyword(s):  
Three Dimensional ◽  
Autonomous Exploration ◽  
Confined Spaces

FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs

2021 ◽  
pp. 1-12
Author(s):  
Á. Martínez Novo ◽  
Liang Lu ◽  
Pascual Campoy
Keyword(s):  
State Of The Art ◽  
Micro Aerial Vehicles ◽  
Autonomous Exploration ◽  
Signed Distance ◽  
Aerial Vehicles ◽  
3D Environment ◽  
Frontier Points ◽  
Computational Resources ◽  
Next Best View ◽  
Signed Distance Field

This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.

Autonomous Exploration, Mapping and Pathfinding using Sensor Fusion with a PhantomX MKIII

2019 ◽  
pp. 53-60
Author(s):  
Christoph Brandau ◽  
Jan-Gerrit Jaeger ◽  
Marco Steinbrink ◽  
Dietmar Tutsch
Keyword(s):  
Sensor Fusion ◽  
Autonomous Exploration
Sign in / Sign up

Export Citation Format

Share Document