Authors:
Evgeni Denisov
1
;
Artur Sagitov
1
;
Konstantin Yakovlev
2
;
Kuo-Lan Su
3
;
Mikhail Svinin
4
and
Evgeni Magid
1
Affiliations:
1
Department of Intelligent Robotics, Higher Institute for Information Technology and Intelligent Systems, Kazan Federal University, 35 Kremlyovskaya street, Kazan and Russian Federation
;
2
Federal Research Center ”Computer Science and Control” of Russian Academy of Sciences, Moscow and Russian Federation
;
3
Department of Electrical Engineering, National Yunlin University of Science and Technology, Tainan City and Taiwan
;
4
Robot Dynamics and Control Laboratory, College of Information Science and Engineering, Ritsumeikan University, Noji Higashi 1-1-1, Kusatsu 525-8577 and Japan
Keyword(s):
Mobile Robot, Path Planning, Autonomous Exploration and Coverage Algorithm, Next-best-view, Dense Clutter Environment, Environment Reconstruction.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
Recent developments in 3D reconstruction systems enable to capture an environment in great detail. Several studies have provided algorithms that deal with a path-planning problem of total coverage of observable space in time-efficient manner. However, not much work was done in the area of globally optimal solutions in dense clutter environments. This paper presents a novel solution for autonomous exploration of a cluttered 2.5D environment using an unmanned ground mobile vehicle, where robot locomotion is limited to a 2D plane, while obstacles have a 3D shape. Our exploration algorithm increases coverage of 3D environment mapping comparatively to other currently available algorithms. The algorithm was implemented and tested in randomly generated dense clutter environments in MATLAB.
