Paper Info
Title
Unsupervised learning-based flexible framework for surveillance planning with aerial vehicles.
Abstract
The herein studied problem is motivated by practical needs of our participation in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 in which a team of unmanned aerial vehicles (UAVs) is requested to collect objects in the given area as quickly as possible and score according to the rewards associated with the objects. The mission time is limited, and the most time-consuming operation is the collection of the objects themselves. Therefore, we address the problem to quickly identify the most valuable objects as surveillance planning with curvature-constrained trajectories. The problem is formulated as a multivehicle variant of the Dubins traveling salesman problem with neighborhoods (DTSPN). Based on the evaluation of existing approaches to the DTSPN, we propose to use unsupervised learning to find satisfiable solutions with low computational requirements. Moreover, the flexibility of unsupervised learning allows considering trajectory parametrization that better fits the motion constraints of the utilized hexacopters that are not limited by the minimal turning radius as the Dubins vehicle. We propose to use Bezier curves to exploit the maximal vehicle velocity and acceleration limits. Besides, we further generalize the proposed approach to 3D surveillance planning. We report on evaluation results of the developed algorithms and experimental verification of the planned trajectories using the real UAVs utilized in our participation in MBZIRC 2017.
Year
DOI
Venue
2019
10.1002/rob.21823
JOURNAL OF FIELD ROBOTICS
Keywords
DocType
Volume
aerial robotics,computing architectures,planning
Journal
36.0
Issue
ISSN
Citations 
SP1.0
1556-4959
6
PageRank 
References 
Authors
0.52
0
4
Name
Order
Citations
PageRank
Jan Faigl133642.34
Petr Vána2116.36
Robert Penicka3338.06
Martin Saska426634.01