Complete 3-D dynamic coverage in energy-constrained multi-UAV sensor networks

This paper considers dynamic coverage control of multiple power-constrained agents subject to 3D rigid body kinematics. The agents are deployed to patrol a domain until the entire space has reached a satisfactory level of coverage. This is achieved through the gathering of information by a forward-f...

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Bibliographic Details
Published in:Autonomous robots 2018-04, Vol.42 (4), p.825-851
Main Authors: Bentz, William, Hoang, Tru, Bayasgalan, Enkhmurun, Panagou, Dimitra
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Batteries
Body kinematics
Charging
Collision avoidance
Computer Imaging
Computer simulation
Control
Energy management
Engineering
Hemispherical shells
Indoor environments
Mechatronics
Pattern Recognition and Graphics
Reagents
Rigid structures
Robotics
Robotics and Automation
Special Issue: Online Decision Making in Multi-Robot Coordination
Three dimensional bodies
Unmanned aerial vehicles
Vision
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Summary:This paper considers dynamic coverage control of multiple power-constrained agents subject to 3D rigid body kinematics. The agents are deployed to patrol a domain until the entire space has reached a satisfactory level of coverage. This is achieved through the gathering of information by a forward-facing sensor footprint, modelled as an anisotropic spherical sector. Coverage and collision avoidance guarantees are met by a hybrid controller consisting of four operating modes: local coverage, global coverage, waypoint scan and subdomain transfer. Energy-aware methods are encoded into the global coverage state to shift the bulk of spatial redistribution onto less constrained agents. Additionally, a novel domain partitioning strategy is used that directs individual agents to explore within concentric hemispherical shells around a centralized charging station. This results in flight paths that are guaranteed to terminate at the charging station in the limit that agent batteries expire. The efficacy of this algorithm is presented through experimental trials with three agents in an indoor environment. Simulations are provided for ten agents.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-017-9661-x