Collision Avoidance Effects on the Mobility of a UAV Swarm Using Chaotic Ant Colony with Model Predictive Control

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with cha...

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Bibliographic Details
Published in:Journal of intelligent & robotic systems Vol. 93; no. 1-2; pp. 227 - 243
Main Authors: Dentler, Jan, Rosalie, Martin, Danoy, Grégoire, Bouvry, Pascal, Kannan, Somasundar, Olivares-Mendez, Miguel A., Voos, Holger
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.02.2019
Springer
Springer Nature B.V
Springer Verlag
Subjects:
Algorithms
Analysis
Ant colony optimization
Artificial Intelligence
Chaotic Dynamics
Collision avoidance
Collision dynamics
Computer Science
Control
Drone aircraft
Dynamical Systems
Economic models
Electrical Engineering
Engineering
Evacuations & rescues
Experimentation
Mathematics
Mechanical Engineering
Mechatronics
Miniature aircraft
Nonlinear Sciences
Optimization
Optimization and Control
Performance evaluation
Pheromones
Predictive control
Robot dynamics
Robotics
Robots
Systems and Control
Tracking
Unmanned aerial vehicles
Waypoints
MPC
Coverage metric
Area exploration
Chaotic dynamics
UAV
Swarm
Model predictive control
Chaotic ant colony optimization
Collision avoidance metric
CACOC
Unmanned aerial vehicles
Rössler system
Collision avoidance
Search & rescue
Mobility model
Area coverage
CA
ISSN:0921-0296, 1573-0409
Online Access:Get full text
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Summary:The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics.
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ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-018-0822-8