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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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
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Abstract	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.
AbstractList	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. 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. Keywords Unmanned aerial vehicles * UAV * Swarm * Search & rescue * Area exploration * Chaotic ant colony optimization * Chaotic dynamics * Mobility model * Rossler system * CACOC * Area coverage * Collision avoidance * CA * Model predictive control * MPC * Coverage metric * Collision avoidance metric
Audience	Academic
Author	Dentler, Jan Olivares-Mendez, Miguel A. Voos, Holger Bouvry, Pascal Danoy, Grégoire Rosalie, Martin Kannan, Somasundar
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Keywords	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
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Snippet	The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance...
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SubjectTerms	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
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Title	Collision Avoidance Effects on the Mobility of a UAV Swarm Using Chaotic Ant Colony with Model Predictive Control
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