Strategies for Patrolling Missions with Multiple UAVs

This paper proposes a set of strategies for the patrolling problem using multiple UAVs and as a result, improving our original NC-Drone algorithm. We present four strategies: Watershed Strategy, Time-based Strategies, Evaporation Strategy, and Communication-Frequency Strategy. The novel strategies consider important aspects of the patrolling movement, such as time, uncertainty, and communication. Results point out that these strategies improve the centralized version of the NC-Drone considering the uniform distribution of visits and drastically reduce in 76% the standard deviation, making the algorithm more stable. Based on the results, we found that there is a trade-off between the evaluated metrics, making it necessary to perform a large number of turns to obtain a more spatially distributed patrolling. We also present a series of strategy combinations, achieving slight improvements as more combinations are adopted. The resulting algorithm from the combination of all strategies reduces the communication frequency in 50 times and outperforms the original version of the NC-Drone in 4.5%.

[1]  Cyrill Stachniss,et al.  UAV-based crop and weed classification for smart farming , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[2]  R. Pfeifer,et al.  Self-Organization, Embodiment, and Biologically Inspired Robotics , 2007, Science.

[3]  Aníbal Ollero,et al.  Area Partition for Coastal Regions with Multiple UAS , 2017, J. Intell. Robotic Syst..

[4]  Jan Zelenka,et al.  Insect pheromone strategy for the robots coordination — Reaction on loss communication , 2014, 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI).

[5]  Artsiom Stalmakou.  UAV/UAS path planning for ice management information gathering , 2011 .

[6]  Bernhard Rinner,et al.  Information merging in multi-UAV cooperative search , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Gökhan Ince,et al.  Multi-agent search strategy based on digital pheromones for UAVs , 2016, 2016 24th Signal Processing and Communication Application Conference (SIU).

[8]  Simon Lacroix,et al.  Monitoring the evolution of clouds with UAVs , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Serge Chaumette,et al.  From Random Process to Chaotic Behavior in Swarms of UAVs , 2016, DIVANet@MSWiM.

[10]  Aníbal Ollero,et al.  Journal of Intelligent & Robotic Systems manuscript No. (will be inserted by the editor) Experimental Results in Multi-UAV Coordination for Disaster Management and Civil Security Applications , 2022 .

[11]  Yan Li,et al.  Research on the coverage path planning of UAVs for polygon areas , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[12]  Giorgio C. Buttazzo,et al.  Coverage Path Planning for UAVs Photogrammetry with Energy and Resolution Constraints , 2016, J. Intell. Robotic Syst..

[13]  Anibal Ollero,et al.  Spiral-like coverage path planning for multiple heterogeneous UAS operating in coastal regions , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[14]  Howie Choset,et al.  Coverage for robotics – A survey of recent results , 2001, Annals of Mathematics and Artificial Intelligence.

[15]  Aníbal Ollero,et al.  Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing , 2017, Sensors.

[16]  Robert Fitch,et al.  Adversarial patrolling with reactive point processes , 2016 .

[17]  Debasish Ghose,et al.  Learning-based preferential surveillance algorithm for persistent surveillance by unmanned aerial vehicles , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[18]  Hyochoong Bang,et al.  Waypoint Planning Algorithm Using Cost Functions for Surveillance , 2010 .

[19]  Richard E. Korf,et al.  Real-Time Heuristic Search , 1990, Artif. Intell..

[20]  Pablo A. Sampaio,et al.  New Patrolling Strategies with Short-Range Perception , 2016, 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR).

[21]  Aníbal Ollero,et al.  Cooperative Large Area Surveillance with a Team of Aerial Mobile Robots for Long Endurance Missions , 2013, J. Intell. Robotic Syst..

[22]  Maurizio Piaggio,et al.  Programming Real Time Distributed Multiple Robotic Systems , 1999, RoboCup.

[23]  Hyochoong Bang,et al.  Waypoint guidance of cooperative UAVs for intelligence, surveillance, and reconnaissance , 2009, 2009 IEEE International Conference on Control and Automation.

[24]  Paulo R. Ferreira,et al.  Terrain Coverage with UAVs: Real-Time Search and Geometric Approaches Applied to an Abstract Model of Random Events , 2016, 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR).

[25]  A. Ollero,et al.  Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms , 2004, DARS.

[26]  Paulo Roberto Ferreira,et al.  An Energy-Aware Real-Time Search Approach for Cooperative Patrolling Missions with Multi-UAVs , 2018, 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE).

[27]  Daniele Nardi,et al.  Field coverage and weed mapping by UAV swarms , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[28]  Dario Floreano,et al.  Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies , 2008 .

[29]  Henry Leung,et al.  Cooperative path planner for UAVs using ACO algorithm with Gaussian distribution functions , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[30]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[31]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  Simin Nadjm-Tehrani,et al.  Mobility Models for UAV Group Reconnaissance Applications , 2006, 2006 International Conference on Wireless and Mobile Communications (ICWMC'06).

[33]  Yann Chevaleyre.  Theoretical analysis of the multi-agent patrolling problem , 2004 .

[34]  Anibal Ollero,et al.  Area decomposition, partition and coverage with multiple remotely piloted aircraft systems operating in coastal regions , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[35]  Izhak Rubin,et al.  A framework and analysis for cooperative search using UAV swarms , 2004, SAC '04.

[36]  Israel A. Wagner,et al.  On-Line Graph Searching by a Smell-Oriented Vertex Process , 1997 .

[37]  Tomáš Kasanický,et al.  Insect Pheromone Strategy for the Robots Coordination , 2014 .

[38]  Debasish Ghose,et al.  A Heuristic Learning Algorithm for Preferential Area Surveillance by Unmanned Aerial Vehicles , 2017, J. Intell. Robotic Syst..

[39]  Sebastian Thrun,et al.  Efficient Exploration In Reinforcement Learning , 1992 .

[40]  Serge Beucher,et al.  The Morphological Approach to Segmentation: The Watershed Transformation , 2018, Mathematical Morphology in Image Processing.

[41]  Håvard Lægreid Andersen.  Path Planning for Search and Rescue Mission using Multicopters , 2014 .

[42]  Antonio Barrientos,et al.  Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots , 2011, J. Field Robotics.

[43]  Jan Zelenka,et al.  Outdoor UAV control and coordination system supported by biological inspired method , 2014, 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD).

[44]  Timothy W. McLain,et al.  Cooperative forest fire surveillance using a team of small unmanned air vehicles , 2006, Int. J. Syst. Sci..

[45]  David Feil-Seifer,et al.  A distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[46]  Giorgio Cannata,et al.  A Minimalist Algorithm for Multirobot Continuous Coverage , 2011, IEEE Transactions on Robotics.

[47]  Giorgio C. Buttazzo,et al.  Energy-Aware Spiral Coverage Path Planning for UAV Photogrammetric Applications , 2018, IEEE Robotics and Automation Letters.

[48]  Emanuele Frontoni,et al.  A Visual Global Positioning System for Unmanned Aerial Vehicles Used in Photogrammetric Applications , 2011, J. Intell. Robotic Syst..

[49]  P. B. Sujit,et al.  Multiple UAV area decomposition and coverage , 2013, 2013 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA).

[50]  Sven Koenig,et al.  Terrain coverage with ant robots: a simulation study , 2001, AGENTS '01.

[51]  Pascal Bouvry,et al.  Area exploration with a swarm of UAVs combining deterministic chaotic ant colony mobility with position MPC , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[52]  Reid G. Simmons,et al.  Easy and Hard Testbeds for Real-Time Search Algorithms , 1996, AAAI/IAAI, Vol. 1.

[53]  José L. Verdegay,et al.  Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction , 2016, Expert Syst. Appl..

[54]  Deshi Li,et al.  Energy-optimal coverage path planning on topographic map for environment survey with unmanned aerial vehicles , 2016 .

[55]  Stefano Carpin,et al.  Deploying teams of heterogeneous UAVs in cooperative two-level surveillance missions , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[56]  Amir Pirzadeh,et al.  A unified solution to coverage and search in explored and unexplored terrains using indirect control , 1990, Proceedings., IEEE International Conference on Robotics and Automation.