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Local interaction and navigation guidance for hunters drones: a chase behavior approach with real-time tests

Published online by Cambridge University Press:  24 January 2022

C. de Souza ,
P. Castillo Open the ORCID record for P. Castillo [Opens in a new window]  and
B. Vidolov
C. de Souza
Affiliation:
Université de Technologie de Compiègne, CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), CS 60 319 – 60 203 Compiègne Cedex, France
P. Castillo*
Affiliation:
Université de Technologie de Compiègne, CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), CS 60 319 – 60 203 Compiègne Cedex, France
B. Vidolov
Affiliation:
Université de Technologie de Compiègne, CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), CS 60 319 – 60 203 Compiègne Cedex, France
*
*Corresponding author. E-mail: castillo@hds.utc.fr
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Abstract

A behavioral-based strategy for cooperative hunting using drones is proposed in this paper. In this decentralized scheme, each drone acts as an individual agent computing its guidance strategy toward the target based on the relative position of its neighbors without the use of direct communication. The algorithm is based on the deviated pure pursuit methodology, and the emerged behavior mimics a natural hunting formation. Simulations and real-time experiments with varying conditions were carried out to validate the effectiveness of the proposed hunting scheme. Videos of the system in action can be seen on: https://youtu.be/g2dODbd6ZLA.

Keywords

hunter dronesautonomous navigationtrackingpursuitreal-time tests

Information

Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2697 - 2715
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Lefebvre, T. and Dubot, T., “Conceptual Design Study of an Anti-drone Drone,” 16th AIAA Aviation Technology, Integration, and Operations Conference, Washinton, DC, USA (2016) pp. 1–14.Google Scholar
Allen, R. L., Quadrotor Intercept Trajectory Planning and Simulation Thesis (Naval Postgraduate School, Monterey, CA, USA, 2017).Google Scholar
Solinger, D., Ehlert, P. and Rothkrantz, L., Creating a dogfight agent, Technical Report DKS05–01/ICE 10 (Delft University of Technology, Netherlands, 2005).Google Scholar
Hert, D., Autonomous Predictive Interception of a Flying Target by an Unmanned Aerial Vehicle Thesis (Czech Technical University in Prague, Prague, Czech Republic, 2018).Google Scholar
Arneberg, J. T., Guidance Laws for Partially-Observable UAV Interception Based on Linear Covariance Analysis Thesis (Massachusetts Institute of Technology, Massachusetts, USA, 2018).Google Scholar
Yulan, H., Qisong, Z. and Pengfei, X., “Study on multi-robot cooperation stalking using finite state machine,” Procedia Engineering 29, 3502–3506 (2012). https://doi.org/10.1016/j.proeng.2012.01.520 CrossRefGoogle Scholar
Yamaguchi, H., “A Cooperative Hunting Behavior by Multiple Nonholonomic Mobile Robots,” SMC’98 IEEE International Conference on Systems, Man, and Cybernetics, vol. 4 (1998) pp. 3347–3352.Google Scholar
Wu, Z., Cao, Z., Yu, Y., Pang, L., Zhou, C. and Chen, E., “A Multi-robot Cooperative Hunting Approach Based on Dynamic Prediction of Target Motion,” 2017 IEEE International Conference on Robotics and Biomimetics, Macau, China (2017) pp. 587–592.Google Scholar
Weitzenfeld, A., Vallesa, A. and Flores, H., “A Biologically-Inspired Wolf Pack Multiple Robot Hunting Model,” 2006 IEEE 3rd Latin American Robotics Symposium, Santiago, Chile (2006) pp. 120–127.Google Scholar
Muro, C., Escobedo, R., Spector, L. and Coppinger, R., “Wolf-pack (canis lupus) hunting strategies emerge from simple rules in computational simulations,” Behav. Process. 88(3), 192–197 (2011).CrossRefGoogle ScholarPubMed
Shishika, D., Yim, J. K. and Paley, D. A., “Robust lyapunov control design for bioinspired pursuit with autonomous hovercraft,” IEEE Trans. Control Syst. Technol. 25(2), 509–520 (2016).CrossRefGoogle Scholar
Angelani, L., “Collective predation and escape strategies,” Phys. Rev. lett. 109(11), 1–5 (2012).CrossRefGoogle ScholarPubMed
Lin, Y. and Abaid, N., “Collective behavior and predation success in a predator-prey model inspired by hunting bats,” Robotica 88(6), 062724 (2013).Google Scholar
Shneydor, N. A., Missile Guidance and Pursuit: Kinematics, Dynamics and Control (Woodhead Publishing, UK, 1998).CrossRefGoogle Scholar
Nahin, P., Chases and Escapes The Mathematic (Princeton University Press, Princeton, NJ, 2007).Google Scholar
Belkhouche, F. and Belkhouche, B., “A method for robot navigation toward a moving goal with unknown maneuvers,” Robotica 23(6), 709–720 (2005).CrossRefGoogle Scholar
Belkhouche, F., Belkhouche, B. and Rastgoufard, P., “Parallel navigation for reaching a moving goal by a mobile robot,” Robotica 25(1), 63–74 (2007).CrossRefGoogle Scholar
Teimoori, H. and Savkin, A. V., “A biologically inspired method for robot navigation in a cluttered environment,” Robotica 28(5), 637–648 (2010).CrossRefGoogle Scholar
Tan, R. and Kumar, M., “Proportional Navigation (PN) Based Tracking of Ground Targets by Quadrotor UAVs,” ASME 2013 Dynamic Systems and Control Conference, Palo Alto, CA, USA (2013) pp. 157–173.Google Scholar
Jung, S., Hwang, S., Shin, H. and Shim, D. H., “Perception, guidance, and navigation for indoor autonomous drone racing using deep learning,” IEEE Rob. Autom. Lett. 3(3), 2539–2544 (2018).CrossRefGoogle Scholar
Huang, H., Zhang, W., Ding, J., StipanoviĆ, D. M. and Tomlin, C. J., “Guaranteed Decentralized Pursuit-Evasion in the Plane with Multiple Pursuers,” 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, USA (2011) pp. 4835–4840.Google Scholar
Li, J., Li, M., Li, Y., Dou, L. and Wang, Z., “Coordinated Multi-robot Target Hunting Based on Extended Cooperative Game,” 2015 IEEE International Conference on Information and Automation, Lijiang, Yunnan, China (2015) pp. 216–221.Google Scholar
Zafeiris, A. and Vicsek, T., Why We Live in Hierarchies? A Quantitative Treatise (Springer, Switzerland, 2017).Google Scholar
Stander, P. E., “Cooperative hunting in lions: The role of the individual,” Behav. Ecol. Sociobiol. 29(6), 445–454 (1992).CrossRefGoogle Scholar
Gong, J., Qi, J., Xiong, G., Chen, H. and Huang, W., “A GA Based Combinatorial Auction Algorithm for Multi-robot Cooperative Hunting,” 2007 International Conference on Computational Intelligence and Security, Heilongjiang, China (2007) pp. 137–141.Google Scholar
Wang, W., Qi, J., Zhang, H. and Zong, G., “A Rapid Hunting Algorithm for Multi Mobile Robots System,” 2007 2nd IEEE Conference on Industrial Electronics and Applications, Harbin, China (IEEE, 2007) pp. 1203–1207.CrossRefGoogle Scholar
Duan, Y., Huang, X. and Yu, X., “Multi-robot Dynamic Virtual Potential Point Hunting Strategy Based on Fis,” 2016 IEEE Chinese Guidance, Navigation and Control Conference, Nanjing, China (2016) pp. 332–335.Google Scholar
Reynolds, C. W., “Flocks, herds and schools: A distributed behavioral model,” ACM SIGGRAPH Comput. Graphics 21(4), 25–34 (1987).CrossRefGoogle Scholar
Vicsek, T. and Zafeiris, A., “Collective motion,” Phys. Rep. 517(3–4), 71–140 (2012).CrossRefGoogle Scholar
Saito, T., Nakamura, T. and Ohira, T., “Group chase and escape model with chasers’ interaction,” Phys. A Stat. Mech. Appl. 447, 172–179 (2016). https://doi.org/10.1016/j.physa.2015.12.023 CrossRefGoogle Scholar
Janosov, M., Virágh, C., Vásárhelyi, G. and Vicsek, T., “Group chasing tactics: How to catch a faster prey,” New J. Phys. 19(5), 1–16 (2017).CrossRefGoogle Scholar
Vásárhelyi, G., Virágh, C., Somorjai, G., Tarcai, N., Szörényi, T., Nepusz, T. and Vicsek, T., “Outdoor Flocking and Formation Flight with Autonomous Aerial Robots,” 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, Illinois (2014) pp. 3866–3873.Google Scholar
Rufus, I., Differential Games , SIAM Series in Applied Mathematics (Wiley, New York, 1965).Google Scholar
Belkhouche, F., Belkhouche, B. and Rastgoufard, P., “Parallel navigation for reaching a moving goal by a mobile robot,” Robotica 25(1), 63–74 (2007).CrossRefGoogle Scholar
De Souza, C., Newbury, R., Cosgun, A., Castillo, P., Vidolov, B. and KuliĆ, D., “Decentralized multi-agent pursuit using deep reinforcement learning,” IEEE Rob. Autom. Lett. (RAL) 6(3), 4552–4559 (2021).CrossRefGoogle Scholar
De Souza, C., Castillo, P., Lozano, R. and Vidolov, B., “Enhanced UAV Pose Estimation Using a KF: Experimental Validation,” 2018 International Conference on Unmanned Aircraft Systems (ICUAS), Dallas, Texas (2018) pp. 1255–1261.CrossRefGoogle Scholar
Soria, E., Schiano, F. and Floreano, D., “The Influence of Limited Visual Sensing on the Reynolds Flocking Algorithm,” Third IEEE International Conference on Robotic Computing (IRC) (2019) pp. 138–145.Google Scholar