Open Access Open Access  Restricted Access Subscription or Fee Access

Cooperative Aerial-Ground Robotic System Using Genetic Algorithm Auto-Tuned Fractional Order PID Control

(*) Corresponding author

Authors' affiliations



Cooperative Aerial-Ground systems demand robust and efficient control authority to perform precise tracking motion in outdoor exploring, surveillance, and mapping missions. The dynamic characteristics of the swarm system reveal some levels of challenging and model uncertainties. This paper develops a Genetic Algorithm (GA) auto-tuned Fractional Order PID (FOPID) to control the tracking trajectories of a cooperative aerial-ground robotic system. The cooperative team consists of an Unmanned Aerial Vehicle (UAV), aka quadrotor, and an Unmanned Ground Vehicle (UGV). The quadrotor communicates, shares information, and performs a coordinated take-off, tracking, and landing over the UGV. The UGV moves on a predetermined path while the UAV follows it. The mission is called over when the UGV makes a complete stop, and then the UAV safely lands over it. The design onboard controllers minimize the tracking errors. A fractional order PID controller is implemented in a genetic algorithm platform in order to perform online optimization to obtain the best control parameters. The simulation results explain the benefits of the presented approach.
Copyright © 2021 Praise Worthy Prize - All rights reserved.


Aerial Ground Team; Fractional PID Controller; Genetic Algorithm; Trajectory Tracking; Swarm Robotics

Full Text:



M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, Swarm robotics: A review from the swarm engineering perspective, Swarm Intell., vol. 7, no. 1, pp. 1-41, 2013.

J. M. Daly, Y. Ma, and S. L. Waslander, Coordinated landing of a quadrotor on a skid-steered ground vehicle in the presence of time delays, Auton. Robots, vol. 38, no. 2, pp. 179-191, 2014.

G. Dudek, M. Jenkin, E. Milios, and D. Wilkes A Taxonomy for Swarm Robots, 1993 IEEWRSJ international Conference on Intelligent Robots and Systems Yokohama, Japan July 26-30, 1993 October, vol. 00, no. C, pp. 441-447, 1993.

L. Bayindir, A review of swarm robotics tasks, Neurocomputing, vol. 172, pp. 292-321, 2016.

S. Waslander, Unmanned Aerial and Ground Vehicle Teams: Recent Work and Open Problems, Autonomous Control Systems and Vehicles, vol. 65. 2013.

X. Gong, Y.-J. Pan, and A. Pawar, A novel leader following consensus approach for multi-agent systems with data loss, International Journal of Control, Automation and Systems, vol. 15, no. 2, pp. 763-775, Aug. 2017.

H. Wang, C. Zhang, Y. Song, and B. Pang, Master-followed Multiple Robots Cooperation SLAM Adapted to Search and Rescue Environment, International Journal of Control, Automation and Systems, vol. 16, no. 6, pp. 2593-2608, 2018.

S. Yoon, H. Do, and J. Kim, Collaborative Mission and Route Planning of Multi-vehicle Systems for Autonomous Search in Marine Environment, International Journal of Control, Automation and Systems, vol. 18, no. 3, pp. 546-555, 2020.

H. Yu, K. Meier, M. Argyle, and R. Beard, Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles, IEEE/ASME Transactions on Mechatronics, vol. 20, no. 2, pp. 541-552, 2015.

P. K. Das, P. K. Jena, Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators, Applied Soft Computing, Volume 92 July 2020.

A. M. Khaleghi, D. Xu, S. Minaeian, M. Li, Y. Yuan, J. Liu, Y.-J. Son, C. Vo, and J.-M. Lien, A dddams-based UAV and UGV team formation approach for surveillance and crowd control, Proceedings of the Winter Simulation Conference 2014, pp. 2907-2918, 2014.

M. Garzón, J. Valente, D. Zapata, and A. Barrientos, An Aerial-Ground Robotic System for Navigation and Obstacle Mapping in Large Outdoor Areas, Sensors, vol. 13, no. 1, pp. 1247-1267, 2013.

E. H. C. Harik, F. Guerin, F. Guinand, J.-F. Brethe, and H. Pelvillain, UAV-UGV cooperation for objects transportation in an industrial area, 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 547-552, 2015.

W. Li, T. Zhang, and K. Kuhnlenz, A vision-guided autonomous quadrotor in an air-ground multi-robot system, 2011 IEEE International Conference on Robotics and Automation, 2011.

S. Minaeian, J. Liu, and Y.-J. Son, Vision-Based Target Detection and Localization via a Team of Cooperative UAV and UGVs, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 7, pp. 1005-1016, 2016.

Y. Bi and H. Duan, "mplementation of autonomous visual tracking and landing for a low-cost quadrotor, Optik - International Journal for Light and Electron Optics, vol. 124, no. 18, pp. 3296-3300, 2013.

K. Wenzel, A. Masselli, and A. Zell, Automatic Take Off, Tracking and Landing of a Miniature UAV on a Moving Carrier Vehicle, Unmanned Aerial Vehicles, pp. 221-238, 2010.

N. Bezzo, B. Griffin, P. Cruz, J. Donahue, R. Fierro and J. Wood, A Cooperative Heterogeneous Mobile Wireless Mechatronic System, IEEE/ASME Transactions on Mechatronics, vol. 19, no. 1, pp. 20-31, 2014.

S. Yang, S. Scherer, K. Schauwecker and A. Zell, Autonomous Landing of MAVs on an Arbitrarily Textured Landing Site Using Onboard Monocular Vision, Journal of Intelligent & Robotic Systems, vol. 74, no. 1-2, pp. 27-43, 2013.

M.-F. R. Lee, S.-F. Su, J.-W. E. Yeah, H.-M. Huang, and J. Chen, Autonomous landing system for aerial mobile robot cooperation, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), 2014.

E. H. C. Harik, F. Guérin, F. Guinand, J.-F. Brethé, H. Pelvillain, and J.-Y. Parédé, Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle, Advanced Robotics, vol. 31, no. 7, pp. 368-381, 2017.

M. Geng, Y. Li, B. Ding, and H. Wang, Deep Learning-based Cooperative Trail Following for Multi-Robot System, 2018 International Joint Conference on Neural Networks (IJCNN), 2018.

Sawarkar, Abhishek, et al. HMD vision-based teleoperating UGV and UAV for hostile environment using deep learning. arXiv preprint arXiv:1609.04147 (2016).‏

E. Yazdani Bejarbaneh, A. Bagheri, B. Yazdani Bejarbaneh, S. Buyamin, and S. Nezamivand Chegini, A new adjusting technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm, Appl. Soft Comput., vol. 85, p. 105822, Oct. 2019.

L. Sun, C. Lyu, and Y. Shi, Cooperative coevolution of real predator robots and virtual robots in the pursuit domain, Appl. Soft Comput., vol. 89, p. 106098, 2020.

Kramar, V., Alchakov, V., Kabanov, A., Dudnikov, S., Dmitriev, A., The Design of Optimal Lateral Motion Control of an UAV Using the Linear-Quadratic Optimization Method in the Complex Domain, (2020) International Review of Aerospace Engineering (IREASE), 13 (6), pp. 217-227.

Salman, S., Al Dhaheri, M., Dawson, P., Anavatti, S., Autonomous Water Sampling Payload Design, (2020) International Review of Aerospace Engineering (IREASE), 13 (3), pp. 120-125.

Uc Ríos, C., Teruel, P., Use of Unmanned Aerial Vehicles for Calibration of the Precision Approach Path Indicator System, (2021) International Review of Aerospace Engineering (IREASE), 14 (4), pp. 192-200.

Mlayeh, H., Ghachem, S., Nasri, O., Ben Othman, K., Stabilization of a Quadrotor Vehicle Using PD and Recursive Nonlinear Control Techniques, (2021) International Review of Aerospace Engineering (IREASE), 14 (4), pp. 211-219.

Bonfante, F., Maggiore, P., Grimaccia, F., Filippone, E., Dalla Vedova, M., Full Integration of Light RPAS into Not Segregated Airspace: Preliminary Safety Analysis for the Implementation of a Risk Model, (2020) International Review of Aerospace Engineering (IREASE), 13 (5), pp. 165-181.

Han, M., Park, T., Integrated Airworthiness Certification Criteria and Security Risk Assessment for UAVs, (2019) International Review of Aerospace Engineering (IREASE), 12 (3), pp. 141-149.

Fas-Millán, M., Pastor, E., Dynamic Workload Management for Multi-RPAS Pilots, (2019) International Review of Aerospace Engineering (IREASE), 12 (2), pp. 57-69.

Higashino, S., Maruyama, Y., Flight Demonstration of Realtime Path Planning of an UAV Using Evolutionary Computation and Rule-Based Hybrid Method, (2018) International Journal on Engineering Applications (IREA), 6 (5), pp. 156-162.

Tran, K., Modified GA Tuning IPD Control for a Single Tilt Tri-Rotors UAV, (2018) International Review of Aerospace Engineering (IREASE), 11 (1), pp. 1-5.

M. P. Lazarević, S. A. Batalov, and T. S. Latinović, Fractional PID Controller Tuned by Genetic Algorithms for a Three DOFs Robot System Driven by DC motors, IFAC Proceedings Volumes, vol. 46, no. 1, pp. 385-390, 2013.

T. Bresciani, Modelling, Identification and Control of a Quadrotor Helicopter, dissertation. October, 2008.

Y. Luo and Y. Q. Chen, Fractional order motion controls. Chichester, West Sussex, United Kingdom: John Wiley & Sons Ltd., 2013.

S. Benhlima, L. Chaymaa, and A. Bekri, Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning, Procedia Comput. Sci., vol. 127, Mar. 2018.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize