Cooperative navigation for a heterogeneous multi-robot system

This paper presents an approach to optimizing navigation strategies for a heterogeneous robot group moving collaboratively in complex environments characterized by obstacles and unstable Global Navigation Satellite System signal reception. Specifically, the study investigates the potential to improve navigation accuracy for each agent in the group by implementing mutual correction algorithms for the navigation systems of aerial and ground robots physically connected by a controllable flexible tether. The developed algorithms enable both the autonomous operation of individual agents and their coordinated functioning as a unified system. They incorporate additional correction channels that utilize data from the tether mechanism's sensors, including length, tension, and deflection angle. This data allows for the continuous determination of the robots' relative spatial positions, effectively compensating for the accumulating drift errors of onboard inertial navigation systems. To provide a theoretical foundation for this approach, a comprehensive mathematical model describing the spatial dynamics of the robots is presented. Furthermore, the paper presents the results of simulation modeling for the movement processes and the estimation of navigation parameters. The obtained data confirm that the proposed sensor fusion method significantly reduces positioning errors compared to the isolated operation of the robots, thereby enhancing the overall reliability of mission execution.

1. Zhang P., Liu Y., Du H. An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields. Frontiers in Plant Science. 2024;15:1440234. https://doi.org/10.3389/fpls.2024.1440234

2. Pereira G.A.S., Kumar V., Campos M.F. Decentralized algorithms for multirobot manipulation via caging. In: Algorithmic Foundations of Robotics V. Berlin, Heidelberg: Springer; 2004. P. 257–273. https://doi.org/10.1007/978-3-540-45058-0_16

3. Groenewald C.A., Saha G.Ch., Mann G., et al. Multi-agent systems in robotics: coordination and communication using machine learning. Naturalista Campano. 2024;28(1):882–897.

4. Salam T., Hsieh M.A. Heterogeneous robot teams for modeling and prediction of multiscale environmental processes. Autonomous Robots. 2023;47(4):353–376. https://doi.org/10.1007/s10514-023-10089-6

5. Bhatia V., Arora R., Sidharth S. Deep learning for automated aircraft surface defect detection. In: 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC), 23–25 November 2024, Guntur, India. IEEE; 2024. https://doi.org/10.1109/ICEC59683.2024.10837564

6. Liu C., Wei S., Zhang M., et al. High-precision monitoring during the installation of large steel structures by UAV nap-of-the-object photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2024;XLVIII-4-2024:317–323. https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-317-2024

7. Chernomorsky A.I., Lelkov K.S., Kuris E.D. About one way to increase the accuracy of navigation system for ground wheeled robot used in aircraft parking. Smart Science. 2020;8(4):219–226. https://doi.org/10.1080/23080477.2020.1824055

8. Lelkov K.S., Chernomorsky A.I. Integrated navigation system for ground wheeled robot. In: 2022 29th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS), 30 May – 01 June 2022, Saint Petersburg, Russia. IEEE; 2022. https://doi.org/10.23919/ICINS51784.2022.9815389

9. Lei Y., Cheng M. Aerodynamic performance of a hex-rotor unmanned aerial vehicle with different rotor spacing. Measurement and Control. 2020;53(3-4):711–718. https://doi.org/10.1177/0020294019901313

10. Wan E.A., Van Der Merwe R. The unscented Kalman filter for nonlinear estimation. In: Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, 04 October 2000, Lake Louise, AB, Canada. IEEE; 2000. P. 153–158. https://doi.org/10.1109/ASSPCC.2000.882463

11. Schacht-Rodriguez R., Ortiz-Torres G., García-Beltrán C.D., et al. SoC estimation using an extended Kalman filter for UAV applications. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), 13–16 June 2017, Miami, FL, USA. IEEE; 2017. P. 179–187. https://doi.org/10.1109/ICUAS.2017.7991381

12. Brodsky M.S., Zvonarev V.V., Khubbiev R.V., et al. Computer model of the satellite communication and data relay system radio channel during multiposition signal transmission. Trudy MAI. 2022;(127). (In Russ.). https://doi.org/10.34759/trd-2022-127-10