Abstract
In the recent year, Unmanned Aerial Vehicles (UAV) which are generally called Drones or Quadcopters have gained a lot of attention towards researches and companies because of their wide range of capabilities in different sectors such as in military and public. The PID control algorithm which runs in the ROS environment controls the 3-D modelled quadcopter in Gazebo. The main focus is to make the quadcopter stable and to hover at the desired point in Gazebo by tuning PID values in the ROS environment. A plot juggler has been used to obtain the trajectory analysis and to analyse the tuning errors by the trial and error method for each quadcopter motion such as roll, pitch and altitude. Through this framework, certain parameters of the drone are obtained such as settling and rise time with PID tuning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lukmana, M.A., Nurhadi, H.: Preliminary study on Unmanned Aerial Vehicle (UAV) Quadcopter using PID controller. In: 2015 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA), Surabaya, pp. 34–37 (2015). https://doi.org/10.1109/ICAMIMIA.2015.7507997
Babu, V.M., Das, K., Kumar, S.: Designing of self-tuning PID controller for AR drone quadrotor. In: 2017 18th International Conference on Advanced Robotics (ICAR), Hong Kong, pp. 167–172 (2017). https://doi.org/10.1109/ICAR.2017.8023513
Vamsi, D.S., Tanoj, T.V.S., Krishna, U.M., Nithya, M.: Performance Analysis of PID controller for Path Planning of a Quadcopter. In: 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC), Chennai, India, pp. 116–121, doi: 1109/ICPEDC47771.2019.9036558
Fernando, H.C.T.E., De Silva, A.T.A., De Zoysa, M.D.C., Dilshan, K.A.D.C., Munasinghe, S.R.: Modelling, simulation and implementation of a quadrotor UAV. In: 2013 IEEE 8th International Conference on Industrial and Information Systems, Peradeniya, pp. 207–212 (2013). https://doi.org/10.1109/ICIInfS.2013.6731982.
Mandal, S., Biswas, S., Balas, V.E., Shaw, R.N., Ghosh, A.: Motion prediction for autonomous vehicles from lyft dataset using deep learning. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 768–773 (2020). https://doi.org/10.1109/ICCCA49541.2020.9250790
Sharma, V.N.V.A., Rajesh, M.: Building a quadcopter: an approach for an autonomous quadcopter. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, 2018, pp. 1252–1258 (2018). https://doi.org/10.1109/ICACCI.2018.8554718
Nithya, M., Rashmi, M.R.: Gazebo—ROS—Simulink framework for hover control and trajectory tracking of crazyflie 2.0. In: TENCON 2019–2019 IEEE Region 10 Conference (TENCON), Kochi, India, pp. 649–653 (2019). doi: 1109/TENCON.2019.8929730
Megalingam, R.K., Raj, R.V.R., Masetti, A., Akhil, T., Chowdary, G.N., Naick, V.S.: Design and implementation of an arena for testing and evaluating quadcopter. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 1–7 (2018). https://doi.org/10.1109/CCAA.2018.8777578
Megalingam, R.K., Nagalla, D., Nigam, K., Gontu, V., Allada, P.K.: PID based locomotion of multi-terrain robot using ROS platform. In: 2020 Fourth International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, pp. 751–755 (2020). https://doi.org/10.1109/ICISC47916.2020.9171152
Megalingam, R.K., Nagalla, D., Pasumarthi, R.K., Gontu, V., Allada, P.K.: ROS based, simulation and control of a wheeled robot using gamer’s steering wheel. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 1–5 (2018). https://doi.org/10.1109/CCAA.2018.8777569.
Acknowledgements
We take this opportunity to express our profound gratitude and deep regards to HuT Labs, Amrita Vishwa Vidyapeetham which provided guidance and space for us to complete this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Megalingam, R.K., Prithvi, D.V., Kumar, N.C.S., Egumadiri, V. (2022). Drone Stability Simulation Using ROS and Gazebo. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-2164-2_11
Download citation
DOI: https://doi.org/10.1007/978-981-16-2164-2_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2163-5
Online ISBN: 978-981-16-2164-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)