Littérature scientifique sur le sujet « Waypoint tracking »
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Articles de revues sur le sujet "Waypoint tracking":
Ichihara, Kaito, Tadahiro Hasegawa, Shin’ichi Yuta, Hirohisa Ichikawa et Yoshihide Naruse. « Waypoint-Based Human-Tracking Navigation for Museum Guide Robot ». Journal of Robotics and Mechatronics 34, no 5 (20 octobre 2022) : 1192–204. http://dx.doi.org/10.20965/jrm.2022.p1192.
Kang, Cheongwoong, Bumjin Park et Jaesik Choi. « Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External Disturbances ». Sensors 22, no 1 (27 décembre 2021) : 150. http://dx.doi.org/10.3390/s22010150.
Lobo, Shawn. « Waypoint Based GPS Tracking ». International Journal for Research in Applied Science and Engineering Technology 6, no 3 (31 mars 2018) : 2264–66. http://dx.doi.org/10.22214/ijraset.2018.3525.
Gutiérrez, Rodrigo, Elena López-Guillén, Luis M. Bergasa, Rafael Barea, Óscar Pérez, Carlos Gómez-Huélamo, Felipe Arango, Javier del Egido et Joaquín López-Fernández. « A Waypoint Tracking Controller for Autonomous Road Vehicles Using ROS Framework ». Sensors 20, no 14 (21 juillet 2020) : 4062. http://dx.doi.org/10.3390/s20144062.
AL TAHTAWI, ADNAN RAFI, ERICK ANDIKA, MAULANA YUSUF et WILDAN NURFAUZAN HARJANTO. « Pengembangan Low-cost Quadrotor dengan Misi Waypoint Tracking Berbasis Pengendali PID ». ELKOMIKA : Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & ; Teknik Elektronika 8, no 1 (31 janvier 2020) : 189. http://dx.doi.org/10.26760/elkomika.v8i1.189.
MISIR, Oğuz, Muhammed ÇELİK et Levent GÖKREM. « Waypoint-Based Path Tracking Approach For Self-Organized Swarm Robots ». Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 14, no 2 (31 juillet 2022) : 799–815. http://dx.doi.org/10.29137/umagd.1118039.
Xu, Chengtao, Kai Zhang, Yushan Jiang, Shuteng Niu, Thomas Yang et Houbing Song. « Communication Aware UAV Swarm Surveillance Based on Hierarchical Architecture ». Drones 5, no 2 (30 avril 2021) : 33. http://dx.doi.org/10.3390/drones5020033.
Oland, Espen, Rune Schlanbusch et Raymond Kristiansen. « Underactuated Waypoint Tracking of a Fixed-Wing UAV* ». IFAC Proceedings Volumes 46, no 30 (2013) : 126–33. http://dx.doi.org/10.3182/20131120-3-fr-4045.00007.
Capello, Elisa, Giorgio Guglieri et Gianluca Ristorto. « Guidance and control algorithms for mini UAV autopilots ». Aircraft Engineering and Aerospace Technology 89, no 1 (3 janvier 2017) : 133–44. http://dx.doi.org/10.1108/aeat-10-2014-0161.
Sun, Wenli, et Xu Gao. « Deep Learning-Based Trajectory Tracking Control forUnmanned Surface Vehicle ». Mathematical Problems in Engineering 2021 (6 janvier 2021) : 1–22. http://dx.doi.org/10.1155/2021/8926738.
Thèses sur le sujet "Waypoint tracking":
Curtis, Andrew B. « Path Planning for Unmanned Air and Ground Vehicles in Urban Environments ». Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2270.pdf.
Sola, Yoann. « Contributions to the development of deep reinforcement learning-based controllers for AUV ». Thesis, Brest, École nationale supérieure de techniques avancées Bretagne, 2021. http://www.theses.fr/2021ENTA0015.
The marine environment is a very hostile setting for robotics. It is strongly unstructured, very uncertain and includes a lot of external disturbances which cannot be easily predicted or modelled. In this work, we will try to control an autonomous underwater vehicle (AUV) in order to perform a waypoint tracking task, using a machine learning-based controller. Machine learning allowed to make impressive progress in a lot of different domain in the recent years, and the subfield of deep reinforcement learning managed to design several algorithms very suitable for the continuous control of dynamical systems. We chose to implement the Soft Actor-Critic (SAC) algorithm, an entropy-regularized deep reinforcement learning algorithm allowing to fulfill a learning task and to encourage the exploration of the environment simultaneously. We compared a SAC-based controller with a Proportional-Integral-Derivative (PID) controller on a waypoint tracking task and using specific performance metrics. All the tests were performed in simulation thanks to the use of the UUV Simulator. We decided to apply these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater vehicle (ROV) converted in an AUV. Thanks to these tests, we managed to propose several interesting contributions such as making the SAC achieve an end-to-end control of the AUV, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm. Moreover we propose a methodology for the training of deep reinforcement learning algorithms on control tasks, as well as a discussion about the absence of guidance algorithms for our end-to-end AUV controller
Ho, Yueh-Sheng, et 何岳昇. « Design of a Waypoint-tracking Controller for a Biomimetic-autonomous Underwater Vehicle ». Thesis, 2003. http://ndltd.ncl.edu.tw/handle/20288048922278026639.
國立臺灣大學
工程科學與海洋工程學系
91
We develop a control system for the waypoint-tracking of a biomimetic-autonomous underwater vehicle (BAUV). Basic swimming modes are determined. The BAUV swims forward by oscillating its body and caudal fin and turns by slanting its body and caudal fin to the side of turning direction. Because of the undulatory motion of BAUV, we take averages of swimming velocity and heading error during motion period as feedbacks to control the velocity and angular velocity of BAUV. We verify the effectiveness of control algorithms by simulations and experiments. Finally, we discuss the influence of control parameters on the swimming performance of the BAUV.
Chapitres de livres sur le sujet "Waypoint tracking":
Srinivas, Chinmay, et Sharanbassappa S. Patil. « A Waypoint Tracking Controller for Autonomous Vehicles Using CARLA Simulator ». Dans Recent Advances in Hybrid and Electric Automotive Technologies, 197–206. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2091-2_16.
Park, Myeong-Chul, et Seok-Wun Ha. « The Visualization Tool of the Open-Source Based for Flight Waypoint Tracking ». Dans Communications in Computer and Information Science, 153–61. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20998-7_20.
Alkurdi, L. M., et R. B. Fisher. « Visual Control of an Autonomous Indoor Robotic Blimp ». Dans Robotic Vision, 352–70. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2672-0.ch019.
Abbas, Roba, Katina Michael, M. G. Michael et Anas Aloudat. « Emerging Forms of Covert Surveillance Using GPS-Enabled Devices ». Dans Cases on Emerging Information Technology Research and Applications, 112–30. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3619-4.ch006.
Abbas, Roba, Katina Michael, M. G. Michael et Anas Aloudat. « Emerging Forms of Covert Surveillance Using GPS-Enabled Devices ». Dans Cases on Public Information Management and E-Government Adoption, 366–84. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0981-5.ch016.
A. Rendón, Manuel. « Quadrotor Unmanned Aerial Vehicles : Visual Interface for Simulation and Control Development ». Dans Robotics Software Design and Engineering. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97435.
Actes de conférences sur le sujet "Waypoint tracking":
Bauer, Peter, et Andrei Dorobantu. « Optimal waypoint guidance, trajectory design and tracking ». Dans 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6579936.
Choi, Woo Young, Chang Mook Kang, Seung-Hi Lee et Chung Choo Chung. « Waypoint tracking predictive control with vehicle speed variation ». Dans 2017 11th Asian Control Conference (ASCC). IEEE, 2017. http://dx.doi.org/10.1109/ascc.2017.8287143.
Lin, Pengfei, Woo Young Choi, Jin Ho Yang et Chung Choo Chung. « Waypoint Tracking for Collision Avoidance Using Artificial Potential Field ». Dans 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189037.
Jeon, Soo Jung, Chang Mook Kang, Seung-Hi Lee et Chung Choo Chung. « GPS waypoint fitting and tracking using model predictive control ». Dans 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2015. http://dx.doi.org/10.1109/ivs.2015.7225702.
Castillo, C. L., W. Moreno et K. P. Valavanis. « Unmanned helicopter waypoint trajectory tracking using model predictive control ». Dans 2007 Mediterranean Conference on Control & Automation. IEEE, 2007. http://dx.doi.org/10.1109/med.2007.4433726.
Ajay, Vibhute Akash, Adi P. Suherlan, Gim Song Soh, Shaohui Foong, Kristin Wood et Kevin Otto. « Localization and Trajectory Tracking of an Autonomous Spherical Rolling Robot Using IMU and Odometry ». Dans ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47223.
Bauer, Peter, et József Bokor. « Tuning and Improvements in a Waypoint and Trajectory Tracking Algorithm ». Dans AIAA Guidance, Navigation, and Control Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-4604.
Tang, Liqiong, et Phillip Abplanalp. « GPS guided farm mapping and waypoint tracking mobile robotic system ». Dans 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2014. http://dx.doi.org/10.1109/iciea.2014.6931437.
Oland, Espen, et Tom Stian Andersen. « Preliminary Results on Waypoint Tracking for Spacecraft with Actuator Constraints ». Dans 2019 9th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 2019. http://dx.doi.org/10.1109/rast.2019.8767812.
Mathew, Robins, et Somashekhar S. Hiremath. « Development of Waypoint Tracking Controller for Differential Drive Mobile Robot ». Dans 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2019. http://dx.doi.org/10.1109/codit.2019.8820389.
Rapports d'organisations sur le sujet "Waypoint tracking":
Mathew, Jijo K., Christopher M. Day, Howell Li et Darcy M. Bullock. Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317435.