Publication Type : Conference Paper
Thematic Areas : Humanitarian-Robotics-HCI
Publisher : Advances in Intelligent Systems and Computing, Springer Verlag,
Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 517, p.103-113 (2017)
ISBN : 9789811031731
Keywords : Artificial intelligence, Backpropagation, Backpropagation algorithms, Control systems, Distance constraints, Ground operations, Individual systems, Intelligent vehicle highway systems, Landing, Landing operations, Neural networks, Offline neural networks, Quad rotors, Quadrotor controls, Robot operating system, Robots, Vision based navigation, Visual servoing
Campus : Amritapuri
School : Department of Social Work
Center : Ammachi labs, Humanitarian Technology (HuT) Labs
Department : Center for Computational Engineering and Networking (CEN), Social Work
Year : 2017
Abstract : Aerial and ground robots have been widely used in tandem to overcome the limitations of the individual systems, such as short run time and limited field of view. Several strategies have been proposed for this collaboration and all of them involve periodic autonomous precision landing of the aerial vehicle on the ground robot for recharging. Intelligent control systems like neural networks lend themselves naturally to precision landing applications since they offer immunity to system dynamics and adaptability to various environments. Our work describes an offline neural network backpropagation controller to provide visual servoing for the landing operation. The quadrotor control system is designed to perform precise landing on a marker platform within the specified time and distance constraints. The proposed method has been simulated and validated in a Gazebo and robot operating system simulation environment.
Cite this Research Publication : U. S. Ananthakrishnan, Akshay, N., Gayathri Manikutty, and Bhavani, R. R., “Control of quadrotors using neural networks for precise landing maneuvers”, Advances in Intelligent Systems and Computing, vol. 517, pp. 103-113, 2017.