Control of Quadrotors Using Neural Networks for Precise Landing Maneuvers
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.
KeywordsUAV-UGV collaboration Quadrotor landing Vision based navigation Neural networks Backpropagation algorithm ROS architecture
Our work has been motivated by the humanitarian initiatives of Mata Amritanandamayi Devi, the Chancellor of Amrita University. She has been our inspiration and support. Our gratitude goes to Prof. Kurien Issac and Dr. Radhamani Pillay, who offered constructive criticism and valuable feedback at various stages of this project. We also thank the research group at Ammachi Labs for their support and offering us the resources that aided our work.
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