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. © Springer Nature Singapore Pte Ltd. 2017.
cited By ; Conference of International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2016 ; Conference Date: 19 May 2016 Through 21 May 2016; Conference Code:195169
U. S. Ananthakrishnan, Akshay, N., Manikutty, G., and Rao R. Bhavani, “Control of quadrotors using neural networks for precise landing maneuvers”, Advances in Intelligent Systems and Computing, vol. 517, pp. 103-113, 2017.