Time constraints is the most critical factor that faces the first respondersâ?? teams for search\nand rescue operations during the aftermath of natural disasters and hazardous areas. The utilization\nof robotic solutions to speed up search missions would help save the lives of humans who are in\nneed of help as quickly as possible. With such a human-robot collaboration, by using autonomous\nrobotic solutions, the first response team will be able to locate the causalities and possible victims\nin order to be able to drop emergency kits at their locations. This paper presents a design of\nvision-based neural network controller for the autonomous landing of a quadrotor on fixed and\nmoving targets for Maritime Search and Rescue applications. The proposed controller does not require\nprior information about the target location and depends entirely on the vision system to estimate the\ntarget positions. Simulations of the proposed controller are presented using ROS Gazebo environment\nand are validated experimentally in the laboratory using a Parrot AR Drone system. The simulation\nand experimental results show the successful control of the quadrotor in autonomously landing on\nboth fixed and moving landing platforms.
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