S.G. Shetty

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Master thesis (1)

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Autonomous Landing of an Unmanned Aerial Vehicle

Master thesis (2022) - S.G. Shetty , R.T. Rajan , M. Mohammadkarimi , C.J.M. Verhoeven , Morten Larsen

With the advancement in technology, Unmanned Aerial Vehicles (UAVs) have been able to safely maneuver in risky environments. During landing, the UAV should be able to slow down while not affecting its physical design. Currently, multiple sensors are being used to increase the accuracy while landing which might weigh down some of the smaller UAVs. The usage of a single sensor to guarantee safe landing is yet on its early stages of development. It was observed that insects use Optical Flow to maneuver, and this has been used as a method to design UAV's journey and land safely. By dividing each frame, an Optical Flow Difference is calculated which is used as a metric for the UAV to move towards the landing platform.

Once the UAV has positioned itself on top of the elevated landing platform, image dilation is used to safely land the UAV. One of the methods tested used Image Dilation Method using IMU. Using the calculated image dilation, the control input to the UAV is generated and the UAV lands. This method failed in providing safe landing and it also did not take the vision of the UAV into consideration. Then, Image Dilation Method using Features from Accelerated Segment Test (IDMF-AST) was tested. This method tracks features observed by the UAV and calculated an estimate of the image dilation. The estimate of image landing is used to control the UAV. This showed dependency on the landing design platform and the hyperparameters that are used for implementing IDMF for the type of landing.

Different landing designs were tested for different elevated landing platforms. It was observed that concentric circles on a textured landing marker provided the highest probability of safe landing. To tackle the dependency of the hyperparameters, a Classification Model was proposed to find an optimal set of hyperparameters for each possible height of the platform. This trained model was incorporated with the IDMF, thereby designing the Adaptive IDMF algorithm. The Adaptive IDMF was tested against the original IDMF on the metrics of safe landing probability, time taken to safely land and simulation time. Adaptive IDMF performed better compared to IDMF providing an 190% increase in probability of safe landing, faster safe landing and lesser simulation and compilation time.