Removing ice and snow from airplanes is a necessary and safety-critical process to enable reliable air transport in the cold winter months at many airports around the world. Most often, specialized de-icing trucks are used for this purpose, operated by trained operators, to ensure that the airplanes can take off and safely fly passengers and cargo to their destination. These operators currently handle almost every aspect of the de-icing process manually. They need to accurately position the vehicle next to the airplane and control the arm and nozzle to spray the de-icing liquid from the proper distance, with the right volume, and on the correct surfaces.
The global push towards industrial automation and autonomous vehicles has led to new technologies becoming commercially available, which allow automated systems to perceive their environment better than ever before. Combined with advances in computing hardware and control algorithms, they form the basis for developing new assistance systems for the airplane de-icing process. Advanced assistance systems can significantly reduce the complexity of the operator’s tasks and improve the efficiency of the de-icing process. These systems can be further developed in the future to facilitate fully autonomous airplane de-icing.
This thesis presents a contribution of several individual solutions that have been researched as part of the autonomous platform for de-icing trucks being developed by Vestergaard Company. Autonomous de-icing offers a unique set of challenges, operating in varied and often challenging weather conditions and in an environment with very high safety standards. The system must be able to complete all its tasks better than the human operator. Based on the data from a lidar sensor, a solution is proposed that can localize airplanes and identify their type. The autonomous system can then plan a path for the de-icing truck to drive to the optimal position to de-ice the airplane.
In order to initially test the autonomous systems, a simulation environment was developed where the sensor data and the algorithms’ reactions can be simulated. Subsequent experimental tests on several airplanes at different airports show how these solutions can be successfully applied as part of an autonomous system on the Vestergaard Elephant BETA de-icing trucks.
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Christina Blaagaard Collignon
Trine Reitz Bjerregaard