View source for Air Surveillance for Airport Safety Using Autonomous Vehicles

{{StudentProjectTemplate
|Summary=Air Surveillance for Airport Safety Using Autonomous Vehicles
|TimeFrame=Winter 2018, Spring 2019
|Supervisor=Cristofer Englund, Fernando Alonso-Fernandez, 
|Level=Master
|Status=Open
}}
'''Introduction'''

This project is part of a the ASAS - Air Surveillance for Airport Safety and AVAP - Autonomous Vehicles for AirPorts projects. Where the Örnsköldsvik airport develops systems for autonomous surveillance and maintenance of their airport.  

This project focus on collision avoidance for drones. The collision avoidance should account for both indoor and outdoor obstacles e.g. trees, signs, vehicles, humans, roof, walls, pillars, other drones etc.  

The algorithm development and testing will be done mostly based on NVIDIA Jetson TX2 connecting to light controllers (Emlid). Potential sensors for collision avoidance include Intel RealSense camera, Ultra-sonic sensor, Lidar and other camera sensors if needed.  

Algorithm development and testing will use the Hardware in the loop and firstly tested in simulated environment, such as with Microsoft AirSim or Gazebo, and then validation testing will be done in realistic environment. 


'''Research Questions'''

The research should focus on the following research questions
* How to make a reliable identi�cation of obstacles?
* What are the critical trade-offs between on-board vs off-board calculations?
*  In what ways can a simulator facilitate system development, veri�cation and validation?


'''Project description'''

The following tasks could be considered:
1. Planning
2. Literature review
3. Sensor evaluation
4. Collision avoidance algorithm development
5. Reliability evaluation
6. Analysis of results
7. Conclusions
8. Report writing (summarize in conference paper) presentation

Relevant literature can be found in [1, 3, 2].


'''Industrial contact'''

Lei Chen, RISE Viktoria


'''References'''

[1] J. Hu, Y. Niu, and Z. Wang. Obstacle avoidance methods for rotor uavs using realsense camera. In 2017 Chinese Automation Congress (CAC), pages 7151-7155, Oct 2017.

[2] Ryan Scott. Autonomous navigation and hazard evasion platform for personal uav's. 2016.

[3] Xian Wu, Yuan Cai, Yu Chen, and Kai Wang. Transmission line unmanned aerial vehicle obstacle avoidance system incorporating multiple sensing technologies. Journal of Physics: Conference Series, 1069(1):012025, 2018.