UAVs, Navigation and Sensor Orientation

The navigation is composed of two main concepts: positioning & guidance.

  • The positioning is the determination of the position and velocity of a moving object with respect to a known reference
  • The guidance is the planning and maintenance of a course from one location (origin) to another (destination)

TOPO is active in aerial photogrammetry research, both airborne and drone-based. In this context, many challenging problems arise related to sensor orientation determination, autonomous navigation, sensor fusion, design, testing and operation of micro aerial vehicles, both fixed wing and multi-rotor.

A list of currently available semester/master projects is maintained below:

‘Sense Dynamics’ research project
Advanced bio-inspired autonomous drone navigation and control based on aerodynamics ‘learning and sensation’. This via a combination of Deep-Learning, CFD modelling and wind-tunnel testing.

For any vehicle moving in autonomous mode, reliable navigation is crucial. To operate drones in cluttered and challenging environments such as cities, forests or mountainous terrain, their position, attitude, and velocity must be known accurately. The lack of GNSS (Global Navigation Satellite System) signals due to obstructions or external interference can cause complete failure of a drone navigation system, which would be unacceptable for any application Beyond Line-Of-Sight (BLOS). Currently, at TOPO, a novel navigation system is developed based on flight dynamics [1]. With a Vehicle Dynamic Model (VDM), drones can navigate through a complex environment in the absence of GNSS signal. However, wind and gust effects cause drift errors, which may accumulate up to hundreds of metres during a GNSS outage of a few minutes (see [2], [3]).

This current research project aims at drawing inspiration from nature to solve this problem. The development of a novel sensor network and analysis framework – based on Computational Fluid Dynamics (CFD) and Deep Neural Networks – will provide real-time drone “skin sensation” of the wind effects, and the underlying aerodynamic forces will be integrated into the VDM-based navigation.

The Student (Master or Semester project) would join the research project and can take part both in the technical (instrumentation/wind tunnel testing) and or computational part (Deep-Learning, Computational Fluid Dynamics). The workload can be split upon specific project arrangement.  In more detail, available challenges can be seen outlined below:

  • Experimental
    1. To contribute to the study of measurement uncertainty, sensitivity and practical performance of state-of-the-art air pressure, strain gauges and beyond state-of-the-art velocity vector measurement sensors (heat-flux based). This experimental work would validate the performance of the novel sensors envisaged to be integrated within the concept of ‘drone-skin’ distributed sensation system.
    2. To participate in the instrumentation of the wind tunnel and the gathering of experimental data.
    3. To validate predictions of new models (CFD and Deep Learning based) for aerodynamic forces prediction by comparison with wind tunnel data.
  • Modelling
    1. To build a database of CFD simulations (steady-state and transient) using the ANSYS Workbench and Parametric Design Language (APDL).
    2. To carry out statistical sensitivity studies for the CFD simulations, in order to assess their accuracy. This will be performed using the ANSYS Workbench interface and a predefined workflow (“design of experiments” technique).
    3. To develop Deep Neural Regression networks that can relate time series of measurements from aerofoil sensors (sequence 1) with lift and drag fields and vectors (sequence 2), and to study their performance and uncertainty under supervisor’s guidance.

General understanding and interest in the field of (drone, robotics, aerodynamics, CFD modelling, deep-learning) are good pre-requisite for a student joining the project. Understanding of Matlab, Python (Pytorch), ANSYS, CATIA and practical instrumentation would be highly south after.

The interested student applicants can benefit of being a part of a new growing beyond state of the art research project. For more information please do contact:

Dr Iordan Doytchinov

https://people.epfl.ch/iordan.doytchinov

References:

[1]   M. Khaghani and J. Skaloud, “Autonomous Vehicle Dynamic Model-Based Navigation for Small UAVs,” Navigation, vol. 63, no. 3, pp. 345–358, Sep. 2016.

[2]   M. Khaghani and J. Skaloud, “Assessment of VDM-based autonomous navigation of a UAV under operational conditions,” Rob. Auton. Syst., vol. 106, no. 106, pp. 152–164, Aug. 2018.

[3]   M. Khaghani and J. Skaloud, “Evaluation of Wind Effects on UAV Autonomous Navigation Based on Vehicle Dynamic Model,” Proc. 29th Int. Tech. Meet. Satell. Div. Inst. Navig. (ION GNSS+ 2016), pp. 1432–1440.

Contact/Proposal

If you have an interest in this domain, please don’t hesitate to contact TOPO staff.