Autonomous Drone Network: Non-Intrusive Control and Indoor Formation Positioning

Harrington, Peter (2019) Autonomous Drone Network: Non-Intrusive Control and Indoor Formation Positioning. Doctoral thesis, Northumbria University.

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Abstract

The Teal Group estimated worldwide drone expenditure in 2013 to be $5.2 billion. Since then, worldwide drone expenditure has risen considerably, with the International Data Corporation (IDC) forecasting worldwide spending on drones to total $12.3 billion in 2019, with a compound annual growth rate (CAGR) forecast of 30.6% to 2022. As of 2019, Goldman Sachs report military applications account for 70% of the total spend with consumer applications accounting for 17%, and commercial/civil applications accounting for the remaining 13% where the latter are showing the fastest growth. Applications in construction, agriculture, offshore oil and gas, policing, journalism, border protection, mining and cinematography are predicted to see the greatest drone investment. As the demands increase, and particularly for applications that are time critical or that span large geographical areas, the single drone solution may be inadequate due to its limited energy and payload.
A multiple drone solution, where the drones are networked and the drone’s position is established by GPS (global positioning system), is able to complete demanding applications more efficiently. In such systems however, the accuracy of GPS can be substantially compromised when deployed near tall buildings, trees, or bridges or if deployed indoors or underground.
In this research, a drone position determination (DPD) algorithm, is proposed to overcome the shortcomings of GPS when satellite signals are compromised. An ad-hoc Wi-Fi network of autonomous quadcopter drones is constructed, as a platform to demonstrate the algorithm performance. To complement the DPD algorithm calculation, a method to estimate the distance flown, and also estimate the complete flightpath of a drone by considering the interaction of the angular velocities of a quadcopter’s four rotors (AVQR), is presented. The flight plan to examine the AVQR algorithm yields results enabling the distance flown to be calculated to an accuracy of 95%.

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