During the flights all the key functions of the UAV were recorded much like an aircraft’s black box. Using this data it was possible to determine how well the various
Flight-ID Operator Flight Time RMSE from target position
(m)
Max drift (m)
Full-1 1 6 min 5 s N/A N/A
Full-2 2 4 min 17 s 0.90 2.85
Full-3 1 2 min 47 s 1.17 4.14
Full-4 2 2 min 43 s 1.17 4.07
Full-5 2 3 min 27 s 0.76 2.71
Full-6 2 3 min 3 s 0.72 3.66
Table 7.1: Textual results from the test flights
algorithms functioned, while also determining the overall flight characteristics of the UAV.
Six identical test flights were performed, using two operators. Table 7.1 gives a statistical overview of the flights, with the graphics listed in Appendix C on page 208. Of the six flights, five were completed successfully. The failed flight (Flight-ID:Full- 1), was the first flight test performed to determine the robustness, accuracy and performance of the UAV as a whole. Ironically this flight was also the first and (to-date) only significant crash the UAV has encountered, and even more ironically due to a fault in the safety controller as opposed to operator or autonomy error. An in-depth discussion of the crash is reported in 7.1.3 on page 153.
Following repairs, the other remaining flights were successfully completed, achieving the full route without the need for intervention between the operator and UAV. One of the flights, Flight-ID:Full-2 is analysed thoroughly below, the remainder of the flights can be viewed using the same plot types in Appendix C on page 208. The first plot, figure 7.4 shows a plan view of the UAV’s path overlaid with the target path set by the operator. The take-off and landing point positions are both at 0,0m, and the operator approximately followed the guide given in figure 7.2. The “actual” position of the UAV is the calculated position from the SLAM algorithm as no other method was available for determining its true position.
Figure 7.3 gives a breakdown of the UAV’s actual and target positions in the three positional axis: X position, Y position and Height. This graph gives a better over- view of the characteristics of the control systems of the UAV as opposed to figure 7.4. To help visualise any motion or significant bias in the position error a polar plot was produced of the lateral position deviations and is shown in figure 7.5. The last set of graphs demonstrates the performance of the SLAM algorithm, with figure 7.6 showing the navigational map created by the UAV as it flew the mission. Figure 7.7, gives an indication of the time taken to process each individual position update, along with the output of the velocity estimator shown in figure 7.8.
0 50 100 150 200 250 300 −5 0 5 10 Time (Seconds) Distance (Metres)
Target Position vs Calculated Position (x−axis)
Calculated Target 0 50 100 150 200 250 300 −5 0 5 10
Target Position vs Calculated Position (y−axis)
Time (Seconds) Distance (Metres) Calculated Target 0 50 100 150 200 250 300 0 2 4
Measured Height (SoNAR)
Time (Seconds)
Height (Metres)
Measured Target
Figure 7.3: UAV’s target position and actual position in X,Y and Height axis (Flight- ID:Full-2)
−4 −2 0 2 4 6 8 −4 −2 0 2 4 6 8
Distance x−axis (Metres)
Distance y−axis (Metres)
Plan View of Robot’s Calculated Path
Calculated Pos. Target Pos.
(a) The UAV’s path
−20 −10 0 10 20 30 −20 −10 0 10 20 30 Distance X (Metres) Distance Y (Metres)
Plan View of Robot’s Calculated Path Overlaid onto the Point−Cloud Pointcloud Calculated Pos. Target Pos.
(b) UAV’s path overlaid onto the resulting point-cloud data
Figure 7.4: A plan view of the UAV’s achieved path and target positions (Flight- ID:Full-2)
1 2 3 30 210 60 240 90 270 120 300 150 330 180 0
Deviation from Target Position (m) vs Direction (degrees)
Figure 7.5: Polar plot of the UAV’s deviation from the target position (Flight- ID:Full-2)
(a) Perspective view (b) Plan view
Figure 7.6: The resulting on-board real-time SLAM navigation map (a), plan view (b)
0 50 100 150 200 250 300 0 0.1 0.2 0.3 0.4 0.5 Time (Seconds)
Update Rate (Seconds)
SLAM Update Rate vs. Flight Time
Figure 7.7: Update rate (processing time) of the SLAM algorithm during the flight (Flight-ID:Full-2) 0 50 100 150 200 250 300 −10 −8 −6 −4 −2 0 2 4 Time in Seconds Velocity m/s
Velocity Smoothing X axis
Raw Velocity IMU Fused Velocity
0 50 100 150 200 250 300 −5 0 5 Time in Seconds Velocity m/s
Velocity Smoothing Y axis
Raw Velocity IMU Fused Velocity
Figure 7.8: Output of the Velocity Estimator through-out the flight (Flight-ID:Full- 2)
As demonstrated in these figures, the UAV remained aware of its own location through-out the mission, successfully proving the desired function of the mapping algorithm in this environment. Although there were numerous large lateral de- viations from the desired track, the PID control loops remained damped and no diverging oscillations developed.
In summary of the six test flights flown, a number of key reoccurring observations regarding the functions of the robot were made:-
Firstly, the tests showed that an operator with little or no knowledge of operating flying vehicles or robots repeatedly completed the set inspection mission successfully and safely, proving the fulfilment of the “ease-of-use” requirement.
Secondly, the SLAM algorithm maintained a robust map of the environment and localised the robot within the generated map without significant error.
Lastly, although the control loops maintained control of the UAV and did not de- velop divergent oscillations, the precision was far from perfect, with the maximum positional deviations often spanning many metres with a Root Mean Square Error
(RMSE) of approximate one metre. The control loops, although forming an essential role in this project, have not been the focus of the project and require significant further work before the UAV should be fully deployed.
One reason which causes the large deviations is due to the over-damped PID loops. To prevent divergent oscillations, the PID loops have been tuned to not react ag- gressively and take a “slow and safe” approach to controlling the UAV. This method perhaps functions well if the UAV is in a hover condition. However, as soon as the UAV is instructed to move or subjected to an external force like a gust of wind, the UAV will be slow to respond and can traverse many metres, potentially in the opposite direction, before again re-aligning with the set-point.
Figure 7.9: UAV after the crash