**9. Perspective**

then one of the closest balls, which are 20 cm away, was not accepted. The BFS showed no good results here and only a VFOV of 30 cm × 45 cm and of 40 cm × 60 cm led to acceptable results. Altogether these results also made the BFS look worse than it was. Some balls were

The evaluation demonstrated that the system is capable of autonomously detecting, counting and localization of objects with an accuracy of about 15–20 cm. It was proven that an optimal value for MA (20 cm) has to be a bit higher than the accuracy of the position system and that objects with the distance of 20 cm (MA) in each axis can still be distinguished. Also the coherence of the parameters MA and VFOV on the performance of the search and the detection errors was demonstrated. A smaller VFOV with a smaller MA leads to more double detections, while a too high MA leads to misses of nearby objects. As a general rule, too high VFOV leads to misses because some areas are not searched properly. In this context the acceptance tolerance, which was set to 25 cm in setup 2, is a parameter, which comes into effect. A waypoint is already marked as reached if the current position of the quadrocopter is within this tolerance. This can result in an incomplete cover of the search area and it explains why the BFS misses

The best parameter for VFOV was 30 cm × 45 cm. This setting together with the best value for

Furthermore, the evaluation proved that the DFS performed better than the BFS. The reason for that is the fact that smaller waypoint steps are less accurate than less big ones because of the set point jumps and the jump effect as well as the control and sensor system. These result simplified mean that also for a flying robot such as a quadrotor using the underlying on‐board sensors less turns and commands are better. This could already be demonstrated in previous experiments [21]. The reason for that can be found in the dynamic of the quadrotor as an aerial vehicle with very little friction (air) and the on‐board optical sensors, which are especially affected by the behaviour of the system. Rotations, which mainly occur after set point changes,

MA showed no detection error even in a challenging room with eight objects.

positioned in such way that the BFS failed them by a few centimetres.

**Figure 22.** BFS detection failures (distribution after VFOV).

**8. Conclusion and discussion**

22 Recent Advances in Robotic Systems

some targets at the side of the search area.

are a source of error for the position determination.

Although the system performed quite well in general, there is potential for optimization. The effect of the already mentioned acceptance tolerance and an improved procedure for the waypoint navigation would allow higher values for VFOV. Furthermore, the system can be improved by using two phases. In the first phase, the object search just tries to find something with a low resolution reducing the computational burden and increasing the possible sample time. The focus of the first phase is to overlook nothing. If it has a hit, the quadrocopter suspends the waypoint search and flies to the position of the hit. Then, the second phase is executed using a high resolution and accuracy and only in this phase the accepted position is determined. Computational burden is unimportant in the second phase because the quadro‐ copter is on position hold.

A different approach with a moving camera and flexible height could also be investigated. In this case, the quadrocopter would possibly not need to search the whole area or at least the waypoint list could be much smaller. In our setup, the quadrocopter could simply fly 4 m up and could see the complete search area. But that will not be possible in every situation as usually rooms are not that high. However, it needs to be compared that which accuracies and detection performance could be achieved then. Taking obstacles and unknown limitations into account as well as the fact that objects might not be detected properly from a distance and at an angle, this approach is much more sophisticated, but also offers more potential and might save flight time, and therefore could reduce the energy consumption.

Another interesting improvement would be to use the obstacle detection sensors to improve the position computation, and therefore the accuracy of the localizations. A challenging part here is a reasonable distribution of the limited resources of the LP‐180 to the different de‐ manding tasks.
