This chapter presents a reliable control system for a low cost visual driverless car in city streets. In comparison with the important research results obtained by high funded projects, this approach uses just one camera to follow a line on the street. No GPS information has been used to position the vehicle. A Fuzzy controller was developed to control the steering wheel of the vehicle. The vehicle uses a visual algorithm to follow a line painted on the street. To control the illumination of the environment a metal structure holding the camera and the UV bulb was designed and installed on the front of the car. The field of view was limited by this structure being just 30 x 50 cm. A mark detection system was developed to give location information and extra data to the control system about the curvature’s size and length of the sector in which the vehicle is. A big amount of tests in different conditions were presented. A maximum speed of 48 km/h was reach with excellent controller response. Distance of more than 6 km was covered with a RMSE test’s value of 3.6874 cm. The system was also tested without mark detection with lower speed range. The system was able to cover a distance of 3 km and response to emergency stops. A set of step response tests were done with and without mark detection to check the behavior of the system. The characteristics of the system make it ideal for autonomous public trans- port inside a city, where it is hard to use lane detection because of the high density of occlusion caused by other vehicles and shadows of buildings, trees and more. The big limitations of the field of view of this approach and the robustness of the system warrant further work to create a system not dependent on a painted line that can be used outside of cities. The developed control system could be adjusted to use more visual information, for example with one or more cameras on the top of the vehicle and using a road lane detector. More sensors could be added to increase the capabilities of the developed system, like a laserscanner to accomplish the task of obstacle avoidance, or a GPS to get the information that is currently obtained by the mark detection.
Chapter
4
Fuzzy Logic Control for Unmanned
Helicopters
This chapter presents the works done with Fuzzy control and a sophisticated Radio controlled helicopter. Two applications were presented to prove the power of the Fuzzy logic technique for control. Both applications use the visual in- formation acquired by onboard cameras. The helicopter used for this test is a commercial RC helicopter adapted for be controlled autonomously by an on- board computer. The helicopter description is presented in Section 4.2. The first application is about the control of an onboard visual pan and tilt platform and the heading of the helicopter. This application is focused to be an eye in the sky for tracking objects and surveillance tasks. This is presented in Section 4.3. The second application presents the Fuzzy control of the helicopter for autonomous
publication related to this chapter:
-“A pan-tilt camera fuzzy vision controller on an unmanned aerial vehicle”, IEEE-IROS 2009 -“Unmanned aerial vehicles UAVs attitude, height, motion estimation and control using visual systems”, Journal of Autonomous Robots, 2010
-“Fuzzy controller for UAV-landing task using 3d-position visual estimation”, IEEE-FUZZ 2010 (WCCI 2010)
-“Visual servoing using fuzzy controllers on an unmanned aerial vehicle”, EUROFUSE 2010 -“Visual Servoing for UAVs”, InTech, Book chapter, 2010
-“Non-symmetric membership function for Fuzzy-based visual servoing on-board a UAV”, Com- putation Intelligence Foundations and Applications, Book chapter, 2010
54 4.1. Related Works
landing tasks. Firstly is presented the control of the thrust of the aircraft, and sec- ondly the control of pitch, roll and thrust for a fully autonomous landing. These work are presented in Section 4.4.
4.1.
Related Works
The unmanned aerial vehicles (UAV) have made its way quickly and deci- sively to the forefront of current aviation technology. Opportunities exist in a broadening number of fields for the application of UAV systems as the com- ponents of these systems become increasingly lighter and more powerful. Of particular interest are those occupations that require the execution of missions which depend heavily on dull, dirty, or dangerous work, UAVs provide a cheap, safe alternative to manned systems and often provide a far greater magnitude of capability.
Many applications were developed and shown in the literature, as is previ- ously mentioned in the Chapter 2. Here are presented the related works of the presented applications.
The control of an onboard visual pan and tilt platform, increase the possibili- ties to detect and track objects. In all of the visual control related works the UAV must change its position to track the object, as is shown in (Mejias et al., 2006a), (Campoy et al., 2009b). The use of a visual platform reduce the limitations of the UAV’s movements, because the aircraft can change its position according the surrounding limitations, while the servo assisted camera continue tracking the object . Related with this work, there are some laboratory tests works made, like (Zou et al., 2006) based on the tracking on a biomimetic Eye. (Dobrokhodov et al., 2006) performed real tests of target tracking and motion estimation for moving target. (Chitrakaran et al., 2006), also performed real tests of a vision assisted autonomous path following. The visual algorithm used to track is also an important topic related to this work. In the presented tests we use pyramidal Lucas-Kanade based algorithm as is presented in 4.3.1. There is a large diversity of tracking methods from which approaches can be mention based on features (Mikolajczyk and Smid, 2005), direct methods (correlation)(Irani and Anandan, 2000), color and shape algorithms among others. The goal of any visual tracking system is to be able to identify a reference pattern correctly and continuously on the image plane independently from the variations presented on the image se- quence with respect to parameters like camera or scene rotations and translations, object occlusions, illumination changes and noise, among others.
Different works have also been done where a vision system was used for low altitude position estimation and autonomous landing. In the work presented in (De Wagter and Mulder, 2005), the authors have evaluated the use of visual information at different stages of a UAV control system, including a visual con- troller and a pose estimation for autonomous landing using a chessboard pattern. Merz (Merz et al., 2004), (Merz et al., 2006), uses a method that fuses visual and inertial information in order to control an autonomous VTOL aircraft landing on