| Unmanned Aerial Vehicles (UAVs) are often used for reconnaissance, search and rescue, damage assessment, exploration, and other tasks that are dangerous or prohibitively difficult for humans to perform. Often, these tasks include traversing indoor environments where radio links are unreliable, hindering the use of remote pilot links or ground-based control, and effectively eliminating Global Positioning System (GPS) signals as a potential localization method. As a result, any vehicle capable of indoor flight must be able to stabilize itself and perform all guidance, navigation, and control (GNC) tasks without dependence on a radio link, which may be available only intermittently.;Stability and control of rotorcraft UAVs is usually achieved by either a passive stability system, such as a Bell stabilizer bar, or by actively measuring body accelerations and angular rates with an onboard Inertial Measurement Unit (IMU) and using that data for feedback control. However, neither active nor passive attitude stabilization methods provide position control by themselves. Therefore, GNC methods must either be tolerant to position drift or have some means of estimating and controlling position, which requires an external reference in order to measure and correct errors in the position estimate. GPS signals are often the most convenient method for providing this external position reference. As a result, most UAVs utilize GPS for localization and to bound error on position drift.;Unfortunately, the availability of GPS signals in unknown environments is not assured, especially during indoor operation. As a result, other sensors must be used to provide position information relative to the environment. This research covers a description of different ranging sensors and methods for incorporating them into the overall guidance, navigation, and control system. Various sensors are analyzed to determine their performance characteristics and suitability for indoor navigation, including sonar, infrared (IR) range sensors, and a scanning laser rangefinder. Each type of range sensor tested has its own unique characteristics and contributes in a slightly different way to effectively eliminate the dependence on GPS. A discussion of vehicle selection criteria is provided, with an analysis of the pros and cons associated with passively versus actively stabilized vehicles. Two levels of navigation capability are presented, each providing different levels of mission performance.;First, the use of low-cost range sensors on an inexpensive passively stabilized coaxial helicopter for drift-tolerant indoor navigation is demonstrated through simulation and flight test. The system developed and own shows promise for future miniaturization, enabling vehicles to be easily transported in large quantities to remote areas. One potential use for such a vehicle would be the distribution of small lightweight sensors deep into a mine or cave through a small opening. Figure 1(a) shows an example of such a deployment. Other sensors, including video cameras and microphones, could also be used in a variety of similar scenarios where access is limited.;For situations where some level of map building may be required, as shown in Figure 1(b), additional capabilities are needed. Hence, this thesis also describes a system with a scanning laser rangefinder mounted onboard a quadrotor helicopter with an IMU to enable active stabilization. Position control is demonstrated in simulation via navigation algorithms that utilize Simultaneous Localization and Mapping (SLAM) techniques. Two different algorithms are evaluated for suitability for use with an IMU-stabilized flying vehicle. Simulation and experimental results of the navigation system are provided, including two novel approaches to extracting useful information about the environment from laser scan data. Thus, the research presented in this thesis demonstrates new capabilities for autonomous UAV operation in adverse unknown environments, specifically those in which GPS signals are unavailable. |
