Vision-based automatic landing of a rotary UAV

A hybrid-like (continuous and discrete-event) approach to controlling a small multi-rotor unmanned aerial system (UAS) while landing on a moving platform is described. The landing scheme is based on positioning visual markers on a landing platform in a detectable pattern. After the onboard camera detects the object pattern, the inner control algorithm sends visual-based servo-commands to align the multi-rotor with the targets. This method is less computationally complex as it uses color-based object detection applied to a geometric pattern instead of feature tracking algorithms, and has the advantage of not requiring the distance to the objects to be calculated. The continuous approach accounts for the UAV and the platform rolling/pitching/yawing, which is essential for a real-time landing on a moving target such as a ship. A discrete-event supervisor working in parallel with the inner controller is designed to assist the automatic landing of a multi-rotor UAV on a moving target. This supervisory control strategy allows the pilot and crew to make time-critical decisions when exceptions, such as losing targets from the field of view, occur. The developed supervisor improves the low-level vision-based auto-landing system and high-level human-machine interface. The proposed hybrid-like approach was tested in simulation using a quadcopter model in Virtual Robotics Experimentation Platform (V-REP) working in parallel with Robot Operating System (ROS). Finally, this method was validated in a series of real-time experiments with indoor and outdoor quadcopters landing on both static and moving platforms. The developed prototype system has demonstrated the capability of landing within 25 cm of the desired point of touchdown. This auto-landing system is small (100 x 100 mm), light-weight (100 g), and consumes little power (under 2 W)

Composite prototyping and vision based hierarchical control of a quad tilt-wing UAV

As the attention to unmanned systems is increasing, unmanned aerial vehicles (UAVs) are becoming more popular based on the rapid advances in technology and growth in operational experience. The main motivation in this vast research field is to diminish the human driven tasks by employing UAVs in critical civilian and military tasks such as traffic monitoring, disasters, surveillance, reconnaissance and border security. Researchers have been developing featured UAVs with intelligent navigation and control systems on more efficient designs aiming to increase the functionality, flight time and maneuverability. This thesis focuses on the composite prototyping and vision based hierarchical control of a quad tilt-wing aerial vehicle (SUAVI: Sabanci University Unmanned Aerial VehIcle). With the tilt-wing mechanism, SUAVI is one of the most challenging UAV concepts by combining advantages of vertical take-off and landing (VTOL) and horizontal flight. Various composite materials are tested for their mechanical properties and the most suitable one is used for prototyping of the aerial vehicle. A hierarchical control structure which consists of high-level and low-level controllers is developed. A vision based high-level controller generates attitude references for the low-level controllers. A Kalman filter fuses data from low-cost inertial sensors to obtain reliable orientation information. Low-level controllers are typically gravity compensated PID controllers. An image based visual servoing (IBVS) algorithm for VTOL, hovering and trajectory tracking is successfully implemented in simulations. Real flight tests demonstrate satisfactory performance of the developed control algorithms

Investigation of advanced navigation and guidance system concepts for all-weather rotorcraft operations

Results are presented of a survey conducted of active helicopter operators to determine the extent to which they wish to operate in IMC conditions, the visibility limits under which they would operate, the revenue benefits to be gained, and the percent of aircraft cost they would pay for such increased capability. Candidate systems were examined for capability to meet the requirements of a mission model constructed to represent the modes of flight normally encountered in low visibility conditions. Recommendations are made for development of high resolution radar, simulation of the control display system for steep approaches, and for development of an obstacle sensing system for detecting wires. A cost feasibility analysis is included

Wide-Area Surveillance System using a UAV Helicopter Interceptor and Sensor Placement Planning Techniques

This project proposes and describes the implementation of a wide-area surveillance system comprised of a sensor/interceptor placement planning and an interceptor unmanned aerial vehicle (UAV) helicopter. Given the 2-D layout of an area, the planning system optimally places perimeter cameras based on maximum coverage and minimal cost. Part of this planning system includes the MATLAB implementation of Erdem and Sclaroff’s Radial Sweep algorithm for visibility polygon generation. Additionally, 2-D camera modeling is proposed for both fixed and PTZ cases. Finally, the interceptor is also placed to minimize shortest-path flight time to any point on the perimeter during a detection event. Secondly, a basic flight control system for the UAV helicopter is designed and implemented. The flight control system’s primary goal is to hover the helicopter in place when a human operator holds an automatic-flight switch. This system represents the first step in a complete waypoint-navigation flight control system. The flight control system is based on an inertial measurement unit (IMU) and a proportional-integral-derivative (PID) controller. This system is implemented using a general-purpose personal computer (GPPC) running Windows XP and other commercial off-the-shelf (COTS) hardware. This setup differs from other helicopter control systems which typically use custom embedded solutions or micro-controllers. Experiments demonstrate the sensor placement planning achieving \u3e90% coverage at optimized-cost for several typical areas given multiple camera types and parameters. Furthermore, the helicopter flight control system experiments achieve hovering success over short flight periods. However, the final conclusion is that the COTS IMU is insufficient for high-speed, high-frequency applications such as a helicopter control system

Improving Leader-Follower Formation Control Performance for Quadrotors

This thesis aims to improve the leader-follower team formation flight performance of Unmanned Aerial Vehicles (UAVs) by applying nonlinear robust and optimal techniques, in particular the nonlinear H_infinity and the iterative Linear Quadratic Regulator (iLQR), to stabilisation, path tracking and leader-follower team formation control problems. Existing solutions for stabilisation, path tracking and leader-follower team formation control have addressed a linear or nonlinear control technique for a linearised system with limited disturbance consideration, or for a nonlinear system with an obstacle-free environment. To cover part of this area of research, in this thesis, some nonlinear terms were included in the quadrotors' dynamic model, and external disturbance and model parameter uncertainties were considered. Five different controllers were developed. The first and the second controllers, the nonlinear suboptimal H_infinity control technique and the Integral Backstepping (IBS) controller, were based on Lyapunov theory. The H_infinity controller was developed with consideration of external disturbance and model parameter uncertainties. These two controllers were compared for path tracking and leader-follower team formation control. The third controller was the Proportional Derivative square (PD2), which was applied for attitude control and compared with the H_infinity controller. The fourth and the fifth controllers were the Linear Quadratic Regulator (LQR) control technique and the optimal iLQR, which was developed based on the LQR control technique. These were applied for attitude, path tracking and team formation control and there results were compared. Two features regarding the choice of the control technique were addressed: stability and robustness on the one hand, which were guaranteed using the H_infinity control technique as the disturbance is inherent in its mathematical model, and the improvement in the performance optimisation on the other, which was achieved using the iLQR technique as it is based on the optimal LQR control technique. Moreover, one loop control scheme was used to control each vehicle when these controllers were implemented and a distributed control scheme was proposed for the leader-follower team formation problem. Each of the above mentioned controllers was tested and verified in simulation for different predefined paths. Then only the nonlinear H_infinity controller was tested in both simulation and real vehicles experiments

Master of Science

thesisThis thesis details the development of the Algorithmic Robotics Laboratory, its experimental software environment, and a case study featuring a novel hardware validation of optimal reciprocal collision avoidance. We constructed a robotics laboratory in both software and hardware in which to perform our experiments. This lab features a netted flying volume with motion capture and two custom quadrotors. Also, two experimental software architectures are developed for actuating both ground and aerial robots within a Linux Robot Operating System environment. The first of the frameworks is based upon a single finite state machine program which managed each aspect of the experiment. Concerns about the complexity and reconfigurability of the finite state machine prompted the development of a second framework. This final framework is a multimodal structure featuring programs which focus on these specific functions: State Estimation, Robot Drivers, Experimental Controllers, Inputs, Human Robot Interaction, and a program tailored to the specifics of the algorithm tested in the experiment. These modular frameworks were used to fulfill the mission of the Algorithmic Robotics Lab, in that they were developed to validate robotics algorithms in experiments that were previously only shown in simulation. A case study into collision avoidance was used to mark the foundation of the laboratory through the proving of an optimal reciprocal collision avoidance algorithm for the first time in hardware. In the case study, two human-controlled quadrotors were maliciously flown in colliding trajectories. Optimal reciprocal collision avoidance was demonstrated for the first time on completely independent agents with local sensing. The algorithm was shown to be robust to violations of its inherent assumptions about the dynamics of agents and the ability for those agents to sense imminent collisions. These experiments, in addition to the mathematical foundation of exponential convergence, submits th a t optimal reciprocal collision avoidance is a viable method for holonomic robots in both 2-D and 3-D with noisy sensing. A basis for the idea of reciprocal dance, a motion often seen in human collision avoidance, is also suggested in demonstration to be a product of uncertainty about the state of incoming agents. In the more than one hundred tests conducted in multiple environments, no midair collisions were ever produced

Intelligent Portable Aerial Surveillance System

Unmanned Aerial Vehicles are critical to modern military operations. They are generally expensive and require several people to use and maintain. This report presents the development of a UAV that is inexpensive, one-man operable, and capable of short range surveillance. Based on requirements provided by the Air Force Research Laboratory, the team established a set of specifications to guide the design. The UAV is lightweight, durable, and small when disassembled. It is tele-operated, and displays image data from multiple cameras

Stochastic Real-time Optimal Control: A Pseudospectral Approach for Bearing-Only Trajectory Optimization

A method is presented to couple and solve the optimal control and the optimal estimation problems simultaneously, allowing systems with bearing-only sensors to maneuver to obtain observability for relative navigation without unnecessarily detracting from a primary mission. A fundamentally new approach to trajectory optimization and the dual control problem is developed, constraining polynomial approximations of the Fisher Information Matrix to provide an information gradient and allow prescription of the level of future estimation certainty required for mission accomplishment. Disturbances, modeling deficiencies, and corrupted measurements are addressed with recursive updating of the target estimate with an Unscented Kalman Filter and the optimal path with Radau pseudospectral collocation methods and sequential quadratic programming. The basic real-time optimal control (RTOC) structure is investigated, specifically addressing limitations of current techniques in this area that lose error integration. The resulting guidance method can be applied to any bearing-only system, such as submarines using passive sonar, anti-radiation missiles, or small UAVs seeking to land on power lines for energy harvesting. Methods and tools required for implementation are developed, including variable calculation timing and tip-tail blending for potential discontinuities. Validation is accomplished with simulation and flight test, autonomously landing a quadrotor helicopter on a wire