Robust post-stall perching with a simple fixed-wing glider using LQR-Trees

Birds routinely execute post-stall maneuvers with a speed and precision far beyond the capabilities of our best aircraft control systems. One remarkable example is a bird exploiting post-stall pressure drag in order to rapidly decelerate to land on a perch. Stall is typically associated with a loss of control authority, and it is tempting to attribute this agility of birds to the intricate morphology of the wings and tail, to their precision sensing apparatus, or their ability to perform thrust vectoring. Here we ask whether an extremely simple fixed-wing glider (no propeller) with only a single actuator in the tail is capable of landing precisely on a perch from a large range of initial conditions. To answer this question, we focus on the design of the flight control system; building upon previous work which used linear feedback control design based on quadratic regulators (LQR), we develop nonlinear feedback control based on nonlinear model-predictive control and 'LQR-Trees'. Through simulation using a flat-plate model of the glider, we find that both nonlinear methods are capable of achieving an accurate bird-like perching maneuver from a large range of initial conditions; the 'LQR-Trees' algorithm is particularly useful due to its low computational burden at runtime and its inherent performance guarantees. With this in mind, we then implement the 'LQR-Trees' algorithm on real hardware and demonstrate a 95 percent perching success rate over 147 flights for a wide range of initial speeds. These results suggest that, at least in the absence of significant disturbances like wind gusts, complex wing morphology and sensing are not strictly required to achieve accurate and robust perching even in the post-stall flow regime.United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-10-1-0951)National Science Foundation (U.S.) (Award IIS-0915148

Safety Control Synthesis with Input Limits: a Hybrid Approach

We introduce a hybrid (discrete--continuous) safety controller which enforces strict state and input constraints on a system---but only acts when necessary, preserving transparent operation of the original system within some safe region of the state space. We define this space using a Min-Quadratic Barrier function, which we construct along the equilibrium manifold using the Lyapunov functions which result from linear matrix inequality controller synthesis for locally valid uncertain linearizations. We also introduce the concept of a barrier pair, which makes it easy to extend the approach to include trajectory-based augmentations to the safe region, in the style of LQR-Trees. We demonstrate our controller and barrier pair synthesis method in simulation-based examples.Comment: 6 pages, 7 figures. Accepted for publication at the 2018 American Controls Conference. Copyright IEEE 201

Supermaneuverable perching

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 83-88).Birds have the impressive ability to gracefully 'swim' through the air while executing aerobatic maneuvers that routinely defy modern aeronautical and control engineering, consistently reminding us that the skies are truly their playground. These animals are masters at inducing and exploiting post-stall aerodynamics to quickly execute maneuvers with unprecedented precision, with nowhere near the sustained propulsive power found in modern state-of-the-art aircraft. This amazing ability to manipulate the air is commonly attributed to the intricate morphology of the wings, tail, feathers and overall sensory motor system of the animal. In this thesis we demonstrate, on real hardware, that using only an approximate model of the post-stall aerodynamics in combination with principled and novel tools in optimal control, even a simple fixed-wing foam glider (no propeller) made out of rigid flat plates, with a single actuator at the tail, is capable of executing a highly dynamic bird-like perching maneuver to land on a power-line by exploiting pressure drag on its stalled wings and tail. We present a feedback controller capable of stabilizing the maneuver over a wide range of flight speeds and quantify its robustness to wind-gust disturbances. In order to better characterize the aerodynamics during perching, we performed smoke-visualization in a low-speed free flight wind-tunnel, where we were able to capture real images of the dominant vortex wake dynamics. We describe the application of these results to the synthesis of higher fidelity aerodynamic models. We also demonstrate our initial perching experiments with flapping wings, using a flapping-wing version of our glider as well as our fully computerized two-meter wingspan robotic bird, Phoenix.by Rick E. Cory.Ph.D

Master of Science

thesisFlying rotorcraft, such as helicopters and quadrotors, can gather useful information without the need for human presence, but they consume a great deal of power and have limited on-board energy resources. Our work aims to provide a passive perching mechanism so that a rotorcraft is able to grip branch-like perches and resist external wind disturbances, using only the weight of the rotorcraft to maintain the grip. Deviating from previous bio-inspired approaches, in this thesis, we propose a mechanism that incorporates a Sarrus linkage to convert the weight of the rotorcraft into grip force. We provide an analysis of the mechanism's kinematics, we present the static force equations that describe how the weight of the rotorcraft is converted into grip force onto a cylindrical perch, and we describe how grip forces relate to the ability to reject horizontal disturbances such as wind gusts. The mechanism is then optimized for use on a single perch size, and then for a range of perch sizes. We conclude by constructing a prototype mechanism, and we demonstrate its use with a remote-controlled helicopter

High-Performance Testbed for Vision-Aided Autonomous Navigation for Quadrotor UAVs in Cluttered Environments

This thesis presents the development of an aerial robotic testbed based on Robot Operating System (ROS). The purpose of this high-performance testbed is to develop a system capable of performing robust navigation tasks using vision tools such as a stereo camera. While ensuring the computation of robot odometery, the system is also capable of sensing the environment using the same stereo camera. Hence, all the navigation tasks are performed using a stereo camera and an inertial measurement unit (IMU) as the main sensor suite. ROS is used as a framework for software integration due to its capabilities to provide efficient communication and sensor interfaces. Moreover, it also allows us to use C++ which is efficient in performance especially on embedded platforms. Combining together ROS and C++ provides the necessary computation efficiency and tools to handle fast, real-time image processing and planning which are the vital parts of navigation and obstacle avoidance on such scale. The main application of this work revolves around proposing a real-time and efficient way to demonstrate vision-based navigation in UAVs. The proposed approach is developed for a quadrotor UAV which is capable of performing defensive maneuvers in case any obstacles are in its way, while constantly moving towards a user-defined final destination. Stereo depth computation adds a third axis to a two dimensional image coordinate frame. This can be referred to as the depth image space or depth image coordinate frame. The idea of planning in this frame of reference is utilized along with certain precomputed action primitives. The formulation of these action primitives leads to a hybrid control law for feasible trajectory generation. Further, a proof of stability of this system is also presented. The proposed approach keeps in view the fact that while performing fast maneuvers and obstacle avoidance simultaneously, many of the standard optimization approaches might not work in real-time on-board due to time and resource limitations. This leads to a need for the development of real-time techniques for vision-based autonomous navigation

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page

Perspectives in control of conditionally controllable problems

Limitations imposed on control functions can significantly affect the performance of a linear controller. When applied to the real physical system, such limitations convert a linear function to a nonlinear input signal that alters the convergence or stability of the solution. The main focus of this study is to identify, classify and propose appropriate techniques to overcome such problems. In this regard, we propose an exact definition for a conditionally controllable problem and investigate control function formulations for such problems under the lenses of planning-based and optimization-based methods

Grasping, Perching, And Visual Servoing For Micro Aerial Vehicles

Micro Aerial Vehicles (MAVs) have seen a dramatic growth in the consumer market because of their ability to provide new vantage points for aerial photography and videography. However, there is little consideration for physical interaction with the environment surrounding them. Onboard manipulators are absent, and onboard perception, if existent, is used to avoid obstacles and maintain a minimum distance from them. There are many applications, however, which would benefit greatly from aerial manipulation or flight in close proximity to structures. This work is focused on facilitating these types of close interactions between quadrotors and surrounding objects. We first explore high-speed grasping, enabling a quadrotor to quickly grasp an object while moving at a high relative velocity. Next, we discuss planning and control strategies, empowering a quadrotor to perch on vertical surfaces using a downward-facing gripper. Then, we demonstrate that such interactions can be achieved using only onboard sensors by incorporating vision-based control and vision-based planning. In particular, we show how a quadrotor can use a single camera and an Inertial Measurement Unit (IMU) to perch on a cylinder. Finally, we generalize our approach to consider objects in motion, and we present relative pose estimation and planning, enabling tracking of a moving sphere using only an onboard camera and IMU