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

Master of Science

thesisThere has been much research on how to get unmanned aerial vehicles (UAVs) to perch on many different types of surfaces and objects, including flat surfaces, ramps, tree branches, power lines, etc. Many of these surfaces are static and it is easy to detect falls using inertial sensors such as accelerometers or gyroscopes. However, some perches, such as tree branches and power lines, are not static. When the UAV is perched on these perches, it will move with them, making the detection of falls from such a perch much more difficult than simply trying to sense motion. This thesis proposes two methods for fall detection of a UAV perched on such a dynamic perch. Computer vision is used on a feed from a camera mounted on the bottom of the UAV. Optical flow is used in conjunction with a filter that segments the perch in the image from the background to estimate the relative motion between the UAV and the perch. If the motion exceeds certain bounds, the UAV is considered falling. The second method tries to find the instantaneous center of rotation (ICR) of the UAV utilizing accelerometers and a gyroscope mounted to the UAV frame. Two methods are proposed to do this, one based on integrating the accelerometers to find the velocity at a point, the other finds the distance between the ICR and three points on the rigid frame of the UAV. The ICR estimates from these two methods are compared to an ICR estimate derived from data from an external Vicon motion capture system. The estimated ICR is then compared to the ICR of the perch that the UAV has perched on, if the two diverge enough, the perch is considered to be falling. The proposed methods were tested experimentally by placing a test quadrotor fitted with the appropriate sensors on three different test perches: a painted PVC pipe, a PVC pipe with a swirl pattern on it, and a tree branch. The quadrotor and perch are then actuated in three different tests. The first test has the quadrotor rotating about the perch while the perch is static. The second test has the quadrotor swinging on the perch without slipping. In the final test, the swing perch angle is increased until the quadrotor falls off. Each of the 9 tests is performed 5 times. Accelerometer, gyroscope, and vision data are gathered during these tests and analyzed using the methods described in this thesis. These experiments show that the vision method works fairly well, and that the ICR method works to a degree, but there is more work to be done in that area

Visual Servoing of Quadrotors for Perching by Hanging From Cylindrical Objects

This paper addresses vision-based localization and servoing for quadrotors to enable autonomous perching by hanging from cylindrical structures using only a monocular camera. We focus on the problems of relative pose estimation, control, and trajectory planning for maneuvering a robot relative to cylinders with unknown orientations. We first develop a geometric model that describes the pose of the robot relative to a cylinder. Then, we derive the dynamics of the system, expressed in terms of the image features. Based on the dynamics, we present a controller which guarantees asymptotic convergence to the desired image space coordinates. Finally, we develop an effective method to plan dynamically-feasible trajectories in the image space, and we provide experimental results to demonstrate the proposed method under different operating conditions such as hovering, trajectory tracking, and perching

Perception-Aware Perching on Powerlines With Multirotors

Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection without human intervention, the robots must be able to perch on the powerlines to recharge their batteries. Highly versatile perching capabilities are necessary to adapt to the variety of configurations and constraints that are present in real powerline systems. This letter presents a novel perching trajectory generation framework that computes perception-aware, collision-free, and dynamically-feasible maneuvers to guide the robot to the desired final state. Trajectory generation is achieved via solving a Nonlinear Programming problem using the Primal-Dual Interior Point method. The problem considers the full dynamic model of the robot down to its single rotor thrusts and minimizes the final pose and velocity errors while avoiding collisions and maximizing the visibility of the powerline during the maneuver. The generated maneuvers consider both the perching and the posterior recovery trajectories. The framework adopts costs and constraints defined by efficient mathematical representations of powerlines, enabling online onboard execution in resource-constrained hardware. The method is validated on-board an agile quadrotor conducting powerline inspection and various perching maneuvers with final pitch values of up to 180 ∘ . The developed code is available online at: https://github.com/grvcPerception/pa_powerline_perchin

Enabling technologies for precise aerial manufacturing with unmanned aerial vehicles

The construction industry is currently experiencing a revolution with automation techniques such as additive manufacturing and robot-enabled construction. Additive Manufacturing (AM) is a key technology that can o er productivity improvement in the construction industry by means of o -site prefabrication and on-site construction with automated systems. The key bene t is that building elements can be fabricated with less materials and higher design freedom compared to traditional manual methods. O -site prefabrication with AM has been investigated for some time already, but it has limitations in terms of logistical issues of components transportation and due to its lack of design exibility on-site. On-site construction with automated systems, such as static gantry systems and mobile ground robots performing AM tasks, can o er additional bene ts over o -site prefabrication, but it needs further research before it will become practical and economical. Ground-based automated construction systems also have the limitation that they cannot extend the construction envelope beyond their physical size. The solution of using aerial robots to liberate the process from the constrained construction envelope has been suggested, albeit with technological challenges including precision of operation, uncertainty in environmental interaction and energy e ciency. This thesis investigates methods of precise manufacturing with aerial robots. In particular, this work focuses on stabilisation mechanisms and origami-based structural elements that allow aerial robots to operate in challenging environments. An integrated aerial self-aligning delta manipulator has been utilised to increase the positioning accuracy of the aerial robots, and a Material Extrusion (ME) process has been developed for Aerial Additive Manufacturing (AAM). A 28-layer tower has been additively manufactured by aerial robots to demonstrate the feasibility of AAM. Rotorigami and a bioinspired landing mechanism demonstrate their abilities to overcome uncertainty in environmental interaction with impact protection capabilities and improved robustness for UAV. Design principles using tensile anchoring methods have been explored, enabling low-power operation and explores possibility of low-power aerial stabilisation. The results demonstrate that precise aerial manufacturing needs to consider not only just the robotic aspects, such as ight control algorithms and mechatronics, but also material behaviour and environmental interaction as factors for its success.Open Acces

Trajectory Generation and Control for Quadrotors

This thesis presents contributions to the state-of-the-art in quadrotor control, payload transportation with single and multiple quadrotors, and trajectory generation for single and multiple quadrotors. In Ch. 2 we describe a controller capable of handling large roll and pitch angles that enables a quadrotor to follow trajectories requiring large accelerations and also recover from extreme initial conditions. In Ch. 3 we describe a method that allows teams of quadrotors to work together to carry payloads that they could not carry individually. In Ch. 4 we discuss an online parameter estimation method for quadrotors transporting payloads which enables a quadrotor to use its dynamics in order to learn about the payload it is carrying and also adapt its control law in order to improve tracking performance. In Ch. 5 we present a trajectory generation method that enables quadrotors to fly through narrow gaps at various orientations and perch on inclined surfaces. Chapter 6 discusses a method for generating dynamically optimal trajectories through a series of predefined waypoints and safe corridors and Ch. 7 extends that method to enable heterogeneous quadrotor teams to quickly rearrange formations and avoid a small number of obstacles

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

Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers

Inverted landing in a rapid and robust manner is a challenging feat for aerial robots, especially while depending entirely on onboard sensing and computation. In spite of this, this feat is routinely performed by biological fliers such as bats, flies, and bees. Our previous work has identified a direct causal connection between a series of onboard visual cues and kinematic actions that allow for reliable execution of this challenging aerobatic maneuver in small aerial robots. In this work, we first utilized Deep Reinforcement Learning and a physics-based simulation to obtain a general, optimal control policy for robust inverted landing starting from any arbitrary approach condition. This optimized control policy provides a computationally-efficient mapping from the system's observational space to its motor command action space, including both triggering and control of rotational maneuvers. This was done by training the system over a large range of approach flight velocities that varied with magnitude and direction. Next, we performed a sim-to-real transfer and experimental validation of the learned policy via domain randomization, by varying the robot's inertial parameters in the simulation. Through experimental trials, we identified several dominant factors which greatly improved landing robustness and the primary mechanisms that determined inverted landing success. We expect the learning framework developed in this study can be generalized to solve more challenging tasks, such as utilizing noisy onboard sensory data, landing on surfaces of various orientations, or landing on dynamically-moving surfaces.Comment: 8 pages, 6 Figures, Submitted for ICRA 2023 Conference (Pending Review