Développement d'un drone percheur pour atterrissage et grimpe sur des surfaces verticales

Ce projet visait le développement du premier drone à aile fixe capable de se percher de façon autonome sur des surfaces verticales et d'en décoller. Inspiré par les oiseaux, l'avion développé utilise une manoeuvre de cabrage assistée par la poussée pour rapidement ralentir avant de se poser. Des microgriffes sont utilisées pour permettre à l'avion de s'accrocher à des surfaces rugueuses, alors que le contrôle de la manoeuvre est entièrement embarqué. L'effet de la poussée aérodynamique sur l'enveloppe d'atterrissage de l'avion est analysée et un contrôleur de vitesse verticale est proposé pour créer des descentes fluides et robustes vers un mur. Plusieurs atterissages ont été testé, à travers une plage de conditions de vol. La poussée aérodynamique de l'avion est également utilisée pour grimper le long de surfaces verticales. Des modèles aérodynamiques sont utilisés pour prédire les performance de l'avion dans plusieurs régimes de grimpe aérienne, et sélectionner un contrôleur pour le maintien d'une distance fixe avec un mur en montée verticale. La manœuvre de grimpe est testée à l'intérieur et à l'extérieur, pour des grimpes courtes et longues

LeggedWalking on Inclined Surfaces

The main contribution of this MS Thesis is centered around taking steps towards successful multi-modal demonstrations using Northeastern's legged-aerial robot, Husky Carbon. This work discusses the challenges involved in achieving multi-modal locomotion such as trotting-hovering and thruster-assisted incline walking and reports progress made towards overcoming these challenges. Animals like birds use a combination of legged and aerial mobility, as seen in Chukars' wing-assisted incline running (WAIR), to achieve multi-modal locomotion. Chukars use forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and overhangs. Husky's design takes inspiration from birds such as Chukars. This MS thesis presentation outlines the mechanical and electrical details of Husky's legged and aerial units. The thesis presents simulated incline walking using a high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.Comment: Masters thesi

On Aerial Robots with Grasping and Perching Capabilities: A Comprehensive Review

Over the last decade, there has been an increased interest in developing aerial robotic platforms that exhibit grasping and perching capabilities not only within the research community but also in companies across different industry sectors. Aerial robots range from standard multicopter vehicles/drones, to autonomous helicopters, and fixed-wing or hybrid devices. Such devices rely on a range of different solutions for achieving grasping and perching. These solutions can be classified as: 1) simple gripper systems, 2) arm-gripper systems, 3) tethered gripping mechanisms, 4) reconfigurable robot frames, 5) adhesion solutions, and 6) embedment solutions. Grasping and perching are two crucial capabilities that allow aerial robots to interact with the environment and execute a plethora of complex tasks, facilitating new applications that range from autonomous package delivery and search and rescue to autonomous inspection of dangerous or remote environments. In this review paper, we present the state-of-the-art in aerial grasping and perching mechanisms and we provide a comprehensive comparison of their characteristics. Furthermore, we analyze these mechanisms by comparing the advantages and disadvantages of the proposed technologies and we summarize the significant achievements in these two research topics. Finally, we conclude the review by suggesting a series of potential future research directions that we believe that are promising

Modélisation et asservissement du décollage vertical d’un drone aquatique à voilure fixe

Les nouvelles applications des petits drones volants prolifèrent grâce à leur récent gain de popularité. Un désavantage majeur de ces appareils est leur court temps de vol. Il est possible d’allonger la durée d’une mission d’un petit drone en effectuant une série de vols intermittents, possibles si l’appareil se pose sur un plan d’eau pour se recharger grâce à l’énergie solaire. La capacité de fonctionner dans les airs et sur l’eau ouvre également de nouveau champs d’application, comme les patrouilles maritimes et des suivis écologiques. L’appareil développé à l’Université de Sherbrooke, le SUWAVE, vise à combiner les avantages des petits drones volants à ceux des modèles hybrides, aquatiques et aériens. Contrairement à d’autres avions aquatiques, le SUWAVE effectue un décollage vertical à partir de l’eau. Le présent projet de recherche vise à mieux comprendre la dynamique d’une telle manoeuvre afin de concevoir le contrôle approprié pour garantir son succès, malgré les perturbations du vent et des vagues

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

Biologically inspired perching for aerial robots

2021 Spring.Includes bibliographical references.Micro Aerial Vehicles (MAVs) are widely used for various civilian and military applications (e.g., surveillance, search, and monitoring, etc.); however, one critical problem they are facing is the limited airborne time (less than one hour) due to the low aerodynamic efficiency, low energy storage capability, and high energy consumption. To address this problem, mimicking biological flyers to perch onto objects (e.g., walls, power lines, or ceilings) will significantly extend MAVs' functioning time for surveillance or monitoring related tasks. Successful perching for aerial robots, however, is quite challenging as it requires a synergistic integration of mechanical and computational intelligence. Mechanical intelligence means mechanical mechanisms to passively damp out the impact between the robot and the perching object and robustly engage the robot to the perching objects. Computational intelligence means computation algorithms to estimate, plan, and control the robot's motion so that the robot can progressively reduce its speed and adjust its orientation to perch on the objects with a desired velocity and orientation. In this research, a framework for biologically inspired perching is investigated, focusing on both computational and mechanical intelligence. Computational intelligence includes vision-based state estimation and trajectory planning. Unlike traditional flight states such as position and velocity, we leverage a biologically inspired state called time-to-contact (TTC) that represents the remaining time to the perching object at the current flight velocity. A faster and more accurate estimation method based on consecutive images is proposed to estimate TTC. Then a trajectory is planned in TTC space to realize the faster perching while satisfying multiple flight and perching constraints, e.g., maximum velocity, maximum acceleration, and desired contact velocity. For mechanical intelligence, we design, develop, and analyze a novel compliant bistable gripper with two stable states. When the gripper is open, it can close passively by the contact force between the robot and the perching object, eliminating additional actuators or sensors. We also analyze the bistability of the gripper to guide and optimize the design of the gripper. At the end, a customized MAV platform of size 250 mm is designed to combine computational and mechanical intelligence. A Raspberry Pi is used as the onboard computer to do vision-based state estimation and control. Besides, a larger gripper is designed to make the MAV perch on a horizontal rod. Perching experiments using the designed trajectories perform well at activating the bistable gripper to perch while avoiding large impact force which may damage the gripper and the MAV. The research will enable robust perching of MAVs so that they can maintain a desired observation or resting position for long-duration inspection, surveillance, search, and rescue

Adaptive Morphology for Multi-Modal Locomotion

There is a growing interest in using robots in dangerous environments, such as for exploration, search-and-rescue or monitoring applications, in order to reduce the risks for workers or rescuers and to improve their efficiency. Typically, flying robots offer the possibility to quickly explore large areas while ground robots can thoroughly search specific regions of interest. While existing robotic solutions are very promising, they are often limited to specific use cases or environments. This makes them impractical for most missions involving complex or unpredictable scenarios, such as search-and-rescue applications. This limitation comes from the fact that existing robots usually exploit only a single locomotion strategy, which limits their flexibility and adaptability to different environments. In this thesis, a multi-modal locomotion strategy is investigated as a way to increase the versatility of mobile robots. We explore integrated design approaches, where the same actuators and structure are used for different modes of locomotion, which allows a minimization of the weight and complexity of the robot. This strategy is challenging because a single locomotor system must accommodate the potentially conflicting dynamics of multiple modes of locomotion. Herein, we suggest taking inspiration from nature, in particular the common vampire bat \emph{Desmodus rotundus}. The goal being to make multiple modes of locomotion dynamically compatible (i.e. have compatible speeds and torques requirements), by optimizing the morphology of the locomotor system and even by adapting the morphology of the robot to a specific mode of locomotion. It is demonstrated in this thesis that the integrated design approach can be effectively implemented on a multi-modal aerial and terrestrial robot, and that two modes of locomotion can be made dynamically compatible by optimizing the morphology. Furthermore, an adaptive morphology is used to increase the efficiency of the different modes of locomotion. A locomotor system used both for walking on the ground and controlling flight, has been successfully implemented on a multi-modal robot, which further has deployable wings to increase its performances on the ground and in the air. By successfully exploiting the concepts of integrated design and adaptive morphology, this robot is capable of hovering, forward flight and ground locomotion. This robot demonstrates a very high versatility compared to state of the art of mobile robots, while having a low complexity

Egospace Motion Planning Representations for Micro Air Vehicles

Navigation of micro air vehicles (MAVs) in unknown environments is a complex sensing and trajectory generation task, particularly at high velocities. In this work, we introduce an efficient sense-and-avoid pipeline that compactly represents range measurements from multiple sensors, trajectory generation, and motion planning in a 2.5–dimensional projective data structure called an egospace representation. Egospace coordinates generalize depth image obstacle representations and are a particularly convenient choice for configuration flat mobile robots, which are differentially flat in their configuration variables and include a number of commonly used MAV plant models. After characterizing egospace obstacle avoidance for robots with trivial dynamics and establishing limits on applicability and performance, we generalize to motion planning over full configuration flat dynamics using motion primitives expressed directly in egospace coordinates. In comparison to approaches based on world coordinates, egospace uses the natural sensor geometry to combine the benefits of a multi-resolution and multi-sensor representation architecture into a single simple and efficient layer. We also present an experimental implementation, based on perception with stereo vision and an egocylinder obstacle representation, that demonstrates the specialization of our theoretical results to particular mission scenarios. The natural pixel parameterization of the egocylinder is used to quickly identify dynamically feasible maneuvers onto radial paths, expressed directly in egocylinder coordinates, that enable finely detailed planning at extreme ranges within milliseconds. We have implemented our obstacle avoidance pipeline with an Asctec Pelican quadcopter, and demonstrate the efficiency of our approach experimentally with a set of challenging field scenarios. The scalability potential of our system is discussed in terms of sensor horizon, actuation, and computational limitations and the speed limits that each imposes, and its generality to more challenging environments with multiple moving obstacles is developed as an immediate extension to the static framework