363 research outputs found
Autonomous Hybrid Ground/Aerial Mobility in Unknown Environments
Hybrid ground and aerial vehicles can possess distinct advantages over
ground-only or flight-only designs in terms of energy savings and increased
mobility. In this work we outline our unified framework for controls, planning,
and autonomy of hybrid ground/air vehicles. Our contribution is three-fold: 1)
We develop a control scheme for the control of passive two-wheeled hybrid
ground/aerial vehicles. 2) We present a unified planner for both rolling and
flying by leveraging differential flatness mappings. 3) We conduct experiments
leveraging mapping and global planning for hybrid mobility in unknown
environments, showing that hybrid mobility uses up to five times less energy
than flying only
Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation
Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
Robust Active Visual Perching with Quadrotors on Inclined Surfaces
Autonomous Micro Aerial Vehicles are deployed for a variety tasks including
surveillance and monitoring. Perching and staring allow the vehicle to monitor
targets without flying, saving battery power and increasing the overall mission
time without the need to frequently replace batteries. This paper addresses the
Active Visual Perching (AVP) control problem to autonomously perch on inclined
surfaces up to . Our approach generates dynamically feasible
trajectories to navigate and perch on a desired target location, while taking
into account actuator and Field of View (FoV) constraints. By replanning in
mid-flight, we take advantage of more accurate target localization increasing
the perching maneuver's robustness to target localization or control errors. We
leverage the Karush-Kuhn-Tucker (KKT) conditions to identify the compatibility
between planning objectives and the visual sensing constraint during the
planned maneuver. Furthermore, we experimentally identify the corresponding
boundary conditions that maximizes the spatio-temporal target visibility during
the perching maneuver. The proposed approach works on-board in real-time with
significant computational constraints relying exclusively on cameras and an
Inertial Measurement Unit (IMU). Experimental results validate the proposed
approach and shows the higher success rate as well as increased target
interception precision and accuracy with respect to a one-shot planning
approach, while still retaining aggressive capabilities with flight envelopes
that include large excursions from the hover position on inclined surfaces up
to 90, angular speeds up to 750~deg/s, and accelerations up to
10~m/s
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