4,243 research outputs found
Toward Indoor Flying Robots
Developing a research autonomous plane for flying in a laboratory space is a challenge that forces one to understand the specific aerodynamic, power and construction constraints. In order to obtain a very slow flight while maintaining a high maneuverability, ultra-light structures and adequate components are required. In this paper we analyze the wing, propeller and motor characteristics and propose a methodology to optimize the motor/gear/propeller system. The C4 model plane (50g, 1.5m/s) demonstrates the feasibility of such a laboratory flying test-bed
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
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
From Monocular SLAM to Autonomous Drone Exploration
Micro aerial vehicles (MAVs) are strongly limited in their payload and power
capacity. In order to implement autonomous navigation, algorithms are therefore
desirable that use sensory equipment that is as small, low-weight, and
low-power consuming as possible. In this paper, we propose a method for
autonomous MAV navigation and exploration using a low-cost consumer-grade
quadrocopter equipped with a monocular camera. Our vision-based navigation
system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense
reconstruction of the environment in real-time. Since LSD-SLAM only determines
depth at high gradient pixels, texture-less areas are not directly observed so
that previous exploration methods that assume dense map information cannot
directly be applied. We propose an obstacle mapping and exploration approach
that takes the properties of our semi-dense monocular SLAM system into account.
In experiments, we demonstrate our vision-based autonomous navigation and
exploration system with a Parrot Bebop MAV
- …