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Results for “Micro Aerial Vehicles (MAVs)”

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15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multi-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft micro-aerial vehicle (MAV)

The subject matter described herein includes a modular and extensible approach to integrate noisy measurements from multiple heterogeneous sensors that yield either absolute or relative observations at different and varying time intervals, and to provide smooth and globally consistent estimates of position in real time for autonomous flight. We describe the development of the algorithms and software architecture for a new 1.9 kg MAV platform equipped with an IMU, laser scanner, stereo cameras, pressure altimeter, magnetometer, and a GPS receiver, in which the state estimation and control are performed onboard on an Intel NUC 3rd generation i3 processor. We illustrate the robustness of our framework in large-scale, indoor-outdoor autonomous aerial navigation experiments involving traversals of over 440 meters at average speeds of 1.5 m/s with winds around 10 mph while entering and exiting buildings.

42 ENGINEERING↗
NASA NTRS2002

Flexible Wing Base Micro Aerial Vehicles: Micro Air Vehicles (MAVs) for Surveillance and Remote Sensor Delivery

Micro Air Vehicles (MAVs) will be developed for tracking individuals, locating terrorist threats, and delivering remote sensors, for surveillance and chemical/biological agent detection. The tasks are: (1) Develop robust MAV platform capable of carrying sensor payload. (2) Develop fully autonomous capabilities for delivery of sensors to remote and distant locations. The current capabilities and accomplishments are: (1) Operational electric (inaudible) 6-inch MAVs with novel flexible wing, providing superior aerodynamic efficiency and control. (2) Vision-based flight stability and control (from on-board cameras).

Ifju, Peter↗
NASA NTRS2013

4DoF Drift Free Navigation Using Inertial Cues and Optical Flow

In this paper, we describe a novel approach in fusing optical flow with inertial cues (3D acceleration and 3D angular velocities) in order to navigate a Micro Aerial Vehicle (MAV) drift free in 4DoF and metric velocity. Our approach only requires two consecutive images with a minimum of three feature matches. It does not require any (point) map nor any type of feature history. Thus it is an inherently failsafe approach that is immune to map and feature-track failures. With these minimal requirements we show in real experiments that the system is able to navigate drift free in all angles including yaw, in one metric position axis, and in 3D metric velocity. Furthermore, it is a power-on-and-go system able to online self-calibrate the inertial biases, the visual scale and the full 6DoF extrinsic transformation parameters between camera and IMU.

reconnissance↗
NASA NTRS2018

Towards Vision-based Safe Autonomous Landing of MAVs at Night

This paper is about vision-based navigation of Micro Aerial Vehicles (MAVs) at night. Despite it being dark almost half of the time, most of the work to date has addressed only daytime operations. Enabling autonomous night-time operation of MAVs with low SWaP on-board sensing capabilities is still an open problem in current robotics research. In this work, we take a step in this direction and study the problem in the context of vision-based autonomous landing at night using thermal-infrared cameras.

Delaune, Jeff↗
NASA NTRS2018

Online Self-Supervised Long-Range Scene Segmentation for MAVs

Recently, there have been numerous advances in the development of payload and power constrained lightweight Micro Aerial Vehicles (MAVs). As these robots aspire for high-speed autonomous flights in complex dynamic environments, robust scene understanding at long-range becomes critical. The problem is heavily characterized by either the limitations imposed by sensor capabilities for geometry-based methods, or the need for large-amounts of manually annotated training data required by data-driven methods. This motivates the need to build systems that have the capability to alleviate these problems by exploiting the complimentary strengths of both geometry and data-driven methods. In this paper, we take a step in this direction and propose a generic framework for adaptive scene segmentation using self-supervised online learning. We present this in the context of vision-based autonomous MAV flight, and demonstrate the efficacy of our proposed system through extensive experiments on benchmark datasets and real world field tests.

Matthies, Larry↗
NASA NTRS2011

Algorithm for Autonomous Landing

Because of their small size, high maneuverability, and easy deployment, micro aerial vehicles (MAVs) are used for a wide variety of both civilian and military missions. One of their current drawbacks is the vast array of sensors (such as GPS, altimeter, radar, and the like) required to make a landing. Due to the MAV s small payload size, this is a major concern. Replacing the imaging sensors with a single monocular camera is sufficient to land a MAV. By applying optical flow algorithms to images obtained from the camera, time-to-collision can be measured. This is a measurement of position and velocity (but not of absolute distance), and can avoid obstacles as well as facilitate a landing on a flat surface given a set of initial conditions. The key to this approach is to calculate time-to-collision based on some image on the ground. By holding the angular velocity constant, horizontal speed decreases linearly with the height, resulting in a smooth landing. Mathematical proofs show that even with actuator saturation or modeling/ measurement uncertainties, MAVs can land safely. Landings of this nature may have a higher velocity than is desirable, but this can be compensated for by a cushioning or dampening system, or by using a system of legs to grab onto a surface. Such a monocular camera system can increase vehicle payload size (or correspondingly reduce vehicle size), increase speed of descent, and guarantee a safe landing by directly correlating speed to height from the ground.

Kuwata, Yoshiaki↗
NASA NTRS2018

Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

Wedescribeanew,on-board,shortrangeperceptionsystem that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.

Matthies, Larry↗
NASA NTRS2022

Preliminary Airfoil Design for Low Reynolds Numbers

Advances in the fields of electronics, batteries, and electric motors have enabled broad adoption of small-scale aircraft for various terrestrial applications. Numerous Micro Aerial Vehicles (MAVs) and small Unmanned Aerial Vehicles (UAVs), both civil and military, are in use today. Operation at their design flight condition often requires rotors, propellers, or wings to perform at Reynolds numbers significantly below 500,000 [1]. Airfoil performance at low Reynolds numbers is generally characterized by low aerodynamic performance (i.e. the attainable lift-to-drag ratio) compared to performance of airfoils at higher Reynolds numbers. The critical chord-based Reynolds number for conventional airfoils lies around Rec = 100,000 to Rec = 500,000 [2]. Below this critical Reynolds number, laminar separation of the boundary layer is usually the cause for poor performance. Researchers have found that below the critical Reynolds number, flat and cambered plates can outperform smooth airfoils. Circular-arc cambered plates especially have remarkable performance advantages compared to conventional airfoil shapes [3].

airfoil↗
NASA NTRS2003

Mechanization and Control Concepts for Biologically Inspired Micro Aerial Vehicles

It is possible that MAV designs of the future will exploit flapping flight in order to perform missions that require extreme agility, such as rapid flight beneath a forest canopy or within the confines of a building. Many of nature's most agile flyers generate flapping motions through resonant excitation of an aeroelastically tailored structure: muscle tissue is used to excite a vibratory mode of their flexible wing structure that creates propulsion and lift. A number of MAV concepts have been proposed that would operate in a similar fashion. This paper describes an ongoing research activity in which mechanization and control concepts with application to resonant flapping MAVs are being explored. Structural approaches, mechanical design, sensing and wingbeat control concepts inspired by hummingbirds, bats and insects are examined. Experimental results from a testbed capable of generating vibratory wingbeat patterns that approximately match those exhibited by hummingbirds in hover, cruise, and reverse flight are presented.

Raney, David L.↗
NASA NTRS2001

New Concepts and Perspectives on Micro-Rotorcraft and Small Autonomous Rotary-Wing Vehicles

A key part of the strategic vision for rotorcraft research as identified by senior technologists within the Army/NASA Rotorcraft Division at NASA Ames Research Center is the development and use of small autonomous rotorcraft. Small autonomous rotorcraft are defined for the purposes of this paper to be a class of vehicles that range in size from rotary-wing micro air vehicles (MAVs) to larger, more conventionally sized, rotorcraft uninhabited aerial vehicles (UAVs) - i.e. vehicle gross weights ranging from hundreds of grams to thousands of kilograms. The development of small autonomous rotorcraft represents both a technology challenge and a potential new vehicle class that will have substantial societal impact for: national security, personal transport, planetary science, and public service.

Young, Larry A.↗
NASA NTRS2002

Flexible Wing Base Micro Aerial Vehicles: Towards Flight Autonomy: Vision-Based Horizon Detection for Micro Air Vehicles

Recently substantial progress has been made towards design building and testifying remotely piloted Micro Air Vehicles (MAVs). This progress in overcoming the aerodynamic obstacles to flight at very small scales has, unfortunately, not been matched by similar progress in autonomous MAV flight. Thus, we propose a robust, vision-based horizon detection algorithm as the first step towards autonomous MAVs. In this paper, we first motivate the use of computer vision for the horizon detection task by examining the flight of birds (biological MAVs) and considering other practical factors. We then describe our vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification, over terrain that includes roads, buildings large and small, meadows, wooded areas, and a lake. We conclude with some sample horizon detection results and preview a companion paper, where the work discussed here forms the core of a complete autonomous flight stability system.

Nechyba, Michael C.↗
NASA NTRS2002

Flexible Wing Base Micro Aerial Vehicles: Vision-Guided Flight Stability and Autonomy for Micro Air Vehicles

Substantial progress has been made recently towards design building and test-flying remotely piloted Micro Air Vehicle's (MAVs). We seek to complement this progress in overcoming the aerodynamic obstacles to.flight at very small scales with a vision stability and autonomy system. The developed system based on a robust horizon detection algorithm which we discuss in greater detail in a companion paper. In this paper, we first motivate the use of computer vision for MAV autonomy arguing that given current sensor technology, vision may he the only practical approach to the problem. We then briefly review our statistical vision-based horizon detection algorithm, which has been demonstrated at 30Hz with over 99.9% correct horizon identification. Next we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feed-back controller for self-stabilized flight, and report results on vision autonomous flights of duration exceeding ten minutes.

Ettinger, Scott M.↗
NASA NTRS2002

Flexible Wing Base Micro Aerial Vehicles: Assessment of Controllability of Micro Air Vehicles

In the last several years, we have developed unique types of micro air vehicles that utilize flexible structures and extensible covering materials. These MAVs can be operated with maximum dimensions as small as 6 inches and carry reasonable payloads, such as video cameras and transmitters. We recently demonstrated the potential of these vehicles by winning the Fourth International Micro Air Vehicle Competition, held at Ft. Huachucha, Arizona in May 2000. The pilots report that these vehicles have unusually smooth flying characteristics and are relatively easy to fly, both in the standard RC mode and "through the camera" when at greater distances. In comparison, they find that similar sized vehicles with more conventional rigid construction require much more input from the pilot just to maintain control. To make these subjective observations more quantitative, we have devised a system that can conveniently record a complete history of all the RC transmitter stick movements during a flight. Post-flight processing of the stick movement data allows for direct comparisons between different types of MAVs when flown by the same pilot, and also comparisons between pilots. Eventually, practical micro air vehicles will be autonomously controlled, but we feel that the smoothest flying and easiest to fly embodiments will also be the most successful in the long run. Comparisons between several types of micro air vehicles will be presented, along with interpretations of the data.

Jenkins, David A.↗
NASA NTRS2012

Fully Self-Contained Vision-Aided Navigation and Landing of a Micro Air Vehicle Independent from External Sensor Inputs

Direct-lift micro air vehicles have important applications in reconnaissance. In order to conduct persistent surveillance in urban environments, it is essential that these systems can perform autonomous landing maneuvers on elevated surfaces that provide high vantage points without the help of any external sensor and with a fully contained on-board software solution. In this paper, we present a micro air vehicle that uses vision feedback from a single down looking camera to navigate autonomously and detect an elevated landing platform as a surrogate for a roof top. Our method requires no special preparation (labels or markers) of the landing location. Rather, leveraging the planar character of urban structure, the landing platform detection system uses a planar homography decomposition to detect landing targets and produce approach waypoints for autonomous landing. The vehicle control algorithm uses a Kalman filter based approach for pose estimation to fuse visual SLAM (PTAM) position estimates with IMU data to correct for high latency SLAM inputs and to increase the position estimate update rate in order to improve control stability. Scale recovery is achieved using inputs from a sonar altimeter. In experimental runs, we demonstrate a real-time implementation running on-board a micro aerial vehicle that is fully self-contained and independent from any external sensor information. With this method, the vehicle is able to search autonomously for a landing location and perform precision landing maneuvers on the detected targets.

autonomous landing↗
NASA NTRS2011

Feature and Pose Constrained Visual Aided Inertial Navigation for Computationally Constrained Aerial Vehicles

A Feature and Pose Constrained Extended Kalman Filter (FPC-EKF) is developed for highly dynamic computationally constrained micro aerial vehicles. Vehicle localization is achieved using only a low performance inertial measurement unit and a single camera. The FPC-EKF framework augments the vehicle's state with both previous vehicle poses and critical environmental features, including vertical edges. This filter framework efficiently incorporates measurements from hundreds of opportunistic visual features to constrain the motion estimate, while allowing navigating and sustained tracking with respect to a few persistent features. In addition, vertical features in the environment are opportunistically used to provide global attitude references. Accurate pose estimation is demonstrated on a sequence including fast traversing, where visual features enter and exit the field-of-view quickly, as well as hover and ingress maneuvers where drift free navigation is achieved with respect to the environment.

autonomous operation↗