603 research outputs found
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
FlightGoggles is a photorealistic sensor simulator for perception-driven
robotic vehicles. The key contributions of FlightGoggles are twofold. First,
FlightGoggles provides photorealistic exteroceptive sensor simulation using
graphics assets generated with photogrammetry. Second, it provides the ability
to combine (i) synthetic exteroceptive measurements generated in silico in real
time and (ii) vehicle dynamics and proprioceptive measurements generated in
motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of
simulating a virtual-reality environment around autonomous vehicle(s). While a
vehicle is in flight in the FlightGoggles virtual reality environment,
exteroceptive sensors are rendered synthetically in real time while all complex
extrinsic dynamics are generated organically through the natural interactions
of the vehicle. The FlightGoggles framework allows for researchers to
accelerate development by circumventing the need to estimate complex and
hard-to-model interactions such as aerodynamics, motor mechanics, battery
electrochemistry, and behavior of other agents. The ability to perform
vehicle-in-the-loop experiments with photorealistic exteroceptive sensor
simulation facilitates novel research directions involving, e.g., fast and
agile autonomous flight in obstacle-rich environments, safe human interaction,
and flexible sensor selection. FlightGoggles has been utilized as the main test
for selecting nine teams that will advance in the AlphaPilot autonomous drone
racing challenge. We survey approaches and results from the top AlphaPilot
teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be
found at https://flightgoggles.mit.edu. Revision includes description of new
FlightGoggles features, such as a photogrammetric model of the MIT Stata
Center, new rendering settings, and a Python AP
Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios
Event cameras are bio-inspired vision sensors that output pixel-level
brightness changes instead of standard intensity frames. These cameras do not
suffer from motion blur and have a very high dynamic range, which enables them
to provide reliable visual information during high speed motions or in scenes
characterized by high dynamic range. However, event cameras output only little
information when the amount of motion is limited, such as in the case of almost
still motion. Conversely, standard cameras provide instant and rich information
about the environment most of the time (in low-speed and good lighting
scenarios), but they fail severely in case of fast motions, or difficult
lighting such as high dynamic range or low light scenes. In this paper, we
present the first state estimation pipeline that leverages the complementary
advantages of these two sensors by fusing in a tightly-coupled manner events,
standard frames, and inertial measurements. We show on the publicly available
Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement
of 130% over event-only pipelines, and 85% over standard-frames-only
visual-inertial systems, while still being computationally tractable.
Furthermore, we use our pipeline to demonstrate - to the best of our knowledge
- the first autonomous quadrotor flight using an event camera for state
estimation, unlocking flight scenarios that were not reachable with traditional
visual-inertial odometry, such as low-light environments and high-dynamic range
scenes.Comment: 8 pages, 9 figures, 2 table
Application of Simultaneous Localization and Mapping Algorithms for Haptic Teleoperation of Aerial Vehicles
In this thesis, a new type of haptic teleoperator system for remote control of Unmanned Aerial Vehicles (UAVs) has been developed, where the Simultaneous Localization and Mapping (SLAM) algorithms are implemented for the purpose of generating the haptic feedback. Specifically, the haptic feedback is provided to the human operator through interaction with artificial potential field built around the obstacles in the virtual environment which is located at the master site of the teleoperator system. The obstacles in the virtual environment replicate essential features of the actual remote environment where the UAV executes its tasks. The state of the virtual environment is generated and updated in real time using Extended Kalman Filter SLAM algorithms based on measurements performed by the UAV in the actual remote environment. Two methods for building haptic feedback from SLAM algorithms have been developed. The basic SLAM-based haptic feedback algorithm uses fixed size potential field around the obstacles, while the robust SLAM-based haptic feedback algorithm changes the size of potential field around the obstacle depending on the amount of uncertainty in obstacle location, which is represented by the covariance estimate provided by EKF. Simulations and experimental results are presented that evaluate the performance of the proposed teleoperator system
Perception-aware time optimal path parameterization for quadrotors
The increasing popularity of quadrotors has given rise to a class of
predominantly vision-driven vehicles. This paper addresses the problem of
perception-aware time optimal path parametrization for quadrotors. Although
many different choices of perceptual modalities are available, the low weight
and power budgets of quadrotor systems makes a camera ideal for on-board
navigation and estimation algorithms. However, this does come with a set of
challenges. The limited field of view of the camera can restrict the visibility
of salient regions in the environment, which dictates the necessity to consider
perception and planning jointly. The main contribution of this paper is an
efficient time optimal path parametrization algorithm for quadrotors with
limited field of view constraints. We show in a simulation study that a
state-of-the-art controller can track planned trajectories, and we validate the
proposed algorithm on a quadrotor platform in experiments.Comment: Accepted to appear at ICRA 202
Mixed marker-based/marker-less visual odometry system for mobile robots
When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test
Fire detection of Unmanned Aerial Vehicle in a Mixed Reality-based System
This paper proposes the employment of a low-cost Micro-electro-mechanical system including; inertial measurement unit (IMU), a consumer-grade digital camera and a fire detection algorithm with a nano unmanned aerial vehicle for inspection application. The video stream (monocular camera) and navigation data (IMU) rely on state-of-the-art indoor/outdoor navigation system. The system combines robotic operating system and computer vision techniques to render metric scale of monocular vision and gravity observable to provide robust, accurate and novel inter-frame motion estimates. The collected onboard data are communicated to the ground station and processed using a Simultaneous Localisation and Mapping (SLAM) system. A robust and efficient re-localisation SLAM was performed to recover from tracking failure, motion blur and frame lost in the received data. The fire detection algorithm was deployed based on the colour, movement attributes, temporal variation of fire's intensity and its accumulation around a point. A cumulative time derivative matrix was used to detect areas with fire's high-frequency luminance flicker (random characteristic) to analyse the frame-by-frame changes. We considered colour, surface coarseness, boundary roughness and skewness features while the quadrotor flies autonomously within clutter and congested areas. Mixed Reality system was adopted to visualise and test the proposed system in a physical/virtual environment. The results showed that the UAV could successfully detect fire and flame, fly towards and hover around it, communicate with the ground station and generate SLAM system
Unmanned Robotic Systems and Applications
This book presents recent studies of unmanned robotic systems and their applications. With its five chapters, the book brings together important contributions from renowned international researchers. Unmanned autonomous robots are ideal candidates for applications such as rescue missions, especially in areas that are difficult to access. Swarm robotics (multiple robots working together) is another exciting application of the unmanned robotics systems, for example, coordinated search by an interconnected group of moving robots for the purpose of finding a source of hazardous emissions. These robots can behave like individuals working in a group without a centralized control
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