182 research outputs found
Vision-based guidance and control of a hovering vehicle in unknown environments
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (leaves 115-122).This thesis presents a methodology, architecture, hardware implementation, and results of a system capable of controlling and guiding a hovering vehicle in unknown environments, emphasizing cluttered indoor spaces. Six-axis inertial data and a low-resolution onboard camera yield sufficient information for image processing, Kalman filtering, and novel mapping algorithms to generate a, high-performance estimate of vehicle motion, as well as an accurate three-dimensional map of the environment. This combination of mapping and localization enables a quadrotor vehicle to autonomously navigate cluttered, unknown environments safely. Communication limitations are considered, and a hybrid control architecture is presented to demonstrate the feasibility of combining separated proactive offboard and reactive onboard planners simultaneously, including a detailed presentation of a novel reactive obstacle avoidance algorithm and preliminary results integrating the MIT Darpa Urban Challenge planner for high-level control. The RAVEN testbed is successfully employed as a prototyping facility for rapid development of these algorithms using emulated inertial data and offboard processing as a precursor to embedded development. An analysis of computational demand and a comparison of the emulated inertial system to an embedded sensor package demonstrates the feasibility of porting the onboard algorithms to an embedded autopilot. Finally, flight results using only the single camera and emulated inertial data for closed-loop trajectory following, environment mapping, and obstacle avoidance are presented and discussed.by Spencer Greg Ahrens.S.M
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Real-time spatial modeling to detect and track resources on construction sites
For more than 10 years the U.S. construction industry has experienced over 1,000
fatalities annually. Many fatalities may have been prevented had the individuals and
equipment involved been more aware of and alert to the physical state of the environment
around them. Awareness may be improved by automatic 3D (three-dimensional) sensing
and modeling of the job site environment in real-time. Existing 3D modeling approaches
based on range scanning techniques are capable of modeling static objects only, and thus
cannot model in real-time dynamic objects in an environment comprised of moving
humans, equipment, and materials. Emerging prototype 3D video range cameras offer
another alternative by facilitating affordable, wide field of view, automated static and
dynamic object detection and tracking at frame rates better than 1Hz (real-time).
This dissertation presents an imperical work and methodology to rapidly create a
spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a
static or moving position. Existing construction site 3D modeling approaches based on
optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling
approaches (dense, sparse, etc.) that offered potential solutions for this research are
reviewed. The choice of an emerging sensing tool and preliminary experiments with this
prototype sensing technology are discussed. These findings led to the development of a
range data processing algorithm based on three-dimensional occupancy grids which is
demonstrated in detail. Testing and validation of the proposed algorithms have been
conducted to quantify the performance of sensor and algorithm through extensive
experimentation involving static and moving objects. Experiments in indoor laboratory
and outdoor construction environments have been conducted with construction resources
such as humans, equipment, materials, or structures to verify the accuracy of the
occupancy grid modeling approach. Results show that modeling objects and measuring
their position, dimension, direction, and speed had an accuracy level compatible to the
requirements of active safety features for construction. Results demonstrate that video
rate 3D data acquisition and analysis of construction environments can support effective
detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated
three-dimensional models for improved visualization, communications, and process
control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction
activities control. In combination with effective management practices, this sensing
approach has the potential to assist equipment operators to avoid incidents that result in
reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin
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