33 research outputs found
MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems
This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV)
platform called the Multi-robot Systems (MRS) Drone that can be used in a large
range of indoor and outdoor applications. The MRS Drone features unique
modularity with respect to changes in actuators, frames, and sensory
configuration. As the name suggests, the platform is specially tailored for
deployment within a MRS group. The MRS Drone contributes to the
state-of-the-art of UAV platforms by allowing smooth real-world deployment of
multiple aerial robots, as well as by outperforming other platforms with its
modularity. For real-world multi-robot deployment in various applications, the
platform is easy to both assemble and modify. Moreover, it is accompanied by a
realistic simulator to enable safe pre-flight testing and a smooth transition
to complex real-world experiments. In this manuscript, we present mechanical
and electrical designs, software architecture, and technical specifications to
build a fully autonomous multi UAV system. Finally, we demonstrate the full
capabilities and the unique modularity of the MRS Drone in various real-world
applications that required a diverse range of platform configurations.Comment: 49 pages, 39 figures, accepted for publication to the Journal of
Intelligent & Robotic System
Vision-Based Control of Unmanned Aerial Vehicles for Automated Structural Monitoring and Geo-Structural Analysis of Civil Infrastructure Systems
The emergence of wireless sensors capable of sensing, embedded computing, and wireless communication has provided an affordable means of monitoring large-scale civil infrastructure systems with ease. To date, the majority of the existing monitoring systems, including those based on wireless sensors, are stationary with measurement nodes installed without an intention for relocation later. Many monitoring applications involving structural and geotechnical systems require a high density of sensors to provide sufficient spatial resolution to their assessment of system performance. While wireless sensors have made high density monitoring systems possible, an alternative approach would be to empower the mobility of the sensors themselves to transform wireless sensor networks (WSNs) into mobile sensor networks (MSNs). In doing so, many benefits would be derived including reducing the total number of sensors needed while introducing the ability to learn from the data obtained to improve the location of sensors installed. One approach to achieving MSNs is to integrate the use of unmanned aerial vehicles (UAVs) into the monitoring application. UAV-based MSNs have the potential to transform current monitoring practices by improving the speed and quality of data collected while reducing overall system costs. The efforts of this study have been chiefly focused upon using autonomous UAVs to deploy, operate, and reconfigure MSNs in a fully autonomous manner for field monitoring of civil infrastructure systems.
This study aims to overcome two main challenges pertaining to UAV-enabled wireless monitoring: the need for high-precision localization methods for outdoor UAV navigation and facilitating modes of direct interaction between UAVs and their built or natural environments. A vision-aided UAV positioning algorithm is first introduced to augment traditional inertial sensing techniques to enhance the ability of UAVs to accurately localize themselves in a civil infrastructure system for placement of wireless sensors. Multi-resolution fiducial markers indicating sensor placement locations are applied to the surface of a structure, serving as navigation guides and precision landing targets for a UAV carrying a wireless sensor. Visual-inertial fusion is implemented via a discrete-time Kalman filter to further increase the robustness of the relative position estimation algorithm resulting in localization accuracies of 10 cm or smaller. The precision landing of UAVs that allows the MSN topology change is validated on a simple beam with the UAV-based MSN collecting ambient response data for extraction of global mode shapes of the structure. The work also explores the integration of a magnetic gripper with a UAV to drop defined weights from an elevation to provide a high energy seismic source for MSNs engaged in seismic monitoring applications. Leveraging tailored visual detection and precise position control techniques for UAVs, the work illustrates the ability of UAVs to—in a repeated and autonomous fashion—deploy wireless geophones and to introduce an impulsive seismic source for in situ shear wave velocity profiling using the spectral analysis of surface waves (SASW) method. The dispersion curve of the shear wave profile of the geotechnical system is shown nearly equal between the autonomous UAV-based MSN architecture and that taken by a traditional wired and manually operated SASW data collection system. The developments and proof-of-concept systems advanced in this study will extend the body of knowledge of robot-deployed MSN with the hope of extending the capabilities of monitoring systems while eradicating the need for human interventions in their design and use.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169980/1/zhh_1.pd
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space
In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based
on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way
Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space
In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based
on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way
Factories of the Future
Engineering; Industrial engineering; Production engineerin
Disruptive Technologies with Applications in Airline & Marine and Defense Industries
Disruptive Technologies With Applications in Airline, Marine, Defense Industries is our fifth textbook in a series covering the world of Unmanned Vehicle Systems Applications & Operations On Air, Sea, and Land. The authors have expanded their purview beyond UAS / CUAS / UUV systems that we have written extensively about in our previous four textbooks. Our new title shows our concern for the emergence of Disruptive Technologies and how they apply to the Airline, Marine and Defense industries. Emerging technologies are technologies whose development, practical applications, or both are still largely unrealized, such that they are figuratively emerging into prominence from a background of nonexistence or obscurity. A Disruptive technology is one that displaces an established technology and shakes up the industry or a ground-breaking product that creates a completely new industry.That is what our book is about. The authors think we have found technology trends that will replace the status quo or disrupt the conventional technology paradigms.The authors have collaborated to write some explosive chapters in Book 5:Advances in Automation & Human Machine Interface; Social Media as a Battleground in Information Warfare (IW); Robust cyber-security alterative / replacement for the popular Blockchain Algorithm and a clean solution for Ransomware; Advanced sensor technologies that are used by UUVs for munitions characterization, assessment, and classification and counter hostile use of UUVs against U.S. capital assets in the South China Seas. Challenged the status quo and debunked the climate change fraud with verifiable facts; Explodes our minds with nightmare technologies that if they come to fruition may do more harm than good; Propulsion and Fuels: Disruptive Technologies for Submersible Craft Including UUVs; Challenge the ammunition industry by grassroots use of recycled metals; Changing landscape of UAS regulations and drone privacy; and finally, Detailing Bioterrorism Risks, Biodefense, Biological Threat Agents, and the need for advanced sensors to detect these attacks.https://newprairiepress.org/ebooks/1038/thumbnail.jp
Proceedings of the Scientific-Practical Conference "Research and Development - 2016"
talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog
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Radiological and Nuclear Threat Detection Using Small Unmanned Aerial Systems
The aim of this research is to demonstrate the feasibility of remotely sensing nuclear and radiological threat materials by leveraging recent advances in radiation detectors, unmanned systems, and contextual sensors. The broad intent is to get detectors out of the hands of humans and onto semi-autonomous systems for a wide range of use cases. The search for special nuclear material is one specific mission area where radiation detectors employed on small unmanned aerial systems could provide significant operational value by exploiting the advantages that remote access enables: improved collection time, decreased source-to-detector distance, and reduced unintentional shielding. The goals of this study are fivefold: (1) assess current capabilities for directed search and substantiate the improvement that an unmanned approach would provide, (2) expand the understanding of the background radiation environment to include building rooftops, (3) establish system requirements and map out the parameter space of trade-offs (i.e., trade space) based on an analysis of current sensor and platform capabilities, (4) investigate and optimize search methods, and (5) identify and characterize additional mission areas for further investigation.To achieve these five goals, we started by identifying boundary conditions for signal collection time, source-to-detector distance (i.e., standoff), and intervening material attenuation for three different search modes: vehicle-mounted standoff detection, rotary-wing aerial detection, and small unmanned aerial system-based remote detection. The objective of this analysis was to calculate the theoretical reduction in detector area required to achieve the same minimum detectable activity of a Cs-137 source for a given detector material. We found that measuring from the rooftop with just 50 cm2 of detector area should detect smaller activity sources than 10,000 cm2 in a vehicle-borne approach or 5,000 cm2 in an aerial helicopter-borne approach.Our next objective was to characterize the background radiation environment sensed from the rooftops of light industrial buildings. We conducted a measurement campaign across fifteen buildings varying in geographic location, size, shape, height, wall construction, and roofing material. We discovered the variation in the background radiation ranged up to ±50% when analyzing contributions from seven prominent background peaks. Across a single building, this variation ranged 25–40% for contributions from potassium, uranium, and thorium. We also examined the attenuation of radiation by roofing materials both in simulation and experiment. We found that typical roof construction attenuates 1461 keV gamma-rays by approximately 50% when passing normal to the roof and continues to increase as the incident angle between the source and the detector increases. This observation directly influenced our approach to developing an optimal search scheme.With knowledge of the background and consideration of threat signatures, we then initiated an effort to develop a system architecture and design a sensor suite capable of detecting relevant threats in the anticipated environment. We employed established requirements analysis techniques to frame the development of a system that will provide tangible operational value to the user. We examined the trade space for platforms and sensors in terms of size, weight, power, cost, and visibility profile. Although our survey of capabilities is a snapshot in time, it lays the foundation for future analysis of alternatives. We recommend a platform that can move both through the air and on the ground and suggest further exploration of tube-launched systems for several military mission areas employing radiation sensors. For detectors, we recommend room temperature semi-conductors: cadmium zinc telluride for gamma-ray spectroscopy and lithium-backfilled etched-silicon diodes for neutron detection. Technologies such as real-time kinematic positioning, solid-state light depth and ranging, and thermal infrared cameras warrant further study as auxiliary contextual sensors in the system.Assuming an overmatched system is attainable, we then constructed a method to select advantageous measurement locations and developed techniques to optimize a search pattern. We devised a nonlinear programming routine and applied threshold cuts to reduce the time to converge to a near-optimal solution. We also explored several parameters that might be used as the objective quantity depending on the mission requirements and intelligence assessment.Finally, with the intent of removing humans from the task of operating detectors in elevated radiation areas, we sought to expand our inquiry to seven additional military mission areas. We briefly examined a historical vignette where unmanned radiation detection assets would have provided considerable value, summarized the general operational conditions, assessed the impact that remote detection might have on the speed, accuracy, fidelity, safety, or feasibility of a given mission, and identified unique challenges that might arise in developing a materiel solution. These additional areas are ripe for exploration and contribution from the broader community of researchers