Unmanned Space Vehicle (usv)
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Radar 2014 cover all aspects of radar systems for civil, security and defence application. Waveform design, beamforming, signal processing, Emerging applications and technologies, sub-systems technologies, Radar environment.
DASC is the premier annual conference providing authors an opportunity for publication and presentation to an international audience of papers encompassing the field of avionics systems for aircraft/rotorcraft/unmanned aircraft (commercial, military, general aviation) launch vehicles, missiles, spacecraft, and space transportation systems, navigation, guidance/control of flight, computers, communications, sensors (radar, infrared, visual bands), avionics architectures and data networking, communications networks, software, crew interface, space and ground components needed for the operation of military, commercial, and business aircraft, and avionics electrical power generation and control, Student papers are entered into a judged competition.
This fifth conference in the on-going series will continue to build on the success of the previous four conferences by bringing together researchers from numerous diverse backgrounds and specialties to facilitate the exchange and cross-fertilization of ideas and research. Recent advances in hardware technology are enabling a much wider range of design freedoms to be explored for sensor and communication systems. As a result, there are emerging and compelling changes in system requirements such as more effic
No periodicals are currently tagged "Unmanned Space Vehicle (usv)"
2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (2014), 2014
In this paper, a Training and Support system for Search and Rescue operations is described. The system is a component of the ICARUS project (http://www.fp7-icarus.eu) which has a goal to develop sensor, robotic and communication technologies for Human Search And Rescue teams. The support system for planning and managing complex SAR operations is implemented as a command and control component ...
OCEANS 2017 - Aberdeen, 2017
Many ocean survey missions require an Unmanned Surface Vehicle (USV) to accurately follow predefined paths, and thus, an efficient and robust path following control algorithm is essential for many applications. The Vector Field Method (VF) has been widely employed in the Unmanned Aerial Vehicle (UAV) community, and evaluating this well-accepted method for USVs will be of great interest for USV ...
OCEANS 2018 MTS/IEEE Charleston, 2018
ARAGON is an unmanned surface vehicle (USV) for ocean observation and sea surveillance of Korea Research Institute of Ships and Ocean Engineering (KRISO). It has been constructed through the research and development project, which is entitled with “The development of intelligent unmanned surface vehicle for multipurpose mission of ocean observation and sea surveillance” under the financial support of Korea Ministry ...
2011 International Conference on Computer Science and Service System (CSSS), 2011
Both kinematics and measurement model help process the trajectory data and estimate aerodynamic parameters of vehicle. In this paper, three dynamics are proposed, which are aerodynamics parameter's model, acceleration's model and α - β - γ model. The measurement model is TDRS (Tracking and Data Relay Satellite), which produces a complicated trajectory in reentering purpose. A fuzzy IMM algorithm is ...
Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV'94), 1995
This paper presents a new distributed decentralized architecture for the organization, command, control and communication (C/sup 3/) of multiple agent system which has to execute a mission, cooperatively and autonomously. This architecture can be applied to multiple unmanned underwater vehicles (UUVs), to unmanned air vehicles (UAVs), as well as to unmanned ground vehicles (UGVs), unmanned space vehicles (USVs) or to ...
Cooperative Vehicle-to-Vehicle and Vehicle-to-Infrastructure Communication and Networking Protocols
Quadrotor Trajectory Tracking with L1 Optimal control
A Conversation About Autonomous Transportation Systems: IEEE TechEthics Interview
How Much Autonomy Is Acceptable? - IEEE TechEthics Virtual Panel
Where's my electric car?
CASS Lecture with Dr. Claude Gauthier, "Automotive Ethernet and Functional Safety"
Navigation and Control of Unmanned Vehicles: A Fuzzy Logic Perspective
State-of-the art techniques for advanced vehicle dynamics control & vehicle state estimation
International Electric Vehicle Conference 2012
ITEC 2014: Next Generation Combat Vehicle Electrical Power Architecture Development
New Approach of Vehicle Electrification: Analysis of Performance and Implementation Issue
Defense Department's Crusher Field Demonstration
GHTC 2015 - Impact of the ISS
Self-Driving Buses: Minnesota Pilot Project - IEEE Region 4 Presentation
Transportation Electrification: Connected Vehicle Environment
Lecture by Dr. Ratnesh Kumar "Vehicle Re-identification for Smart Cities: A New Baseline Using Triplet Embedding"
Future of Space Exploration from the Leaders at Mars One, Astrobotic, and Teledyne Brown Engineering: Innovation Spotlight with Grant Imahara
APEC Speaker Highlights: JB Straubel, CTO, Tesla Motors, Inc.
Vehicle Electrification Technology Policy
In this paper, a Training and Support system for Search and Rescue operations is described. The system is a component of the ICARUS project (http://www.fp7-icarus.eu) which has a goal to develop sensor, robotic and communication technologies for Human Search And Rescue teams. The support system for planning and managing complex SAR operations is implemented as a command and control component that integrates different sources of spatial information, such as maps of the affected area, satellite images and sensor data coming from the unmanned robots, in order to provide a situation snapshot to the rescue team who will make the necessary decisions. Support issues will include planning of frequency resources needed for given areas, prediction of coverage conditions, location of fixed communication relays, etc. The training system is developed for the ICARUS operators controlling UGVs (Unmanned Ground Vehicles), UAVs (Unmanned Aerial Vehicles) and USVs (Unmanned Surface Vehicles) from a unified Remote Control Station (RC2). The Training and Support system is implemented in SaaS model (Software as a Service). Therefore, its functionality is available over the Ethernet. SAR ICARUS teams from different countries can be trained simultaneously on a shared virtual stage. In this paper we will show the multi-robot 3D mapping component (aerial vehicle and ground vehicles). We will demonstrate that these 3D maps can be used for Training purpose. Finally we demonstrate current approach for ICARUS Urban SAR (USAR) and Marine SAR (MSAR) operation training.
Many ocean survey missions require an Unmanned Surface Vehicle (USV) to accurately follow predefined paths, and thus, an efficient and robust path following control algorithm is essential for many applications. The Vector Field Method (VF) has been widely employed in the Unmanned Aerial Vehicle (UAV) community, and evaluating this well-accepted method for USVs will be of great interest for USV practitioners. In this paper, we will adapt and apply this algorithm on the USV path following problem. We provide a comprehensive study of the VF algorithm for tracking straight and circular paths, which includes searching the parameter space, doing simulation tests and carrying out field trials. Finally, a mock ocean survey task has been planned and the successful results prove the robustness and accuracy of the introduced VF algorithm.
ARAGON is an unmanned surface vehicle (USV) for ocean observation and sea surveillance of Korea Research Institute of Ships and Ocean Engineering (KRISO). It has been constructed through the research and development project, which is entitled with “The development of intelligent unmanned surface vehicle for multipurpose mission of ocean observation and sea surveillance” under the financial support of Korea Ministry of Oceans and Fisheries since 2011. Now, it is the final eighth fiscal year of the project. The length of ARAGON is about 7.5 meter and its maximum speed is over 40 knots. ARAGON has 400-HP diesel engine with single water-jet. In order to make USV navigate safely according to the convention on the international regulations for preventing collisions at sea, 1972 (COLREGs) without human operation, autonomous navigation system is needed. A collision avoidance system is developed by using changeable action space searching. Action space can be flexibly changed according to the collision risk, which is estimated by using obstacle information on a basis of fuzzy inference. Navigational information of USV such as position, speed, course and attitude are acquired by using real-time kinematic (RTK) GPS and Integrated Navigation System (INS). Obstacles can be detected and tracked by using multi-sensors fusion of automatic identification system (AIS), Pulse radar, light detection and ranging (Lidar) and EO/IR (Electro Optical/Infra-Red) camera. Optimal route for collision avoidance is estimated according to the cost functions related to collision risk in real-time. Autopilot and speed controller is actuated for the following to the optimal route. In order to evaluate the performance of autonomous navigation of ARAGON, field tests are carried out in actual sea area, Busan on the complicated colliding situations such as head-on, crossing and overtaking with multiple obstacles. Three physical powerboats are used as moving obstacles in the colliding situation of head-on, port-crossing and starboard-crossing. Two virtual boats are used as moving obstacles in the colliding situation of the 2nd head-on and overtaking. In this paper, the main features of ARAGON and main results of field test are described.
Both kinematics and measurement model help process the trajectory data and estimate aerodynamic parameters of vehicle. In this paper, three dynamics are proposed, which are aerodynamics parameter's model, acceleration's model and α - β - γ model. The measurement model is TDRS (Tracking and Data Relay Satellite), which produces a complicated trajectory in reentering purpose. A fuzzy IMM algorithm is proposed in this paper, which through a fuzzy inference to get the matching degree of every UKF filtering model, instead of calculating model transition probabilities in IMM. With the matched degrees, the estimation from each filtering is weighted to get maneuvering target overall estimation and its covariance. Simulation results show that the FIMM algorithm performs well in tracking re-entering vehicle and estimating the aerodynamics parameters as well.
This paper presents a new distributed decentralized architecture for the organization, command, control and communication (C/sup 3/) of multiple agent system which has to execute a mission, cooperatively and autonomously. This architecture can be applied to multiple unmanned underwater vehicles (UUVs), to unmanned air vehicles (UAVs), as well as to unmanned ground vehicles (UGVs), unmanned space vehicles (USVs) or to any other types of autonomous cooperative multiple agent systems. The rationale for autonomous cooperative operation of a multiple agent system, stems from the need to execute critical missions under time, space resources and availability constraints, which are beyond the capability of a single agent to perform successfully. If the system agents are distributed geographically, cooperative operation is a reasonable approach. It supports share of information, share of resources, efficient resource allocation, context and situation driven responsiveness, robustness and flexibility under changing conditions, as well as redundancy. A key feature of the proposed architecture is that all the agents are identical in their sensing, information processing, decision making, communication and mission related capabilities. Thus, a failed agent can be functionally replaced by a peer agent. The architecture is both distributed and decentralized. It is implemented via four major building blocks, which are embedded within each agent.
The quasi-static catenary curve of a semi-slack tether between an essentially stationary unmanned air vehicle (UAV) and a small unmanned surface vehicle (USV) is investigated and characterized. An empirical analysis, performed over a discretized space of vertical and horizontal separations of the two vehicles, determines an optimum cable length & tension for maximizing system robustness during the vertical heave of the USV due to high seas. Operating at this optimum condition allows for equal displacements of the USV in the up and down directions, minimizing the possibility of both fouling (with the tether touching the water) and excessive downforce on the UAV (with the tether pulled taut) during dynamic heave events. Scaling the horizontal offset, tether length, and tension by the flying height collapses all empirical results into convenient curves depending only on a nondimensional relative position parameter (Δx/Δy), accurately fit by low order polynomials. This eliminates the need for a lookup table, and decreases computation time during implementation. The heave robustness analysis results in a recommended operating relative position of Δx/Δy ≈ .46. Experimental results are presented and confirm the catenary analysis for the proposed tether.
In his best-selling book, War Made New, military historian Max Boot supports his thesis with historical examples to show how technological-driven “Revolutions in Military Affairs” have transformed warfare and altered the course of history. The U.S. military has embraced a wave of technological change that has constituted a true transformation of the way that military forces will fight in the 21st Century. One of the most transformational technologies adapted for military use is unmanned and autonomous systems. The expanding use of these systems for civilian and military applications has increased dramatically over the past decade. This should come as no surprise, as these systems represent one of the most rapidly growing areas of innovative technology adoption. In the military trade space, the use of military unmanned systems (UxS) is already creating strategic, operational, and tactical possibilities that did not exist a decade ago. These systems are not only changing the face of modern warfare, but are also altering the process of decision-making in combat operations. Indeed, it has been argued that the rise in drone warfare is changing the way we conceive of and define “warfare” itself. However, while these unmanned systems are of enormous value today and are evolving to deliver better capabilities to the warfighter, it is their promise for the future that causes the most excitement. Indeed, these systems have created substantial buzz in policy, military, industry and academic circles. One of the most cutting-edge and challenging aspects of autonomous systems is enabling systems that operate in different domains - Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vehicles (USVs), and Unmanned Underwater Vehicles (UUVs) - work together as a heterogeneous whole. As autonomous systems become more important to military operators, and especially as they are used for more diverse and complex missions, the issue of command and control (C2) of cross-domain unmanned vehicles (UxVs) will become more important. Today, while the performance of UxV in all domains has improved dramatically, the C2 issues of controlling UxVs in multiple domains simultaneously remains an area requiring additional research, modeling and simulation, and operational testing. This paper will present the results of operational testing of cross-domain UxVs in The Technical Cooperation Program (five-eyes) Hell Bay 4 experiment during international exercise Unmanned Warrior 2016 at the British Underwater Test & Evaluation Centre (BUTEC), in the Kyle of Lochalsh, Scotland, United Kingdom. The primary objectives of this experiment focused on cooperative teaming of a UAV with UUVs to demonstrate extended range C2 of remotely deployed UUVs in a contested littoral environment. One of the most promising results of this experiment was the ability of the test UAV to travel for one hour at twenty-five miles per hour while carrying a ten-pound payload. This UAV (a United States Vapor 55) was thus able to transit from one ship to the other, autonomously take off and land, and recharge when necessary. This capability demonstrates the potential for these UAVs to operate from ships in international waters (outside a nation's twelve mile territorial sea) and have enough endurance to conduct missions ashore. The undersea portion of the experiment was equally promising, with the operational team able to effectively control Iver2 and Iver3 UUVs - as well as the UAV - via the ONR CaSHMI control station. Of note, we were able to successfully relay data between UUVs and the UAV at several miles separation. Finally, this paper will present the details of proposed future experimentation and provide on-ramps for industry, academia, military and allied partners to participate in future events. The presenting author will provide appropriate points of contact at the various organizations that participated in The Technical Cooperation Program Hell Bay 4 experiment during international exercise Unmanned Warrior 2016, and will suggest ways of other parties can join future experiments.
In this paper, a fusion CKF algorithm with TDRS (Tracking and Data Relay Satellite) and ground stations is presented for a micro reentering USV (Unmanned Space Vehicle). A micro reentering USV has high lift-drag ratio and maneuverability, unlike traditional reentry vehicle, is hard to track. In order to solve this problem, a fusion strategy of CKF (Cubature Kalman Filter) with multi-sensors is estimated. In the kinematics model, the aerodynamics parameters of reentering vehicle are designed. In the measurement models, although TDRS has more coverage capability than ground stations, a little angle error could cause a big error for the measurement data. Both TDRS and ground stations are presented. Meanwhile, in the filter algorithm, a new kind of filter named CKF is presented, which using spherical-radial cubature rule to numerically compute multivariate moment integrals encountered in the nonlinear filter. Also the fusion roles are used helping calculate the probabilities of multi-sensors. Simulation results show that the fusion CKF algorithm performs better than UKF in tracking sub-orbit USV and estimating the aerodynamics parameters.
This paper addresses a three-dimensional (3D) reconstruction of a flooded open pit mine with an autonomous surface vehicle (ASV) and unmanned aerial vehicle (UAV). The ROAZ USV and the Otus UAV were used to provide the underwater bathymetric map and aerial 3D reconstruction based from image data. This work was performed within the context of the European research project VAMOS with the objective of developing robotic tools for efficient underwater mining.
The issues affecting low-level controller design for a wave-adaptive modular class of unmanned surface vehicles (USVs) are presented in the context of supporting the autonomous launch and recovery (ALR) of an Autonomous Underwater Vehicle (AUV) aboard an USV. The ALR of an AUV presents a challenging control problem because control parameters are chosen based on the dynamics of the vehicle, which can change abruptly and significantly when an AUV is jettisoned or docked. Tight control of the vehicle state is required to conduct an underway recovery, with looser requirements for the launch. Knowledge of the vehicle response is important for control system design. A simulation, based on physical modeling and experimental results, was developed to support the design of a low level controller. A cascaded proportional derivative (PD) controller has been implemented in simulation and tested for two dissimilar reference speeds and two payload conditions. The simulation results suggest that an adaptive controller may be necessary to overcome the time-varying nature of USV dynamics during an ALR mission.
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