Quantum Signal, LLC | Date: 2016-11-23
Quantum Signal, LLC | Date: 2014-02-18
Agency: Department of Defense | Branch: Office of the Secretary of Defense | Program: SBIR | Phase: Phase II | Award Amount: 999.82K | Year: 2015
This topic addresses the problem of robustly commanding and controlling unmanned ground vehicles operating in complex, unstructured environments. Current approaches to this task rely on dense scene reconstruction from a variety of sensor data such as LIDAR and video imagery. Scene representations are then relayed to a remote human operator who provides commands at varying levels of supervisory control (intelligent teleoperation). Challenges arise in scenarios where the available communication link allows only low bandwidth data transmission and/or exhibits high latency. In such scenarios, bandwidth limitations prevent rich scene representations from being transmitted from the vehicle to the operator in a timely manner. In addition, high latency may have a destabilizing effect, causing commands issued by the operator to lead to unsafe actions by the vehicle. This effect is exacerbated as the frequency of command inputs increases. Novel frameworks, and associated algorithms, are required to enable robust operations of autonomous unmanned ground vehicles operating in complex, unstructured environments, over a low-bandwidth, high latency communication link. Such approaches would provide an operator with sufficient information to make timely command and control decisions, even in harsh communication scenarios. They would also provide contingency-based assurance of system safety in the absence of timely command and control decisions. In addition, other functional relationships such as sensor costs need to be addressed since these costs are generally proportional to the level of autonomy or intelligence. Approaches to this problem may emphasize perception, vehicle control, or some combination of the two. In the perception domain, approaches to intelligent data compression and minimal scene representation are desired . Such approaches may condense raw sensor data into compact, human-recognizable primitives that can be efficiently transmitted over low-bandwidth communication links. These methods may be optimized for particular contexts (e.g. urban operations) to enable improved data compression, and they may also dynamically vary scene representation richness or complexity depending on available bandwidth. In the control domain, contingency-based control algorithms that ensure vehicle safety in the absence of operator inputs, or when provided with unsafe command inputs (perhaps due to the effects of latency) are desired. Again, such approaches may be optimized for particular contexts to enable improved performance. Methods that act as vehicle co-pilots, which both ensure vehicle safety and attempt to predict operator intent, would be particularly useful . The output of this work is software that would be integrated with an existing autonomous vehicle(s) to yield measureable improvements in safety and operational speed compared to a baseline system, for the low-bandwidth, high-latency scenarios of interest. If successful, this work will have broad applications for autonomous and semi-autonomous military vehicle operations.
Quantum Signal, LLC | Date: 2014-04-22
A digital image of a first garment having one or more first garment portions is received. A user has identified the first garment portions as matching one or more corresponding second garment portions of a second garment. The probability of accidental match of the first garment within the digital image in relation to the second garment is determined, by using a statistical model based on one or more parameters and based on analyses of the first garment portions. The probability of accidental match is output.
Quantum Signal, LLC | Date: 2013-07-22
Quantum Signal, LLC | Date: 2013-11-29
A video stream of people within a venue like a movie theater is received. The people within the video stream are analyzed. Based on analysis of the people within the video stream, virtual content is overlaid onto the video stream. The video stream, with the virtual content overlaid thereon, is then displayed onto a screen within the venue. As such, the virtual content and one or more of the people within the venue can appear to be interacting with one another as if the virtual content were real and present within the venue.
Agency: Department of Transportation | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.97K | Year: 2015
Recent research suggests there are opportunities to enhance driver hazard anticipation and avoidance, which could in turn dramatically impact public safety. For example, the fatality rate per 100 million vehicle miles among 16 year old drivers is some ten times higher than it is among those between the ages of 45 and 55. However, hazard-related training has traditionally been difficult to accomplish, with classroom exercises lacking impact and experiential components, and real-world driving experiences hard to constrain and keep safe. The purpose of LookOut is to create a real-time, scenario-based desktop/handheld driving simulator that helps drivers with differing backgrounds (e.g., age, experience) gain greater awareness of hazards and safely avoid them. A simulator, an underlying training paradigm for hazard anticipation/detection and avoidance that is implemented in the simulator, and a database of hazards/scenarios that will be used in conjunction with it are being created. The system will leverage video game technology and realistic vehicle models/physics, and run on standard PC computers (and handheld tablets) using very low-cost steering controllers, and, optionally, a low cost eye tracker.
Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase I | Award Amount: 149.78K | Year: 2014
Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase II | Award Amount: 999.71K | Year: 2014
Control of both manned and unmanned (i.e. autonomous or teleoperated) military vehicles is difficult for many reasons. As a result, vehicle accidents (most notably rollover) are one of the leading causes of casualty in Army operations (in manned vehicles) and disabling (in UGVs). Therefore, there exists a significant need to introduce vehicle stability enhancement technology to military vehicles. Quantum Signal has initiated development of a low cost vehicle stability enhancement system, dubbed VSS+, that is applicable to a wide range of legacy vehicles of varying size and type. VSS+ will provide information to the operator (or onboard control systems) on vehicle stability in real time, and will have both reactive and predictive capabilities. It will be intended as an add-on functionality for retrofitting existing vehicles, and use standardized interface architectures and protocols. The primary objective of previous Phase I work by Quantum Signal was to investigate the feasibility of the VSS+ system and to demonstrate proof of concept results through extensive simulation. In Phase II, the primary objective is to develop a fully-functional prototype of the VSS+ system, and demonstrate its performance on a manned or unmanned vehicle operating in Army-relevant conditions.
Agency: Department of Defense | Branch: Army | Program: STTR | Phase: Phase I | Award Amount: 150.00K | Year: 2014
Autonomous or teleoperated navigation of unmanned ground vehicles (UGVs) is difficult even in benign environments due to challenges associated with perception, decision making, and human-machine interaction, among others. In environments with rough, sloped, slippery, and/or deformable terrain, the difficulty of the navigation problem increases dramatically. In this effort, Quantum Signal, LLC, University of Michigan, and Massachusetts Institute of Technology propose to collaboratively research methods for robust terrain-adaptive planning and control to enable a future generation of UGVs with assured mobility in highly challenging terrain. The approach will exploit physics-based terrain modeling with data-driven variance estimation, stochastic vehicle motion planning through feasible corridor, and terrain-adaptive predictive vehicle control integrated into a threat-based control arbitration architecture. This architecture will enable operation at (and seamless transition between) any point on the autonomy spectrum, ranging from manual teleoperation to full autonomy. In Phase 1 the team will develop, test, and characterize algorithm performance with Quantum Signal"s high fidelity ANVEL robotic vehicle simulator and determine feasibility. Should the methods prove feasible, Phase 2 will involve the further development, integration, and testing of the methodology on experimental vehicle hardware.