BRN - NASA

[Fact Finder]

You will note the date of this article reporting on a talk given by
Piyush Mehrotra, division chief of NASA’s Advanced Supercomputing (NAS) Division at its Ames Research Center is 14.4.22.

I have extracted the introduction then jumped to what he has to say about MLAi at NASA ramping up about 18 months ago. Now NASA as an EAP was announced 23.12.20 and the NASA Vorago SBIR Phase 1 to harden AKD1000 finished end March, 2021 (I think 21st) at which time they prepared a further application to proceed to Phase 2/3 and it was sent according to an email response I think Jesse Chapman received from Vorago, to NASA in May, 2021.

So some might think this fits perfectly with NASA interest and activity ramping up about 18 months ago:
My opinion only DYOR
FF

AKIDA BALLISTA

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NASA Spotlights Its Galaxy of HPC Activities​

By Oliver Peckham
April 15, 2022
“HPC Matters!” was the big, bold title of a talk by Piyush Mehrotra, division chief of NASA’s Advanced Supercomputing (NAS) Division at its Ames Research Center, during the meeting of the HPC Advisory Council at Stanford last week. At the meeting, Mehrotra offered a glimpse into the state of supercomputing at NASA—and how its systems are being applied.
“The NASA Ames supercomputing facility that is located just down the road from you guys there at Stanford is NASA’s premiere supercomputer center,” Mehrotra said. “There’s another one in Colorado which is slightly smaller in size, focused more on the earth sciences.”
“Our charter is to support all of NASA’s missions,” he continued. “At any point, we have about 1,500, 1,600 users, with about 600 projects—science and engineering projects which are spread across all the four mission directives that NASA supports on the science side……………

As opposed to simulation activities, AI and machine learning at NASA remain fairly nascent. “NASA is in some sense late to the game [on AI and ML],” Mehrotra said. “But in the last 18 months, there’s been an explosion of projects using machine learning [and] deep learning[.]” These projects, he said, span feature detection projects (like identifying exoplanets and trees in imagery), prediction projects (like space weather prediction), anomaly detection (like aviation safety and systems behavior) and more.

 

[Fact Finder]

Newer investors will know that NASA approached Brainchip and paid to enter the Brainchip Early Access Program on 23.12.20.

NASA approaching Brainchip and paying to play with AKIDA was and is very significant but more so when you realise it was following NASA awarding an SBIR to Vorago to design a hardened AKD1000 but even more so when considered against the following SBIR which NASA had issued at the time it approaches Brainchip:

Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition​

Agency:
National Aeronautics and Space Administration
Branch:
N/A
Program | Phase | Year:
SBIR | Phase I | 2021
Solicitation:
SBIR_21_P1
Topic Number:
H6.22
NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date. For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.
The official link for this solicitation is:https://sbir.gsfc.nasa.gov/solicitations
Release Date:
November 09, 2020
Open Date:
November 09, 2020
Application Due Date:
January 08, 2021
Close Date:
January 08, 2021
Description:
Lead Center: GRC
Participating Centers: ARC
Scope Title:

Neuromorphic Capabilities​

Scope Description:
This subtopic specifically focuses on advances in signal and data processing. Neuromorphic processing will enable NASA to meet growing demands for applying artificial intelligence and machine learning algorithms onboard a spacecraft to optimize and automate operations. This includes enabling cognitive systems to improve mission communication and data-processing capabilities, enhance computing performance, and reduce memory requirements. Neuromorphic processors can enable a spacecraft to sense, adapt, act, and learn from its experiences and from the unknown environment without necessitating involvement from a mission operations team. Additionally, this processing architecture shows promise for addressing the power requirements that traditional computing architectures now struggle to meet in space applications.

The goal of this program is to develop neuromorphic processing software, hardware, algorithms, architectures, simulators, and techniques as enabling capability for autonomous space operations. Emerging memristor and other radiation-tolerant devices, which show potential for addressing the need for energy-efficient neuromorphic processors and improved signal processing capability, are of particular interest due to their resistance to the effects of radiation.

Additional areas of interest for research and/or technology development include: (a) spiking algorithms that learn from the environment and improve operations, (b) neuromorphic processing approaches to enhance data processing, computing performance, and memory conservation, and (c) new brain-inspired chips and breakthroughs in machine understanding/intelligence. Novel memristor approaches that show promise for space applications are also sought.

This subtopic seeks innovations focusing on low-size, -weight, and -power (-SWaP) applications suitable to lunar orbital or surface operations, thus enabling efficient onboard processing at lunar distances. Focusing on SWaP-constrained platforms opens up the potential for applying neuromorphic processors in spacecraft or robotic control situations traditionally reserved for power-hungry general-purpose processors. This technology will allow for increased speed, energy efficiency, and higher performance for computing in unknown and uncharacterized space environments including the Moon and Mars. Proposed innovations should justify their SWaP advantages and target metrics over the comparable relevant state of the art”

If you have read the above you will have noted that NASA is exploring the use of neuromorphic computing for every aspect of space flight/exploration.

As Rob Telson said Brainchip is working on vision with NASA and other things that they cannot discuss.

So take your pick from the above SBIR Mercedes Benz has confirmed the AKIDA advantage.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[Fact Finder]

You will note the date of this article reporting on a talk given by
Piyush Mehrotra, division chief of NASA’s Advanced Supercomputing (NAS) Division at its Ames Research Center is 14.4.22.

I have extracted the introduction then jumped to what he has to say about MLAi at NASA ramping up about 18 months ago. Now NASA as an EAP was announced 23.12.20 and the NASA Vorago SBIR Phase 1 to harden AKD1000 finished end March, 2021 (I think 21st) at which time they prepared a further application to proceed to Phase 2/3 and it was sent according to an email response I think Jesse Chapman received from Vorago, to NASA in May, 2021.

So some might think this fits perfectly with NASA interest and activity ramping up about 18 months ago:
My opinion only DYOR
FF

AKIDA BALLISTA

• HPCWIRE JAPAN
• 
  
• HPC & AI WALL STREET

Toggle navigation
Search the siteGo

NASA Spotlights Its Galaxy of HPC Activities​

By Oliver Peckham
April 15, 2022
“HPC Matters!” was the big, bold title of a talk by Piyush Mehrotra, division chief of NASA’s Advanced Supercomputing (NAS) Division at its Ames Research Center, during the meeting of the HPC Advisory Council at Stanford last week. At the meeting, Mehrotra offered a glimpse into the state of supercomputing at NASA—and how its systems are being applied.
“The NASA Ames supercomputing facility that is located just down the road from you guys there at Stanford is NASA’s premiere supercomputer center,” Mehrotra said. “There’s another one in Colorado which is slightly smaller in size, focused more on the earth sciences.”
“Our charter is to support all of NASA’s missions,” he continued. “At any point, we have about 1,500, 1,600 users, with about 600 projects—science and engineering projects which are spread across all the four mission directives that NASA supports on the science side……………

As opposed to simulation activities, AI and machine learning at NASA remain fairly nascent. “NASA is in some sense late to the game [on AI and ML],” Mehrotra said. “But in the last 18 months, there’s been an explosion of projects using machine learning [and] deep learning[.]” These projects, he said, span feature detection projects (like identifying exoplanets and trees in imagery), prediction projects (like space weather prediction), anomaly detection (like aviation safety and systems behavior) and more.

Anyone else wondering if there are some AKIDA cards plugged into NASA’s super computer by chance.

I mean if they could be accidentally plugged into Huawei’s ports in the Super computer in Europe why not.

Brainchip really need to put a warning on the box. Take care may accidentally plug into super computers.

My opinion only DYOR
FF


AKIDA BALLISTA

 

[TechGirl]

Anyone else wondering if there are some AKIDA cards plugged into NASA’s super computer by chance.

I mean if they could be accidentally plugged into Huawei’s ports in the Super computer in Europe why not.

Brainchip really need to put a warning on the box. Take care may accidentally plug into super computers.

My opinion only DYOR
FF


AKIDA BALLISTA

 

[Deleted member 118]

Research and Testing of Artificial Intelligence (AI) at Defense Logistics Agency (DLA) Distribution Center Warehouses | SBIR.gov

www.sbir.gov

Description:

RT&L FOCUS AREA(S): Artificial Intelligence/ Machine Learning

TECHNOLOGY AREA(S): Information Systems

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country (ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop an innovative Artificial Intelligence (AI) solution with a state-of-the-art capability that operates within the DLA Distribution Warehouse environment. The warehouse AI system may use various sensors (e.g., Internet of Things (IoT)) where applicable. It should minimize the need for infrastructure modifications to enable an artificial intelligence system within the warehouse environment. The goal of this objective is for the vendor to develop a capability for a warehouse AI system that addresses the requirements for integration with a Warehouse Management System (WMS) and a Warehouse Execution System (WES) as specific warehouse infrastructures dictate. This capability will provide for the seamless execution of AI and interactions with Smart Warehouse systems such as 5G Networks, IoT Sensors, Blockchain technology, Quantum Computers, and Machine Learning (ML).

The state-of-the-art AI solution must integrate into the existing warehouse communications systems to communicate with WES systems when installed. This integration allows Autonomous Guided Vehicles, Autonomous Mobile Robots (AMRs), Robotic Arms, IoT Sensors to receive tasking in an automated fashion to operate frequently and report success or failure at tasking. In support of routine warehouse operations, this research seeks to identify and test AI technology that can be used uninterruptedly and continuously within the DLA Distribution Warehouse environment. This research effort addresses DLA identified cybersecurity requirements through the test and evaluation of government security controls. It leverages current technologies in the AI industry. This research project will operate in locations at designated DLA Distribution Centers in the United States.

DESCRIPTION: Defense Logistics Agency (DLA) Distribution Modernization Program (DMP) topics of interest are research focused on a Continental United States-based Artificial Intelligence (AI) solution in support of the routine warehouse operations. This research project will involve the use of Commercial/Industry AI technology that can meet the demands of warehouse operations, can be integrated with autonomous warehouse vehicles, robots, and warehouse communications, and be integrated with warehouse navigation systems, 5G Networks, IoT Sensors, Quantum Computing architecture, and warehouse based Machine Learning (ML) that:

1. Support a joint effort between DLA Research and Development (R&D) and DLA J4 Distribution Headquarters to conduct research and test a warehouse AI system that works with various autonomous platforms, 5G Networks, IoT Sensors, Quantum Computing systems, and ML applications during warehouse operations.

2. Significantly addresses the AI capabilities of AI within a distribution warehouse operations environment.

3. Features an AI system able to implement high precision data for regular use in warehouse operations.

4. Can be integrated into warehouse communications systems such as a WMS or a WES to receive tasking and report status.

5. Demonstrates a state-of-the-art operational capability when operating within the distribution warehouse environment through the application of AI technology and facilitates a robust communications network technology used in a working environment shared with warehouse workers.

6. It is a reliable and robust technology solution that allows DLA Distribution Warehouses to perform automated tasks without significantly lower operating speeds per existing industry trends.

7. Demonstrates compatibility with a Government data cloud environment to store and retrieve warehouse-generated data without relying on a separate commercial data cloud environment to navigate successfully.

8. Conclusively demonstrates the use of new AI technology and concepts for application and integration in the distribution and delivery of material and goods during representative distribution warehouse operations in an innovative way.

PROJECT DURATION and COST:

PHASE I: NTE 12 Months $150K- Base NTE $100K base 6-9 Months, - Option 1 NTE $50K base 3-6 Months

PHASE II: – NTE 24 Months $1.6M - Base 12-18 months, $1M Option 6 Months NTE $.6M

PERIOD OF PERFORMANCE: The phase one period of performance is not to exceed 12 months total. Options are not automatic. Approval is at the discretion of the DLA SBIP Program Manager. The decision is based on Project Performance, Priorities of the Agency, and/or the availability of funding.

PHASE I: Perform a design study to determine how to use artificial intelligence to optimize DLA Distribution Warehouse operations, sustainment, and logistics support. Deliver a final design of AI's capabilities, a simulation model of DLA Distribution assets, and a demonstration of an AI-infused model capable of making intelligent trade-off decisions to meet specified PM requirements. A successful design will optimize support, minimize DLA Distribution Warehouse system downtime, and maximize system availability, using logistics inputs (component failure rates, shipping times, repair times, maintenance man-hours, and warehouse staffing).

The research and development goals of Phase I provide Small Business eligible Research and Development firms the opportunity to successfully demonstrate how their proposed warehouse AI concept of operations (CONOPS) improves the distribution of goods and materials within the DLA distribution enterprise and effectively lessens the time to provide needed supplies to the Warfighter. The selected vendor will conduct a feasibility study to:

1. Address the requirements described above in the Description Section for warehouse AI operations.

2. Identify capability gap(s) and the requirement for DLA to use AI in the DLA Distribution Operations environment.

3. Develop the vendor's Concept of Operations (CONOPS) to utilize warehouse AI and describe clearly how the requirements develop from it.

Note: During Phase I of the SBIR, testing is not required.

The vendor must create a CONOPS for Warehouse AI in support of both routine and wartime distribution warehouse operations. The concept of operations will cover the utilization of artificial intelligence within distribution warehouses during routine procedures, describing precisely all operational requirements as part of this process. This artificial intelligence requirement intends to operate inside distribution warehouses successfully.

The deliverables for this project include a final report, including a cost breakdown of courses of action.

PHASE II: Based on the research and the concept of operations developed during Phase I, the research and development goals of Phase II emphasizes the execution of the Warehouse AI system following the typical DLA Distribution Warehouse concept of operations for materiel handling. During Phase II, the vendor will:

1. Address the specific user requirements, functional requirements, and system requirements as defined and provided by DLA.

2. Develop a prototype Warehouse AI system for Developmental Test and Evaluation (DT&E) and Operational Test and Evaluation (OT&E).

3. Implement government cybersecurity controls in the prototype design and secure all necessary cybersecurity certifications to operate the equipment in the DLA warehouse environment with DOD cloud connections.

4. Design the prototype equal to the technology maturity of Technology Readiness Level (TRL) 9 after Phase II.

5. Deliver a final Distribution Warehouse AI prototype system to DLA capable of successfully executing the operational concepts established in the Phase I CONOPS.

The DLA Warehouse Artificial Intelligence system will operate across the United States at various DLA Distribution Center sites mutually agreed upon between DLA R&D and DLA Distribution HQ. This project's deliverables include a final report, including a cost breakdown of courses of action (COAs).

PHASE III DUAL USE APPLICATIONS: PHASE III: Dual Use Applications: At this point, there is no specific funding associated with Phase III. During Phase I and Phase II, the progress made should result in a vendor's qualification as an approved source for a Warehouse Artificial Intelligence system and support participation in future procurements.

COMMERCIALIZATION: The manufacturer will pursue the commercialization of the Warehouse Artificial Intelligence (AI) technologies and designs developed to apply to the warehouse environment. The processes developed in preliminary phases and potential commercial sales of manufactured mechanical parts or other items. The first path for commercial use will be at DLA's twenty-six Distribution Centers and twenty Disposition Centers. When fielded, DLA estimates 20 - 26 units, but the number of units could be more.

 

[Deleted member 118]

Research and Testing of a Robotic Arm Embedded with Artificial Intelligence (AI) for use within Defense Logistics Agency (DLA) Distribution Center Warehouses | SBIR.gov

www.sbir.gov

Description:

RT&L FOCUS AREA(S): Warfighter Requirements (GWR) TECHNOLOGY AREA(S): Information Systems The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Concept statement: DLA is exploring the use of robots to include robot arms to better understand what capability these machines provide to leverage human tasks in materiel handling. One approach DLA wants to explore is to incorporate Artificial Intelligence into individual robots to provide autonomy to resolve current issues in materiel handling. Develop a Robotic Arm that utilizes an Artificial Intelligence (AI) solution (with deep learning if applicable) to provide a state-of-the-art capability to identify items, and pick, pack, and arrange picked items within selected boxes and operate within the DLA Distribution Warehouse environment. Additionally, the AI-embedded Robotic Arm must provide the adaptive pushing displacement required for the tight packing of items within shipping boxes, and must communicate with various warehouse systems (e.g., Internet of Things (IoT)) as needed. The desired solution should minimize infrastructure modifications to enable the artificial intelligence embedded robotic arm to operate within the warehouse environment. The goal of this effort is for the vendor to develop a capability an AI-embedded robotic arm system operating in the warehouse, that addresses the requirements for integrating with warehouse communications systems onsite (if required), such as the Warehouse Execution System (WES) at the specific warehouse. As such, this capability provides for the seamless execution of the AI-embedded Robotic Arm and its subsequent interactions with any future Smart Warehouse systems that may be developed and employed. Provide a report with a detailed analysis that captures concepts on using robotics to include robotic arms which incorporate features of artificial intelligence. The study and analysis can include concepts and approaches that are innovative and may not be known from current market research, or individual development through industry or academia. Prospective vendors should organize the objectives by priority as shown below: • Explore using methods and schemes that allow for least cube space. • How the robot system adapts or can be integrated into existing Warehouse Management System (WMS). • How the robotic system seamlessly integrates into communications and equipment like current Internet of Things (IoT), 5G communications, and knowledge systems to manage warehouse operations. • Future communications systems and equipment beyond 5G, and IoT. The state-of-the-art AI-embedded Robotic Arm solution must integrate into the existing warehouse communications systems to communicate with the WES system to allow for the embedded AI Robotic Arm to receive automated tasking instructions to pick and pack boxes, crates, and bins and use AI to accurately identify boxes, cases, crates, and individual end-items using loaded configuration information and task instructions. The provided packing instructions will be pushed to the Robotic Arm by the WES. The robotic arm should be able to operate continually as needed, and report back to the WES on the programmed Robotic Arm’s task success or failure. At a minimum, the prospective vendors should: • Explore what systems in a robotic arm or other mechanism can offer the best way to identify materiel correctly that matches with materiel transaction requests (i.e., machine vision). • Recommend methods and schemes that impact or complement robotic functionality like accurate transactions using block chain. • Discuss methods where robots and robotic arms adapt to random materiel requests regardless of timeframe, location, or item request. • Discuss methods and schemes as to the flexibility of robot tasks that mimic human tasks like packing, moving parts and equipment, wrapping, and other tasks in warehouse operations. In support of routine warehouse robotic arm operations, this research seeks to identify and test a Robotic Arm utilizing AI technology used to intelligently pack boxes within the DLA distribution warehouse environment. Importantly, the selected vendor must address the DLA-identified cybersecurity requirements by testing and evaluating the government's security control. The vendor should leverage the current technologies found in both the Robotic Arm and the AI industries. This research project will operate in locations at designated DLA Distribution Centers in the United States. DESCRIPTION: Defense Logistics Agency (DLA) Distribution Modernization Program (DMP) topics of interest are research focused on a Continental United States-based robotic arm with an Artificial Intelligence (AI) solution in support of the routine warehouse end-item picking for box packing operations. The resulting solution must be integrated with existing WES communications suites and integrate with warehouse navigation systems, that: 1. Supports a joint effort between DLA Research and Development (R&D) and DLA J4 Distribution Headquarters to conduct research and test an AI-embedded warehouse Robotic Arm system that works during warehouse operations. 2. Significantly addresses an AI-embedded Robotic arm's capabilities within an operational distribution warehouse environment. 3. Features an AI-embedded Robotic Arm that can implement repetitive box packing tasks with high precision and accuracy for regular use in warehouse operations. 4. Can be integrated into warehouse communications systems such as a WES to receive tasking and report on performance status. 5. Demonstrates a state-of-the-art operational capability when operating within the distribution warehouse environment through the application of AI-embedded Robotic Arm technology and seamlessly integrates with robust communications network technologies in a distribution warehouse environment shared with warehouse workers. 6. Provides for a reliable and robust technology solution that allows DLA Distribution Warehouses to perform automated tasks without significantly lowering operating speeds per existing industry trends. 7. Demonstrates compatibility with a Government data cloud environment to store and retrieve warehouse-generated data without relying on a separate commercial data cloud environment to navigate successfully. 8. Conclusively demonstrates the use of new AI technology and concepts for application and integration with a Robotic Arm to improve the distribution and delivery of material and goods during representative distribution warehouse operations in an innovative way. 9. All robotic/AI software control remains within the DLA server and does not transfer/communicate out to a vendor server. 10. Robotic arm needs to safely maneuver around humans without the need of a safety cage. PHASE I: Perform a design study to determine how to use a robotic arm that utilizes artificial intelligence to optimize DLA Distribution Warehouse operations, sustainment, and logistics support. Deliver a final design of a Robotic Arm with AI capability, a simulation model of DLA Distribution assets, and a demonstration of an AI-infused Robotic Arm model capable of making intelligent trade-off decisions to meet specified PM requirements. A successful design optimizes support, minimizes DLA Distribution Warehouse system downtime, and maximizes system availability using logistics inputs (component failure rates, shipping times, repair times, maintenance man-hours, and warehouse staffing). The SBIR Phase I expectation is to provide and successfully demonstrate how their proposed AI-embedded Robotic Arm concept of operations (CONOPS) improves the packing and arrangement of boxes. This automation provides for the more efficient distribution of goods and materials within the DLA distribution enterprise and effectively lessens the time to provide needed supplies to the Warfighter. The selected vendor will conduct a feasibility study to: 1. Address the requirements described above in the Description Section for AI-embedded Robotic Arm operations. 2. Identify capability gap(s) and the requirement for DLA to use an AI-embedded Robotic Arm in the DLA Distribution Operations environment. 3. Develop the vendor's Concept of Operations (CONOPS) to utilize an AI-embedded Robotic Arm and clearly describe how the requirements develop. The vendor must create a CONOPS for an AI-embedded Robotic Arm to support both routine and wartime distribution warehouse operations. The concept of operations covers the utilization of artificial intelligence with Robotic Arms within DLA distribution warehouses during routine box packing procedures, precisely describing all operational requirements as part of this process. The vendor must provide a CONOPS that includes the following tasks: • Picking, placing, and relocating items where needed • Perform packing operations mimicking human actions to complete the same steps. • Wrapping tasks to protect materiel, food, perishables, or consumables. • Distinguish in how to perform operations that have hazardous materials, or containers with volatile, caustic, corrosive, or possible explosive content. • Other operations in a warehouse as may be described with end users. This project's deliverables include a final report, including a cost breakdown of the proposed courses of action (COAs). Phase I – 6 Months $100K Phase II – 24 Months $1.6M PHASE II: Based on the research and the concept of operations developed during Phase I, Phase II's research and development goals emphasize the development of the AI-embedded Robotic Arm system following the typical DLA Distribution Warehouse concept of operations for materiel handling. During Phase II, the vendor will: 1. Address the specific user requirements, functional requirements, and system requirements as defined and provided by DLA. 2. Develop a prototype AI-embedded Robotic Arm system for Developmental Test and Evaluation (DT&E) and Operational Test and Evaluation (OT&E). 3. Implement government cybersecurity controls in the prototype design, and secure all necessary cybersecurity certifications to operate the AI-embedded Robotic Arm equipment in the DLA warehouse environment with DOD cloud connections. The DLA AI-embedded Robotic Arm system will operate across the United States at various DLA Distribution Center sites mutually agreed upon between DLA R&D and DLA Distribution HQ. This project's deliverables include a final report, including a cost breakdown of courses of action (COAs). PHASE III DUAL USE APPLICATIONS: Phase III is any proposal that “Derives From”, “Extends” or Completes a transition from a Phase I or II project. Phase III proposals will be accepted after the completion of Phase I and or Phase II projects. There is no specific funding is associated with Phase III, except Phase III is not allowed to use SBIR/STTR coded funding. Any other type funding is allowed. Phase III proposal Submission. Phase III proposals are emailed directly to DLA SBIR2@dla.mil. The PMO team will set up evaluations and coordinate the funding and contracting actions depending on the outcome of the evaluations. A Phase III proposal should follow the same format as Phase II for the content, and format. There are, however, no limitations to the amount of funding requested, or the period of performance. All other guidelines apply. During Phase I and Phase II, the progress made should result in a vendor's qualification as an approved source for an AI-embedded Robotic Arm system and support participation in future procurements. COMMERCIALIZATION: The manufacturer will pursue the commercialization of the AI-embedded Robotic Arm technologies and designs developed to apply to the warehouse environment-- the processes developed in preliminary phases and potential commercial sales of manufactured mechanical parts or other items. The first path for commercial use is at DLA's twenty-six Distribution Centers and twenty Disposition Centers. When fielded, DLA estimates 20 - 26 units, but the number of units could be more. REFERENCES: 1. J. J. Enright and P. R. Wurman, "Optimization and Coordinated Autonomy in Mobile Fulfillment Systems," in AAAIWS'11-09, 2011. 2. F. Wang and K. Hauser, "Stable bin packing of non-convex 3d objects with a robot manipulator," in IEEE ICRA, 2019, pp. 8698–8704. 3. F. Wang and K. Hauser, "Robot packing with known items and nondeterministic arrival order," in R: SS, 2019. 4. A. Sahbani, S. El-Khoury, and P. Bidaud, "An Overview of 3D Object Grasp Synthesis Algorithms," RAS, vol. 60, no. 3, 2012

 

[Fact Finder]

Research on Fault Diagnosis Based on Spiking Neural Networks in Deep Space Environment​

• Authors:
• Ruowei Li
  ,
• Jiabin Yuan

Authors Info & Claims
ASSE' 22: 2022 3rd Asia Service Sciences and Software Engineering ConferenceFebruary 2022 Pages 165–170https://doi.org/10.1145/3523181.3523205
Online:18 April 2022Publication History

• 0citation
• Downloads

• • Get Access

ABSTRACT​

Deep space detection technology is one of the development directions in the aerospace field today. The long-distance, high cost, high uncertainty, and severe resource limitations of deep space detection missions determine that deep space detectors need autonomous fault diagnosis capabilities. Analyze the shortcomings of the current fault diagnosis technology, and conduct a research on the fault diagnosis technology based on the spiking neural network. Its characteristics of low energy consumption, fast decision-making speed, working in discrete time and training without relying on large amounts of data meet the requirements for autonomous fault diagnosis of the detector in the deep space environment. After selecting multiple data sets and comparing them with the fault diagnosis method based on the second-generation neural network, the feasibility of the application of the autonomous fault diagnosis technology based on the spiking neural network in the deep space environment is demonstrated.

References​

1. Mass W. Networks of spiking neurons: The third generation of neural network models [J]. Neural Networks,1997, 19 (9) : 1659-1671.
2. Xing Yan, Wu Hongxin, Wang Xiaolei, Li Zhibin. Overview of Spacecraft Fault Diagnosis and Fault Tolerant Control Technology [J]. Journal of Astronautics,2003 (03) : 221 - 226.
3. Baldi P, Castaldi P, Mimmo N, A new aerodynamic decoupled frequent FDIR methodology for satellite actuator faults [J]. International Journal of Adaptive Control and Signal Processing,2014,28 (9) : 812-832.

 

[Dhm]

Research on Fault Diagnosis Based on Spiking Neural Networks in Deep Space Environment​

• Authors:
• 
  
• Ruowei Li
  ,
• 
  
• Jiabin Yuan

Authors Info & Claims
ASSE' 22: 2022 3rd Asia Service Sciences and Software Engineering ConferenceFebruary 2022 Pages 165–170https://doi.org/10.1145/3523181.3523205
Online:18 April 2022Publication History

• 0citation
• Downloads

• 
  
• • Get Access

ABSTRACT​

Deep space detection technology is one of the development directions in the aerospace field today. The long-distance, high cost, high uncertainty, and severe resource limitations of deep space detection missions determine that deep space detectors need autonomous fault diagnosis capabilities. Analyze the shortcomings of the current fault diagnosis technology, and conduct a research on the fault diagnosis technology based on the spiking neural network. Its characteristics of low energy consumption, fast decision-making speed, working in discrete time and training without relying on large amounts of data meet the requirements for autonomous fault diagnosis of the detector in the deep space environment. After selecting multiple data sets and comparing them with the fault diagnosis method based on the second-generation neural network, the feasibility of the application of the autonomous fault diagnosis technology based on the spiking neural network in the deep space environment is demonstrated.

References​

1. Mass W. Networks of spiking neurons: The third generation of neural network models [J]. Neural Networks,1997, 19 (9) : 1659-1671.
2. Xing Yan, Wu Hongxin, Wang Xiaolei, Li Zhibin. Overview of Spacecraft Fault Diagnosis and Fault Tolerant Control Technology [J]. Journal of Astronautics,2003 (03) : 221 - 226.
3. Baldi P, Castaldi P, Mimmo N, A new aerodynamic decoupled frequent FDIR methodology for satellite actuator faults [J]. International Journal of Adaptive Control and Signal Processing,2014,28 (9) : 812-832.

ABSTRACT​

Deep space detection technology is one of the development directions in the aerospace field today. The long-distance, high cost, high uncertainty, and severe resource limitations of deep space detection missions determine that deep space detectors need autonomous fault diagnosis capabilities. Analyze the shortcomings of the current fault diagnosis technology, and conduct a research on the fault diagnosis technology based on the spiking neural network. Its characteristics of low energy consumption, fast decision-making speed, working in discrete time and training without relying on large amounts of data meet the requirements for autonomous fault diagnosis of the detector in the deep space environment. After selecting multiple data sets and comparing them with the fault diagnosis method based on the second-generation neural network, the feasibility of the application of the autonomous fault diagnosis technology based on the spiking neural network in the deep space environment is demonstrated.

This abstract seems to be screaming Brainchip!

 

[Deleted member 118]

https://techport.nasa.gov/datasheet/113006

 

[Deleted member 118]

Maybe someone can open a military page

(20.2 SBIR) Navy - Modeling Neuromorphic and Advanced Computing Architectures

Naval Air Warfare Center Aircraft Division- White Paper Submissions

Naval Air Warfare Center Aircraft Division- White Paper Submissions

today.iit.edu

 

[Space Cadet]

Wouldn't surprise me if uiux hasn't already posted this. He's a little bit ahead of most of us in tbis space but anyway worth a look.

https://techport.nasa.gov/view/102930#stage

 

[Space Cadet]

Wouldn't surprise me if uiux hasn't already posted this. He's a little bit ahead of most of us in tbis space but anyway worth a look.

https://techport.nasa.gov/view/102930#stage

Sorry fellas tablet crashed. Gone to phone. In above link if you click on project library and then click on show project library, you can download the PDF with more info. The last line states that at end of project they will demonstrate on neuromorpic hardware. Can't link it.

SC

 

[Deleted member 118]

Im probably barking up the wrong tree, but I’ve been trying to come up with a company that BrN maybe collaborating in regarding VR and when I search Akida on the SBR website it comes up with. Like I said it’s probably nothing but maybe someone can find a link.

Hybrid Augmented Reality Military HMD | SBIR.gov

www.sbir.gov

PhaseSpace will deliver 10 working lightweight Augmented Reality Head Mounted Display unit(s) for test and evaluation, indoors, outdoors, for mission enhancement, maintenance, repair, and medical applications. The devices will be mounted external to a standard helmet, with injection molded rugged and easily replaceable, plastic lenses - beam splitters, and characterize with the assistance of Night Vision Labs, the optical performance and user experience. PhaseSpace has demonstrated capability to create an indoor / outdoor Augmented Reality Head Mounted Display device that can be mounted to the existing night vision goggle mount, to display multiple applications to enhance missions in a lightweight form factor. PhaseSpace has garnered interest from the Department of Homeland Security for devices for over 100,000 agents, including Coast Guard, TSA, ICE, Border Patrol and others under their department, as well as interest from Dell and commercial / industrial partners, as a low cost, outdoor compatible Hardhat compatible AR Headset. The AR Market industry and commercial shipping has huge potential that has not been met with expensive, hard to use devices that can’t be easily adapted to repair and maintenance activities. AR Displays tend to have a very narrow field of view and are only useful indoors in office room type lighting. Most designs are not compatible with glasses and restrict viewing of the virtual image to an image plane two meters from the user. This creates eyestrain trying to use the device while working at arm’s length, or viewing at a distance, where the eye accommodation distance conflicts with the image plane focus. Allowing an adjustable image plane that can be set with a dial knob similar to binoculars, at half a meter to infinity, of which both are much more common for military use than fixed viewing at two meters, allows greater use, and utility. PhaseSpace modified a lens design created under a previous Navy SBIR, and commercial efforts, to allow testing on a Helmet with recently improved commercially available LCD displays, and testing photochromic filters that will darken the lens (in Phase II) when exposed to UV light for use outdoors.

PhaseSpace Motion Capture – Infinite Possibilities

www.phasespace.com

Also why did the Army postpone

U.S. Army postpones its $22 billion Microsoft augmented-reality headset deliverables to late 2022

The deal for Microsoft to supply the U.S. Army with headsets based on technology in the HoloLens is still on, even though the Army has pushed back its timeline for testing and deployment.

www.zdnet.com

https://media.defense.gov/2022/Apr/22/2002981953/-1/-1/1/DODIG-2022-085.PDF

 

[Fact Finder]

Im probably barking up the wrong tree, but I’ve been trying to come up with a company that BrN maybe collaborating in regarding VR and when I search Akida on the SBR website it comes up with. Like I said it’s probably nothing but maybe someone can find a link.

Hybrid Augmented Reality Military HMD | SBIR.gov

www.sbir.gov

PhaseSpace will deliver 10 working lightweight Augmented Reality Head Mounted Display unit(s) for test and evaluation, indoors, outdoors, for mission enhancement, maintenance, repair, and medical applications. The devices will be mounted external to a standard helmet, with injection molded rugged and easily replaceable, plastic lenses - beam splitters, and characterize with the assistance of Night Vision Labs, the optical performance and user experience. PhaseSpace has demonstrated capability to create an indoor / outdoor Augmented Reality Head Mounted Display device that can be mounted to the existing night vision goggle mount, to display multiple applications to enhance missions in a lightweight form factor. PhaseSpace has garnered interest from the Department of Homeland Security for devices for over 100,000 agents, including Coast Guard, TSA, ICE, Border Patrol and others under their department, as well as interest from Dell and commercial / industrial partners, as a low cost, outdoor compatible Hardhat compatible AR Headset. The AR Market industry and commercial shipping has huge potential that has not been met with expensive, hard to use devices that can’t be easily adapted to repair and maintenance activities. AR Displays tend to have a very narrow field of view and are only useful indoors in office room type lighting. Most designs are not compatible with glasses and restrict viewing of the virtual image to an image plane two meters from the user. This creates eyestrain trying to use the device while working at arm’s length, or viewing at a distance, where the eye accommodation distance conflicts with the image plane focus. Allowing an adjustable image plane that can be set with a dial knob similar to binoculars, at half a meter to infinity, of which both are much more common for military use than fixed viewing at two meters, allows greater use, and utility. PhaseSpace modified a lens design created under a previous Navy SBIR, and commercial efforts, to allow testing on a Helmet with recently improved commercially available LCD displays, and testing photochromic filters that will darken the lens (in Phase II) when exposed to UV light for use outdoors.

PhaseSpace Motion Capture – Infinite Possibilities

www.phasespace.com

Also why did the Army postpone

U.S. Army postpones its $22 billion Microsoft augmented-reality headset deliverables to late 2022

The deal for Microsoft to supply the U.S. Army with headsets based on technology in the HoloLens is still on, even though the Army has pushed back its timeline for testing and deployment.

www.zdnet.com

https://media.defense.gov/2022/Apr/22/2002981953/-1/-1/1/DODIG-2022-085.PDF

I think the answer is the tech does not work well enough as evidenced by the following extracts:

“ The reason for the pause seems to be questions about the maturity of at least some pieces of the IVAS technology.”

If Microsoft offering chip wise was good enough for the US Military you would think the following would be unnecessary:

“Microsoft and Qualcomm plan to jointly develop custom augmented-reality chips that can be used in future lightweight AR glasses, the pair announced at CES on January 4. Microsoft and Qualcomm also announced plans to integrate their AR software, namely Microsoft Mesh and the Snapdragon Spaces XR (Mixed Reality) Developer Platform”

The connection we now need is from Brainchip too one or all of these players in the ARVR space as we know Brainchip are engaged in someway with this technology space.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[Deleted member 118]

Been a while since I’ve found anything new, but going from bluehalo job description I’ve come across this

https://techport.nasa.gov/datasheet/113324

 

[Deleted member 118]

https://techport.nasa.gov/datasheet/113198

 

[Deleted member 118]

https://techport.nasa.gov/datasheet/105723

 

[Deleted member 118]

Self training Ai

https://techport.nasa.gov/datasheet/113116

 

[Fullmoonfever]

Self training Ai

https://techport.nasa.gov/datasheet/113116

View attachment 6498 View attachment 6498

Hey Rocket

Way too many GIFs today though mate....your ph battery just about fried

 

[Deleted member 118]

@Fact Finder have I posted this before?

We expect that the device that we are proposing will be extremely interesting to US Government and commercial aerospace customers who have already expressed an interest in using the RISC-V ISA. We expect that this IC will be used in similar applications as NASA use at customers such as Air Force, Lockheed Martin, Raytheon, Northrop Grumman, SSL, SEAKR, Bigelow Aerospace, Blue Origin and Spire

https://techport.nasa.gov/datasheet/102677