ORAU
Resource Constraint adaptive computing: Algorithm and optimization for ARL Auton
ORAU, Aberdeen, Maryland, United States, 21001
Resource Constraint adaptive computing: Algorithm and optimization for ARL Autonomy Stack
Overview: There is an internship opportunity from Army Research Laboratory at Aberdeen Proving Ground for graduate students with US citizenship or green card holder. The ARL team focuses on optimizing computationally expensive perception algorithms of the Autonomy stack. Autonomous vehicles use sensors such as RGB camera and LiDAR to sense the environment and build the world map for autonomous maneuver. ARL autonomy stack has many perception algorithms including object detection, semantic segmentation, image classification, etc. These sensors collect large amounts of data that have to be processed by multiple perception algorithms sharing the limited computing resource in real-time. Army SWaP (Size, Weight, and Power) requirements which significantly limit the computing and communication resources of autonomous vehicles and also limit the battery size. Therefore, ARL team will develop and apply different approaches to optimize the perception algorithms to fit in tactical unmanned vehicles (UGVs) with limited computing resources to achieve real-time operation and accomplish the mission. The student will closely work with ARL researchers in optimizing and integrating containerized ML algorithms to UGVs and evaluating the model's performance in the lab and field tests.
Organization DEVCOM Army Research Laboratory
Description About the Research
There is an internship opportunity from Army Research Laboratory at Aberdeen Proving Ground for graduate students with US citizenship or green card holder. The ARL team is mainly focused on optimizing computationally expensive perception algorithms of Autonomy stack. Autonomous vehicles use various sensors such as RGB camera and LiDAR to sense the environment and build the world map for autonomous maneuver. ARL autonomy stack has many perception algorithms including object detection, semantic segmentation, image classification etc. These sensors collect large amount of data that have to be processed by multiple perception algorithms sharing the limited computing resource in real-time. At the same time, the Army has SWaP (Size, Weight, and Power) requirements which significantly limit the computing and communication resources of autonomous vehicles and also limit the battery size. Therefore, ARL team will develop and apply different approaches to optimize the perception algorithms to fit in tactical unmanned vehicles (UGVs) with limited computing resources to achieve real-time operation and accomplish the mission. The student will closely work with ARL researcher in optimizing and integrating containerized ML algorithms to UGVs and evaluating the model's performance in the lab and field tests.
Position will include
Using Python, C++, and software repositories
Optimizing deep learning perception algorithms
Using containerization technologies such as Docker to create docker image for perception algorithms to be evaluated in ROS environment.
Deploying machine learning algorithms in a ROS environment on UGV
Documenting and publishing the results in technical reports or conference papers
The candidate does not need to have all skills right now but must be willing to learn new technology.
ARL Advisors: Peng Wang, Billy Geerhart
ARL Advisor Email: peng.wang2.civ@army.mil, billy.e.geerhart2.civ@army.mil
Keywords: Deep Learning Perception model; model optimization; ROS
About Army Research Directorate (ARD) ARL’s Army Research Directorate (ARD) focuses on exploiting concept development, discovery, technology development, and transition of the most promising disruptive science and technology to deliver to the Army fundamentally advantageous science-based capabilities through laboratory’s 11 research competencies. This intramural research directorate also manages the laboratory’s essential research programs, which are flagship research efforts focused on delivering defined outcomes.
About Network Cyber & Computational Sciences (NCCS) Sciences to enable and ensure secure resilient communication networks for distributed analytics in Multi-Domain Operations.
About ARL-RAP The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at the CCDC Army Research Laboratory (ARL) help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, propulsion and flight physics, communication and networking, and computational and information sciences.
A complete application includes
Curriculum Vitae or Resume
Three References Forms
Transcripts
Transcript verifying receipt of degree must be submitted with the application. Student/unofficial copy is acceptable
If selected by an advisor the participant will also be required to write a research proposal to submit to the ARL-RAP review panel for :
Research topic should relate to a specific opportunity at ARL
The objective of the research topic should be clear and have a defined outcome
Explain the direction you plan to pursue
Include expected period for completing the study
Include a brief background such as preparation and motivation for the research
References of published efforts may be used to improve the proposal
A link to upload the proposal will be provided to the applicant once the advisor has made their selection.
Questions about this opportunity? Please email ARLFellowship@orau.org.
Eligibility Requirements
Degree: Currently pursuing a Master's Degree or Doctoral Degree.
Academic Level(s): Any academic level.
Discipline(s):
Computer, Information, and Data Sciences
Artificial Intelligence (including Robotics, Computer Vision, and Human Language Processing)
Computer Architecture and Grids
Computer Science - Languages and Systems
Computer Science - Theoretical Foundations
Computer Science (general)
Computer Systems Analysis
Computer Systems Design (including Signal Processing)
Data Science
Databases, Information Retrieval, and Web Search
Graphics and Visualization
Human Computer Interaction
Information Science and Technology
Information Security and Assurance
Networks and Communications
Operating Systems and Middleware
Scientific Computing and Informatics
Software Engineering
Engineering (3 )
Seniority level Internship
Employment type Full-time
Job function Management and Manufacturing
Industries Government Administration
#J-18808-Ljbffr
Organization DEVCOM Army Research Laboratory
Description About the Research
There is an internship opportunity from Army Research Laboratory at Aberdeen Proving Ground for graduate students with US citizenship or green card holder. The ARL team is mainly focused on optimizing computationally expensive perception algorithms of Autonomy stack. Autonomous vehicles use various sensors such as RGB camera and LiDAR to sense the environment and build the world map for autonomous maneuver. ARL autonomy stack has many perception algorithms including object detection, semantic segmentation, image classification etc. These sensors collect large amount of data that have to be processed by multiple perception algorithms sharing the limited computing resource in real-time. At the same time, the Army has SWaP (Size, Weight, and Power) requirements which significantly limit the computing and communication resources of autonomous vehicles and also limit the battery size. Therefore, ARL team will develop and apply different approaches to optimize the perception algorithms to fit in tactical unmanned vehicles (UGVs) with limited computing resources to achieve real-time operation and accomplish the mission. The student will closely work with ARL researcher in optimizing and integrating containerized ML algorithms to UGVs and evaluating the model's performance in the lab and field tests.
Position will include
Using Python, C++, and software repositories
Optimizing deep learning perception algorithms
Using containerization technologies such as Docker to create docker image for perception algorithms to be evaluated in ROS environment.
Deploying machine learning algorithms in a ROS environment on UGV
Documenting and publishing the results in technical reports or conference papers
The candidate does not need to have all skills right now but must be willing to learn new technology.
ARL Advisors: Peng Wang, Billy Geerhart
ARL Advisor Email: peng.wang2.civ@army.mil, billy.e.geerhart2.civ@army.mil
Keywords: Deep Learning Perception model; model optimization; ROS
About Army Research Directorate (ARD) ARL’s Army Research Directorate (ARD) focuses on exploiting concept development, discovery, technology development, and transition of the most promising disruptive science and technology to deliver to the Army fundamentally advantageous science-based capabilities through laboratory’s 11 research competencies. This intramural research directorate also manages the laboratory’s essential research programs, which are flagship research efforts focused on delivering defined outcomes.
About Network Cyber & Computational Sciences (NCCS) Sciences to enable and ensure secure resilient communication networks for distributed analytics in Multi-Domain Operations.
About ARL-RAP The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at the CCDC Army Research Laboratory (ARL) help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, propulsion and flight physics, communication and networking, and computational and information sciences.
A complete application includes
Curriculum Vitae or Resume
Three References Forms
Transcripts
Transcript verifying receipt of degree must be submitted with the application. Student/unofficial copy is acceptable
If selected by an advisor the participant will also be required to write a research proposal to submit to the ARL-RAP review panel for :
Research topic should relate to a specific opportunity at ARL
The objective of the research topic should be clear and have a defined outcome
Explain the direction you plan to pursue
Include expected period for completing the study
Include a brief background such as preparation and motivation for the research
References of published efforts may be used to improve the proposal
A link to upload the proposal will be provided to the applicant once the advisor has made their selection.
Questions about this opportunity? Please email ARLFellowship@orau.org.
Eligibility Requirements
Degree: Currently pursuing a Master's Degree or Doctoral Degree.
Academic Level(s): Any academic level.
Discipline(s):
Computer, Information, and Data Sciences
Artificial Intelligence (including Robotics, Computer Vision, and Human Language Processing)
Computer Architecture and Grids
Computer Science - Languages and Systems
Computer Science - Theoretical Foundations
Computer Science (general)
Computer Systems Analysis
Computer Systems Design (including Signal Processing)
Data Science
Databases, Information Retrieval, and Web Search
Graphics and Visualization
Human Computer Interaction
Information Science and Technology
Information Security and Assurance
Networks and Communications
Operating Systems and Middleware
Scientific Computing and Informatics
Software Engineering
Engineering (3 )
Seniority level Internship
Employment type Full-time
Job function Management and Manufacturing
Industries Government Administration
#J-18808-Ljbffr