The Autonomous Systems and Intelligent Machines (ASIM) Laboratory was established in the Mechanical Engineering Department of Virginia Tech to conduct research on autonomy from an intelligent controls and dynamical systems perspective. The goal is to develop intelligent machines, such as robots, vehicles, and electromechanical mechanisms, which can operate either autonomously or semi- autonomously in coordination and interface with humans to perform useful tasks. For example, perception from sensors, sensors fusion techniques, connectivity through communications, and advanced controls and learning algorithms are used to create intelligent coordinated mobile robots and vehicles. We also attempt to learn from biological systems and human brain functions, through signal processing, to mimic intelligent motor control.
1. A first area of research focus is on the coordinated control of communication-enabled mobile robots and vehicles. These robots are equipped with on-board CPUs and various sensors which are linked to other robots/vehicles and the surrounding environment. Our mobile robots emulate autonomous and connected vehicles with vehicle-to-vehicle (V2V), Vehicle-to Infrastructure (V2I) and Vehicle to other entities (V2X) (such as road users, pedestrians, bicyclists, etc.) communications. We are developing and evaluating hybrid hierarchical control algorithms for autonomous driving, platooning, merging, collision avoidance, and several other dynamic coordinated functions of intelligent vehicles and robots in complex environments.
2. A second area of focus is driving safety, developing and conducting research on Advanced Driver Assistance Systems (ADAS) and active safety, with a special interest in mixed mode of autonomous and manual driving. In one area, we seek to understand driver cognitive perception-response abilities through human brain, eye, and physiological monitoring and modeling human motor control actions. The in-depth understanding of the driver’s perception response to external stimuli enables development of more coherent and rational ADAS, thus leading to more intelligent vehicles. These vehicles appropriately interact with drivers as needed and augment driving functions automatically as a continuously supporting co-pilot. Driving, due to its complexity and involvement of continuous sensing, decision making, and perception-response tasks, is a suitable test bed for human brain monitoring and studies. However, part of our research discoveries would be equally useful and applicable to other brain controlled tasks and functions such as limb control, patient rehabilitation, or brain control of machines.
The ASIM lab is equipped with the state-of-the-art equipment to conduct research in multi-agent robotics using mobile robots, autonomous and driverless driving, advanced driver assistance systems (ADAS), neuro-engineering and signal processing using human brain signals, and driver monitoring using eye-tracking and other physiological sensors. We are using two safe testing and evaluation paradigms for our algorithm development and evaluation, in addition to real-life autonomous vehicle testing:
1. Scaled-Down Emulation of a Smart City with Mobile Robots
2. Driving Simulator Environment
3. Autonomous Vehicle platform
In addition to computational modeling and simulation, the lab is equipped with a number of robots capable of teleoperation and autonomous motion. Mobile robots with decentralized CPUs are augmented with a host of sensors and wireless communications to operate independently and in coordinated assemblies.
A small emulated track space is created in an indoor lab to allow testing of mobility functions in a communication enabled environment. Continuous tracks, intersections, building blocks, and road features are simulated in a scaled down model space in which the mobile robots can be tested. Road markings for lanes and scaled-size traffic devices (Stop sign, etc.) are installed for robot’s (vehicle’s) vision processing. Although the robots are equipped with outdoor GPS, an indoor GPS function is required for localization. An overhead camera and digitization of the track map provides a GPS-like function. Proximity sensors emulate traffic loop detectors to determine the presence or absence of robots in intersections and estimate their speeds. Robots communicate with each other and with a central station if required.
Several other features are added to enable a complete scaled down testing environment. Robots vision system can maintain lane keeping or road following using trajectory plans. Robots can be tested for teleoperation and autonomous driving using vision, GPS, and other sensors. Various functions such as lane change maneuvers, collision avoidance, car-following, platooning, intersection control, and numerous other connected vehicle functions can be tested in this environment. We are building and enhancing this environment as the project needs arise.
Full Cabin Driving Simulator :
A full-cabin driving simulator (developed by Realtime Technologies) simulates driver-controlled and autonomous driving in a smart traffic environment with several other advanced simulation capabilities. Among the features are a totally embedded full-cabin environment with realistic control features, full vehicle dynamics with adjustable parameters, curved projection with 180-degree driver field of view, day, night, rain, snow and fog driving environments, an extensive library of roads, infrastructure, and traffic control devices, and many other capabilities for simulating realistic driving environments, along with advanced data acquisition system. A specific unique feature is the ability of multiple drivers driving within the same scenario from different networked simulators.
Desktop Simulator (Networked):
A desktop simulator using the same software is linked/networked with the full cabin simulator to allow simultaneous driving of driver-in-loop along with autonomous driving on the same road/infrastructure scenarios. Multiple drivers driving within the same scenario is also possible and allows extensive testing of ADAS in a safe environment.
The lab is fully equipped with human (driver) monitoring systems such as eye-monitoring and brain EEG sensors and modeling system, among others, for a variety of perception-response, controls, and ADAS experimentations. In collaboration with other departmental and college of engineering labs, we are equipped with extensive and comprehensive set of physiological monitoring systems.
In collaboration with other ME colleagues and laboratories, an autonomous vehicle is developed based on the Virginia Tech’s earlier vehicle platform (Victor Tango) which secured the third place nationally in DARPA Urban Challenge competition in November 2007. The vehicle is updated with new sensors and up-to-date adaptable capabilities to accommodate experimentation of various sensory platforms, sensor-fusion methods, decision algorithms, and control systems for driverless cars and connected vehicle research and testing with other similar autonomous vehicles.
Lin Y, Eskandarian A. Experimental evaluation of cooperative adaptive cruise control with autonomous mobile robots. InControl Technology and Applications (CCTA), 2017 IEEE Conference on 2017 Aug 27 (pp. 281-286). IEEE.
At this time there are no open positions in the lab. Please visit this site for potential future opportunities.
All students seeking a position in this lab must be first officially admitted to the VT Mechanical Engineering Department or another VT College of Engineering department with related expertise, e.g., ECE, CS, AOE, CEE, BEAM, among others. Only hard-working outstanding students with excellent academic records, an open mind and desire to learn, dedicated work ethics, and major accomplishments and experience, demonstrated by high quality publications and thesis in directly related areas will be considered. A few Research Assistantships and Fellowships are competitively awarded to selected students at various funding levels, depending on the availability of funds.
An open position in the lab is not a guarantee of availability of funds and scholarships.
Self-funded (national or international) visiting scholars and students may contact the lab director, Dr. Eskandarian, for additional information and research opportunities in this lab. Due to limited capacity, please contact us only if you have a directly relevant background and experience to themes of this laboratory.
Women, minorities, and members of underrepresented groups are encouraged to apply.
We are fortunate to have several colleagues in the ME department conducting relevant research in robotics and mechatronics, a major area of strength at ME/VT. You can find more information about them at the following links:
Assistive Robotics Lab Dr. Alan Asbeck
Robotics & Mechatronics Lab (RMLab) Dr. Pinhas Ben-Tzvi
Computational Multiphysics Systems Laboratory Dr. Tomonari Furukawa
Unmanned Systems Lab (USL) Dr. Kevin Kochersberger
Center for Intelligent Material Systems and Structures Dr. Andy Kurdila
Terrestrial Robotics, Engineering and Controls Laboratory Dr. Alexander Leonessa
Center for Vehicle Systems & Safety (CVeSS) Dr. Mehdi Ahmadian
Vehicle Terrain Performance Laboratory (VTPL) Dr. John Ferris
Modeling and simulation of multibody dynamics systems Dr. Corina Sandu
Ground Vehicle Chassis & Suspension Performance Dr. Steve Southward
Center for Tire Research Dr. Said Taheri
I have conducted similar research in my previous academic positions, with emphasis on vehicles active and passive safety. At GWU, I founded the Center for Intelligent Systems Research (1996-2015), which focused primarily on intelligent vehicles and transportation systems using dynamics, control systems, and signal processing for collision avoidance, vehicle dynamics, suspension design, driver assistance, trajectory planning and control, communications security, and semi-autonomous driving. Four laboratories including two driving simulators (car and truck), and a mobile robotics laboratory were established. Several sponsored projects were successfully completed in drowsy driver detection, advanced speed adaptation systems, communications integrated with traffic simulation, and vehicle dynamics simulation, among many others. At GWU, I founded and directed a University Area of Excellence (signature Program) in “Transportation Safety and Security” (2003-2015), involving multiple departments and investigators. This program enjoyed substantial internal investment which supported and complemented our externally sponsored research projects. I have also worked on vehicle crash analysis and simulations using computational mechanics and finite element modeling for crash modeling and accident reconstruction, and injury biomechanics. I helped other colleagues establish the National Crash Analysis Center at GWU in 1992 with federal and industry funding and served as its director during different periods (1998-2002 and 2013-15.)
Yuan Lin, Ph.D.
Research focus: dynamics and controls, computer vision, autonomous driving and connected vehicles.
Ph.D. in Engineering Mechanics, Virginia Tech, 2016
Graduate Research Assistant
Research focus: Cooperative perception and platooning for ground vehicles.
B.Sc. in Mechanical Engineering, Sharif University of Technology, Iran, 2016
Head Graduate Teaching Assistant for ME 4015/4016
Research focus: dynamics and control.
M.Sc. in Mechanical Engineering, Virginia Tech, 2017
M.Sc. in Mechanical and Process Engineering, Technische Universität Darmstadt, Germany, 2017
B.Sc. in Mechanical and Process Engineering, Technische Universität Darmstadt, Germany, 2014
Ph.D. visiting scholar
Research focus: vehicle dynamics, autonomous vehicles.
B.Sc. in Vehicle Engineering, Wuhan University of Technology, China, 2014
Ph.D. visiting scholar
Research focus: dynamics and control of intelligent systems.
B.Sc. in Mechanical Engineering and Automation, Nanjing University of Science and Technology, China, 2014
Graduate Research Assistant
Research focus: neural networks, machine vision.
B.Sc. in Mechanical Engineering, Virginia Tech, USA, 2017
Lirong Wang, Ph.D.
Thesis title: Cooperative Perception in Autonomous Ground Vehicles using a Mobile Robot Testbed
Currently working at Qualcomm R&D
Laboratory contact details:
438 Goodwin Hall
635 Prices Fork Road, Blacksburg, VA 24061
Department of Mechanical Engineering
College of Engineering, Virginia Tech
635 Prices Fork Road, Blacksburg, VA 24061