Author: srawa

Nov 11

Presenting at IEEE Southeast Michigan 2015 Fall Conference

We are excited to be presenting our research on Obstacle Avoidance for Drones at the IEEE SEM 2015 Fall Conference, 5-6PM, Nov 17. The talk is titled

“Obstacle Detect, Sense, and Avoid for Unmanned Aerial Systems”

Abstract:

Drones, or Unmanned Aerial Systems (UAS), are expected to be adopted for a wide range of commercial applications and become an aspect of everyday life. The Federal Aviation Administration (FAA) regulates airspace access of unmanned systems and has put forward a road map for UAS adoption for commercial use. It is expected that vehicles flying outside line-of-sight be capable of sensing and avoiding other aircraft and obstacles. Whether the UAS is autonomous or remotely piloted, it is expected that drones become capable of safe flight without depending on communication links which are susceptible. Therefore, sensor technologies and real-time processing and control approaches are required on board unmanned aircraft to provide situational awareness without depending on remote operation or inter-aircraft communication. This talk overviews some research activities at the University of Michigan Dearborn to address this challenges. We are developing a stereo-vision system for obstacle detection on aerial vehicles. Using stereo video (3D video), a depth map can be generated and used to detect approaching objects that need to be avoided. We are also developing a visual navigation approach to enable drones to navigate in GPS denied environments, such as between buildings or indoors. Also, a virtual “bumper” system is being developed to over-ride commands being given by an in-experienced pilot in the case of an impending crash. Such a system could help prevent incidences such as the video drone crash at the last US Open Tennis Championships.

IEEE SEM Fall Conf 2015 - Slide Screensho

Conference Time and Venue:

Tuesday Evening, November 17, 2015, From 4:00 PM to 9:00 PM

University of Michigan – Dearborn
Fairlane Center – North Building
19000 Hubbard Drive, Dearborn,
Michigan 48126

More information on conference agenda can be found at the main page, and in this flyer.

 

Sep 22

Motion Capture using Inertial Sensors

We are developing wearable motion capture devices using inertial measurement units (IMUs). Our focus is on shoulder health, specifically monitoring stresses and providing feedback to the user in order to help prevent injury. Another application is in physical therapy guidance. The following two videos illustrate how the device can capture the arm’s orientation as a function of time.

The subject is performing four repetitions of an external rotation with the arm elevated to 90 degrees. The second video shows the three-axis accelerometer and gyroscope measurements, along with a replay of the arm’s orientation (shown as the device’s three orthogonal axes).

Aug 06

Presenting at CubeSat Workshop, SmallSat 2015, Logan, UT

I will be presenting at the CubeSat Workshop on August 8th, part of the annual conference on Small Satellites on the campus of Utah State University, in Logan, UT. The presentation is about our design of a miniature star camera for small satellites and CubeSats in particular. The goal is to build a camera and develop image processing algorithms to implement a star imager at the scale of modern smart-phone cameras, which will enable the use of an array of cameras on a CubeSat.

The Workshop and Conference schedules can be found here.

Distributed Star Imaging for CubeSats

May 09

3rd Place at Autonomous Aerial Vehicle Competition

The University of Michigan Dearborn team placed in 3rd place at the Autonomous Aerial Vehicle Competition (AAVC) held on April 28, 2015, in Dayton, OH.  The competition challenges university teams to develop drones that can fly and navigate indoors to locate and image a target. The University of Michigan Dearborn team placed in 3rd place and won $2,600, out of eight entries. The vehicle was designed by engineering undergraduate students consisting of members of the Intelligent Systems Club and professor Samir Rawashdeh’s research group. The students built and tested the vehicle (a quad-copter), developed algorithms for autonomous navigation, and developed image processing algorithms to detect the target.

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Apr 30

Real-Time Motion Capture Using Wearable Inertial Sensors

A senior design project I’ve advised on developing real-time motion capture using wearable inertial sensors recently concluded. The work was done by a group of four undergraduate students; Jingwei Luo, Yi Wang, Yongxu Yao, and Jun Yu. The following two videos highlight their results.

The wearable units consist of an STM32L series ARM Cortex-M3 controller, and a single-chip Inertial Measurement Unit (IMU), which contains a set of accelerometers, gyroscopes, and magnetic field sensors along three orthogonal axes.

 

Apr 30

Projects in Embedded Systems Design Course (Winter 2015)

ECE473 Embedded Systems Design for Winter 2015 has concluded recently. In the course, we work with ARM Cortex-M4F microcontrollers, mainly using the TI Tiva C Launchpad. The following are some of the final projects student teams have designed and implemented (in about 3.5 weeks).

RealTime Vehicle Data Display via Bluetooth Communication with OBD-II Port

Digital Laser Harp Musical Synthesizer and Remote DJ Sound Bar Controller

Embedded Encryption Module

Swipe Gesture Maze

Mar 09

CubeSat Star Imaging

We have received an award from the NASA Michigan Space Grant Consortium to develop a star imaging approach for CubeSats using an array of miniature cameras.

Attitude determination for small spacecraft in the 1-5 kilogram range is one of the major technological challenges limiting their utility for a variety of missions. In prior work, we have developed a visual approach for attitude propagation. By tracking the motion of stars in a camera’s field of view, the rotation of the spacecraft can be found in three degrees of freedom. We refer to the approach as a stellar gyroscope. The proposed work builds on the prior success and findings to pursue a promising new topology. Essentially, we will miniaturize the sensor nodes and lenses to design a camera in the size range of modern smartphone cameras capable of star imaging while utilizing the stellar gyroscope algorithm’s noise tolerance in post-processing. This will allow small spacecraft to incorporate up to one camera on each side if needed, with one centralized image processing subsystem.

MSGC Star Imaging

 

Dec 22

Obstacle "Sense and Avoid" using Stereo Vision for Unmanned Aerial Systems

We have received a small award to pursue Stereo Vision on board small unmanned aircraft (aka drones, quad-copters, multi-copters). Obstacle sense and avoid (SAA) on board aerial vehicles is a key technology that needs to be addressed in order to meet the FAA safety requirements for future integration of drones into the civil airspace. Visual approaches, such as stereo vision, can play an important role in meeting these requirements.

We will be working with the 3D Robotics X8 copter and a set of embedded cameras. Students interested in being involved, please contact me.

Illustration of depth perception using stereo cameras3D Robotics X8 copter