Person:

Johnson, Eric N.

Permanent Link

https://hdl.handle.net/1853/72197

Associated Organization(s)

Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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Publication Search Results

Now showing 1 - 10 of 10

The Semi-Coaxial Multirotor

( 2018-05) Bershadsky, Dmitry ; Haviland, Stephen ; Johnson, Eric N.

The ”semi-coaxial” multirotor configuration is presented including its advantages over the conventional coaxial rotor configuration. The semi-coaxial configuration retains the benefits of the coaxial configuration, and additionally alleviates the loss of efficiency encountered when rotors are stacked coaxially. In addition to being more power-efficient than the standard coaxial configuration, the described configuration allows for nearly- or fully-actuated control of a multirotor when used in configurations such as the three-armed Y6 hexarotor. Using this configuration, a new Direct Force Control (DFC) multirotor is presented: the Y6sC, a specific example of the semi-coaxial multirotor. The configuration orients six rotors in a way which allows the vehicle to hover in non-zero attitudes and translate without rotating with higher efficiency than the corresponding coaxial design.
Vision-Based Optimal Landing On a Moving Platform

(Georgia Institute of Technology, 2016-05) Nakamura, Takuma ; Haviland, Stephen ; Bershadsky, Dmitry ; Johnson, Eric N.

This paper describes a vision-based control architecture designed to enable autonomous landing on a moving platform. The landing trajectory is generated by using the receding-horizon differential dynamic programming (DDP), an optimal control method. The trajectory generation is aided by the output of a vision-based target tracking system. The vision system uses multiple extended Kalman filters which allows us to estimate the position and heading of the moving target via the observed locations. The combination of vision-based target tracking system and the receding-horizon DDP gives an unmanned aerial vehicle the capability to adaptively generate a landing trajectory against tracking errors and disturbances. Additionally, by adding the exterior penalty function to the cost of the DDP we can easily constrain the trajectory from collisions and physically infeasible solutions. We provide key mathematics needed for the implementation and share the results of the image-in-the-loop simulation and flight tests to validate the suggested methodology.
Vision-Based Closed-Loop Tracking Using Micro Air Vehicles

(Georgia Institute of Technology, 2016) Nakamura, Takuma ; Haviland, Stephen ; Bershadsky, Dmitry ; NodeIn, Daniel Magree ; Johnson, Eric N.

This paper describes the target detection and tracking architecture used by the Georgia Tech Aerial Robotics team for the American Helicopter Society (AHS) Micro Aerial Vehicle (MAV) challenge. The vision system described enables vision-aided navigation with additional abilities such as target detection and tracking all performed onboard the vehicles computer. The author suggests a robust target tracking method that does not solely depend on the image obtained from a camera, but also utilizes the other sensor outputs and runs a target location estimator. The machine learning based target identification method uses Haar-like classifiers to extract the target candidate points. The raw measurements are plugged into multiple Extended Kalman Filters (EKFs). The statistical test (Z-test) is used to bound the measurement, and solve the corresponding problem. Using Multiple EKFs allows us not only to optimally estimate the target location, but also to use the information as one of the criteria to evaluate the tracking performance. The MAV utilizes performance-based criteria that determine whether or not to initiate a maneuver such as hover or land over/on the target. The performance criteria are closed in the loop which allows the system to determine at any time whether or not to continue with the maneuver. For Vision-aided Inertial Navigation System (VINS), a corner Harris algorithm finds the feature points, and we track them using the statistical knowledge. The feature point locations are integrated in Bierman Thornton extended Kalman Filter (BTEKF) with Inertial Measurement Unit (IMU) and sonar sensor outputs to generate vehicle states: position, velocity, attitude, accelerometer and gyroscope biases. A 6- degrees-of-freedom quadrotor flight simulator is developed to test the suggested method. This paper provides the simulation results of the vision-based maneuvers: hovering over the target, and landing on the target. In addition to the simulation results, flight tests have been conducted to show and validate the system performance. The 500 gram Georgia Tech Quadrotor (GTQ)- Mini, was used for the flight tests. All processing is done onboard the vehicle and it is able to operate without human interaction. Both of the simulation and flight test results show the effectiveness of the suggested method. This system and vehicle were used for the AHS 2015 MAV Student Challenge where the GPS-denied closed-loop target search is required. The vehicle successfully found the ground target, and landed on the desired location. This paper shares the data obtained from the competition.
Development of a 500 gram Vision-based Autonomous Quadrotor Vehicle Capable of Indoor Navigation

(Georgia Institute of Technology, 2015-05) Haviland, Stephen ; Bershadsky, Dmitry ; Magree, Daniel ; Johnson, Eric N.

This paper presents the work and related research done in preparation for the American Helicopter Society (AHS) Micro Aerial Vehicle (MAV) Student Challenge. The described MAV operates without human interaction in search of a ground target in an open indoor environment. The Georgia Tech Quadrotor-Mini (GTQ-Mini) weighs under 500 grams and was specifically sized to carry a high processing computer. The system platform also consists of a monocular camera, sonar, and an inertial measurement unit (IMU). All processing is done onboard the vehicle using a lightweight powerful computer. A vision navigation system generates vehicle state data and image feature estimates in a vision SLAM formation using a Bierman Thornton extended Kalman Filter (BTEKF). Simulation and flight tests have been performed to show and validate the systems performance.
Benchmarking of UAV Guidance Systems in Nap of the Earth (NOE) Flight

(Georgia Institute of Technology, 2014-05) Johnson, Eric N. ; Bershadsky, Dmitry

This paper describes the development of a proposed framework of metrics for the evaluation of the performance of aircraft guidance systems. The methodologies and metrics developed remain generally agnostic to whether or not the aircraft is manned. Although more complicated missions such as autonomous exploration/search, ferry, surveillance, multi-agent collaboration, and manned flight may be addressed at a later time, A-B flight scenarios are chosen to study the proposed metrics. The proposed metrics will form building blocks for the more complicated missions. Metrics development has thus far generally focused on NOE flight, and in particular on the observability of the vehicle throughout its mission. That is, a formulation of probability of detection by potential and generally unknown threats in the mission area will be the main metric. Secondary metrics provide insight into the vehicle's trajectory quality in terms of safety and comfort, experienced by both humans and machines are described as well. Scalability of the benchmarking system is also important and benchmarking should be general enough to allow guidance algorithms to be graded independently of the vehicle platform, for instance. Non-dimensionalization metrics will address this concern.
Situational and Terrain Awareness and Warning System Implementation on Android Smartphone for Manned Aviation Applications

(Georgia Institute of Technology, 2014-01) Bershadsky, Dmitry ; Dressel, Louis ; Johnson, Eric N.

General aviation (GA) aircraft are for the most part not equipped with situational awareness or alerting systems, namely in terms of traffic or terrain collision. This is largely due to lack of regulatory requirements, but also because such systems tend to be costly. By over an order of magnitude, these types of aircraft are the most common in the world's airspace. Their prevalence, combined with their more terrain-proximal flight profiles, make GA aircraft most susceptible to controlled flight into terrain (CFIT) incidents. We introduce an economical situational awareness and alerting system in an attempt to mitigate CFIT accidents in otherwise uninstrumented GA aircraft. We do so using a common smartphone to run an application which interfaces with NASA's Shuttle Radar Topography Mission (SRTM) digital terrain elevation database (DTED).
Georgia Tech Team Entry for the 2013 AUVSI International Aerial Robotics Competition

(Georgia Institute of Technology, 2013-08) Magree, Daniel ; Bershadsky, Dmitry ; Costes, Chris ; Haviland, Stephen ; Sanz, David ; Kim, Eric ; Valdez, Pierre ; Dyer, Timothy ; Johnson, Eric N.
Indoor GPS-denied Context Based SLAM Aided Guidance for Autonomous Unmanned Aerial Systems

(Georgia Institute of Technology, 2013-08) Bershadsky, Dmitry ; Johnson, Eric N.

Autonomous exploration and mapping of environments is an important problem in robotics. Efficient exploration of structured environments requires that the robot utilize region-specific exploration strategies and coordinate with search other agents. This paper details the exploration and guidance system of a multi-quadrotor unmanned aerial system (UAS) capable of exploring cluttered indoor areas without relying on any external aides. Specifically, a graph-based frontier search algorithm which is aided by an onboard Simultaneous Localization and Mapping (SLAM) system is developed and flight tested. A technique is developed in for segmenting an indoor office-like environment into regions and to utilize the SLAM map to conduct specific activities in these regions. A goal-directed exploration strategy is created building on existing hybrid deliberative-reactive approaches to exploration. An obstacle avoidance and guidance system is implemented to ensure that the vehicle explores maximum indoor area while avoiding obstacles. The environment is explored and regions are segmented by detecting rooms and hallways which expedites the search. The multi-vehicle system is Georgia Tech Aerial Robotic Team's entry for the annual International Aerial Robotics Competition (IARC).
Georgia Tech Team Entry for the 2012 AUVSI International Aerial Robotics Competition

(Georgia Institute of Technology, 2012-08) Magree, Daniel ; Bershadsky, Dmitry ; Wang, Xo ; Valdez, Pierre ; Antico, Jason ; Coder, Ryan ; Dyer, Timothy ; George, Eohan ; Johnson, Eric N.

This paper describes the details of a Quadrotor Unmanned Aerial Vehicle capable of exploring cluttered indoor areas without relying on any external navigational aids. A Simultaneous Localization and Mapping (SLAM) algorithm is used to fuse information from a laser range sensor, an inertial measurement unit, and an altitude sonar to provide relative position, velocity, and attitude information. A wall avoidance and guidance system is implemented to ensure that the vehicle explores maximum indoor area. A model reference adaptive control architecture is used to ensure stability and mitigation of uncertainties. Finally, an object detection system is implemented to identify target objects for retrieval.
Georgia Tech Team Entry for the 2011 AUVSI International Aerial Robotics Competition

(Georgia Institute of Technology, 2011-08) Chowdhary, Girish ; Magree, Daniel ; Bershadsky, Dmitry ; Dyer, Timothy ; George, Eohan ; Hashimoto, Hiroyuki ; Kalghatgi, Roshan ; Johnson, Eric N.

his paper describes the details of a Quadrotor Unmanned Aerial Vehicle capable of exploring cluttered indoor areas without relying on any external navigational aids. An elaborate Simultaneous Localization and Mapping (SLAM) algorithm is used to fuse information from a laser range sensor, an inertial measurement unit, and an altitude sonar to provide relative position, velocity, and attitude information. A wall-following guidance rule is implemented to ensure that the vehicle explores maximum indoor area in a reasonable amount of time. A model reference adaptive control architecture is used to ensure stability and mitigation of uncertainties. The vehicle is intended to be Georgia Tech Aerial Robotic Team's entry for the 2011 International Aerial Robotics Competition (IARC) Symposium on Indoor Flight Issues.