Eighth Symposium on Indoor Flight Issues

The 2016 Symposium on Indoor Flight Issues was held during August 2016 at the American Venue in Atlanta, Georgia, USA, and in September 2016 at the Asia/Pacific Venue in Beijing, China. Links to the .pdf versions of the Symposium papers that met the formatting requirements are provided below:
Journal Paper for International Aerial Robotics Competition
No authors listed
California State Univeristy Northridge
Northridge, California, United States
The problem for this competition is the interaction between aerial robots and moving objects. The navigation will happen without GPS or any point of reference. The other goal is for interaction between other unmanned aerial vehicles.
Georgia Tech Team Entry for the 2016 AUVSI IARC
Takuma Nakamura, Stephen Haviland, Dmitry Bershadsky
Georgia Institute of Technology
Atlanta, Georgia United States
This paper describes some of the key technologies used to accomplish the control, guidance, and navigation required in the mission 7 of the international aerial robotics competition (IARC). We also discuss the details of our quadrotor aerial vehicle, Georgia Tech quadrotor mini (GTQ-mini), which is used for the 2016 IARC. The GTQ-mini carries a high performance computer, and the system platform consists of an inertial measurement unit, a laser altimeter, and a monocular camera. The monocular camera is used for two purposes: localization of the vehicle and target tracking. Our localization is powered by a Bierman Thornton extended Kalman filter which utilizes feature points extracted with the corner Harris algorithm. We use two vision techniques to detect target ground vehicles: the Haar-like feature detection and the normalizedcorrelation-coefficient-based template matching. These detections are plugged into multiple extended Kalman filters, and that achieves the fusion of the multiple outputs of the two different techniques and the tracking of multiple agents. The 3D trajectory generation is required when the vehicle approaches a ground target, and that is achieved by an optimal control method called the receding horizon differential dynamic programming. The strategy for the mission is analyzed by using the image-in-the-loop simulator. We discuss the results of the simulations and the flight tests operated using the GTQ-mini.
Autonomous Quadrotor for the 2016 IARC by Team Elikos
Christophe Bédard, David Binet, Pierre-Yves Lajoie, Justine Pepin, Antonio Sanniravong, Olivier St-Amour, Eva Terriault
École Polytechnique de Montréal
Montréal, Quebec, Canada
This paper presents different aspects of team Elikos’ solution attempting to resolve two of the most trending issues of the moment in this domain, being the interaction between robots, and navigation in a sterile environment with no external navigation aids such as GPS. Using various sensors including GNC sensors, camera and lasers, the vehicle shall be able of such interaction while avoiding obstacles, and present autonomous navigation capabilities based on computer vision and sensors analytics work.
HITCSC’s Aerial Vehicle Solution for the Mission 7 of the IARC
Ning Hao, Ruihang Ji, Yu Zhang, Yi Hou, Jinghao Ma
Harbin Institute of Technology
Harbin, Heilongjiang, China
This manuscript introduces the scheme and progress of HITCSC Team for the International Aerial Robotics Competition in 2016. To finish the task, we utilized a quadrotor aerial vehicle equipped with N1 autopilot, onboard computer NUC and visual cameras. The overall architecture is constituted by Navigation and Flight Control, Obstacle detection and avoidance and Localization and Robot Detection. The safety measures and the experimental tests are also introduced.
Super Squadron Technical Paper for IARC
C. Aasish, Cyril Anthony
Hindustan University
Chennai, Tamil Nadu, India
The Team Recon from India present an indigenously developed Super Squadron an autonomous quad copter designed for the 7th mission of the AUVSI International Aerial Robotics Competition (IARC).This paper describes the technical details of a quad rotor system to be used as an aerial robot for interaction with ground robots and demonstrate the mission requirements. The Super Squadron exhibits the required behaviours of autonomous flight for interaction with multiple objects on ground to recognize, track and navigate in a sterile environment with no external navigational aids. The objective of multiple object tracking would be achieved using HOG- based SVM. The algorithm runs on an ARM Processor with depth camera and the autonomous navigation is done by using optical flow with higher resolution. The instant sensing by aerial robots and interaction between aerial and ground robots would be achieved - by depth sensor and a custom developed ANN and through the use of effective path based algorithms.
Multirotor Aircraft Developed By Kennesaw State University to Compete in the 2016 IARC
Nick Schulz, Syed Ali, Brandon Hopewell, Steffen Lim, Alec Graves, David Haffner, Haris Jafri
Kennesaw State University
Marietta, Georgia, United States
For Mission 7 of the International Aerial Robotics Competition, the Kennesaw State University Aerial Robotics Team has developed a multirotor aerial vehicle capable of stable interest-based autonomous flight. Using its on-board sensor array, the multirotor can locate and interact with other robotic vehicles in order to accomplish the objectives. Additionally, the robust 3D-printed design allows the multirotor to safely withstand collisions with obstacles and other aerial vehicles encountered during the mission.
MultiBee Autonomous Quadrotor for the 2016 International Aerial Robotics Competition
Atıf Çağatay Kocaarslan, Oğuzhan Karabulut, Ali Can Türker, Ahmet Kaplan, Yakup Görür
Istanbul Technical University
Istanbul, Turkey
MultiBee team has designed an air vehicle for IARC 7and reports the detailed analysis of quadrotor in this report. Outdoor navigation systems have been easily accessible for a long while. MultiBee team for International Aerial Robotics Competition designed unique and creative solution for IARC 2016 without using GPS or SLAM technology. Our air vehicle preference is DJI M100 with the visual sensing system DJI GUIDANCE for safely cruising around the competition area. Our air vehicle is also contained with PIXY camera for rapidly detecting and enumerating ground robot.
Team Description Paper of Indian Institute of Technology Kharagpur for IARC
Aditya Agarwal, Soumyadeep Mukherjee, Manash Pratim Das, Gaurav Gardi, Sairam K, Kumar Ankit, Kumar Krishna Agarwal, Vishnu Sharma
Indian Institute of Technology, Kharagpur
Kharagpur, West Bengal, India
This paper reports the current preparation strategy of Aerial Robotics Kharagpur, participating in IARC Mission 7 2016. Our main goal includes robust, indoor localization in GPS denied environments supported by optical flow sensors. Other features like ground i-robots detection and differentiation between the target bots and patrol bots is also discussed, followed by a brief description of the herding algorithm AI algorithm to be used.
Autonomously Herding Ground Robots in a GPS-Denied Environment
No authors listed
Massachusetts Institute of Technology
Cambridge, Massachusetts, United States
This paper describes MIT UAV Team’s full system for Mission 7a of the International Aerial Robotics Competition (IARC). Our system involves a hardware layer and vision, modeling, communication, and planning software layers to herd Roombas in an environment denied of external navigation aids such as GPS or large stationary points of reference. The vision component processes the camera input from a GoPro attached to the aerial vehicle to identify Roombas and gridlines at each frame. These partial observations of the field are combined to create a global model, which allows for vehicle localization and Roomba tracking. Using this model, our vehicle utilizes a heuristic strategy for high-level coordination planning consisting of a finite state machine, where each state is a hard-coded "behavior module."
Unmanned QuadrotorHelicopter of NUAA for 2016 IARC
Qin Haiqun, Tan Qingyan, Yin Yanqing, Liu Qingwen, Bao Sheng, Shi Peng
Nanjing University
Nanjing, Jiangsu, China
The Nanjing University of Aeronautics and Astronautics team designs and develops an unmanned quadrotor helicopter to complete the seventh IARC mission. The unmanned quadrotor helicopter is designed to autonomously fly in the arena without GPS, interacting with the ground robots, and sensing and avoiding the presence of moving special ground robots. ArduPilotMega(APM) which is a full-featured multicopter UAV controller,is used as the flight control systemofquadrotor helicopter. In the case without GPS, orientation and navigation of quadrotor helicopter is based on vision, through catching and tracing the ground robots and the lines of arena. Besides, to herd the ground robots toward the green side of the arena such that as many as possible cross over the green line in the shortest amount of time, optimization control algorithm is used to quadrotor helicopter.
Naval Aeronautical and Astronautical Institute Team Entry for the 2016 AUVSI IARC
Yan Shi, Wang Chao, Bai Jie,Gong Weisi, Hua Wei
Naval Aeronautical and Astronautical Institute
Yantai, Shandong, China
This paper describes the details of an autonomous aircraft capable of navigating in a sterile environment with no external navigation aids such as GPS or large stationary points of reference such as walls and interacting with autonomous ground robots. The vehicle localizes itself by laser device and a camera installed on the gimbal, the use of the gimbal can keep the camera stable to the inertial coordinate system. With the help of the processor--odroid, the vehicle can process the data from onboard sensors and plan the mission all by itself. During its flight, the onboard sensors measure parameters such as altitude, velocity. Then send them to odroid. According to these data, the odroid could localize itself and identifies the autonomous ground robots and obstacle ground robots. And then, odroid perform related algorithm and send instruction to the flight controller--pixhawk, according to the instruction, pixhawk could control the vehicle’s attitude and speed perfectly, so the vehicle can fulfill the task such as avoid obstacle ground robots, determine and predict trajectory of the autonomous ground robots, track the autonomous ground robot, and descends upon it when the collision condition is meeting. The vehicle is intended to be Naval Aeronautical Engineering Institution’s entry for the International Aerial Robotics Competition in 2016.
Technical Paper for the IARC
Thomas Rostrup Andersen, Brage Eikanger, Hakon Flatval, Simen Haugo, Martin Lysvand Sollie, Tobias Stene Hansteen, Christian Wilhelmsen
Norwegian University of Science and Technology
Trondheim, Norway
The aim of this paper is to describe a system for a fully autonomous MAV capable of solving the seventh IARC mission. The system is designed to perform on-line strategic planning, collision avoidance, robot-to-robot interaction and navigation, relying on INS sensors and camera vision in a GPS-denied environment.
Autonomous Object Tracking and Obstacle Avoiding Multirotor of Team Aeolus
Sovan Das, Akshay Kalkunte Suresh, Siddharth R N, Anush S Kumar, Pranav Singhania
PES University
Bangalore, Karnataka, India
This paper details the design and assembly of an autonomous micro aerial vehicle with navigation capabilities in GPS denied environment developed by Team Aeolus, PES University. It uses a downward facing optical flow sensor for state estimation and computer vision using an Intel® RealSense™ (R200) camera for localisation. It is capable of tracking and guiding multiple randomly moving ground robots by priority assignment programming while actively making use of dynamic path planning to avoid ground based and aerial obstacles observed by a rotating LIDAR Sensor.
PCC's Autonomous Air Vehicle System for IARC 2016
Stephanie Guzman, Alberto Heras, Stephen Jewell, Colin LaSharr, Ryan Province, Frank Manning
Pima Community College
Tucson, Arizona, United States
The Pima Community College UAV Club has designed an air vehicle system to compete in the International Aerial Robotics Competition (IARC). The rules require an autonomous air vehicle to herd a group of 10 ground robots while avoiding collisions with a second group of 4 obstacle robots. All 14 ground-based robots are themselves autonomous and move according to their own internal algorithms, including specified responses to external collisions and mechanical forces. The air vehicle is designed to use machine vision as well as lidar and sonar scanning to sense the positions of ground robots, and to navigate relative to a 20m x 20m arena. The arena is marked with a known grid pattern.
Development of an Aerial Robot for Flying in Confined Spaces and Interacting with Ground Robots
Lukas Vacek, Benjamin T. Kramer
University of Pennsylvania
Philadelphia, Pennsylvania, United States
Penn Aerial Robotics will present a solution to Mission 7 of the International Aerial Robotics Competition 2016. In this competition, a single quadcopter will herd 10 ground robots towards a goal line while avoiding moving obstacles. Our solution actively simulates the position of the ground robots to determine which ground robots are at risk of going out of bounds and require a direction change. Accurate maneuvering, navigation and ground robot tracking is achieved through computer vision using downward and front facing cameras. The Intel NUC on board the quadcopter collects data from various data sources through ROS and runs advanced algorithms, including optical flow, feature detection and inertial navigation to feed accurate vision position estimates to the onboard autopilot. Control of the quadcopter is achieved by sending set points through MAVLink to the Pixhawk, which then executes the necessary maneuvers to reach the desired 3D position and orientation.
Autonomous Quadrotor for the 2016 IARC
Alec Ten Harmsel, Sajan Patel, Ryan Gysin
Universiy of Michigan
Ann Arbor, Michigan, United States
Unmanned Aerial Vehicles (UAV) are becoming more popular for both professional and casual uses, but are restricted to open areas and require GPS for navigation. Vehicles capable of flying in environments without relying on GPS will pave the way toward redefining currently outdated and expensive methods of structural inspection, search and rescue, and law enforcement operations that often take place in areas with limited GPS availability. Michigan Autonomous Aerial Vehicles (MAAV) designs and builds lightweight quadrotor UAVs capable of stable, autonomous flight without GPS. MAAV’s vehicle will compete in the 2016 International Aerial Robotics Competition (IARC) where it will demonstrate its ability to autonomously manage a herd of ground vehicles in an open environment. Using a combination of control, computer vision, and path planning algorithms, it will herd ground robots over the goal line in the required time.
ZMART Technical Report
No authors listed
Zhejiang University
Hangzhou, Zhejiang, China
The Zhejiang University Micro-Aerial Robotics Team (ZMART) has prepared to participate the 2014 International Aerial Robotics Competition (IARC). Our team aims to demonstrate interaction with one moving object while autonomously navigating in an sterile open environment. The basic system architecture consists of a quadrotor helicopter platform, control units, different kinds of sensors, communication module, a RC controller and a base station. The hardware structure, as well as the algorithm structure, will be introduced in this report.
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