DCULab. 연구실장 이웅원

History 1. RC 쿼드콥터 만들기 2. 아두이노 쿼드콥터 만들기 %89%EB%93%9C%EB%A1%A0%EC%97%B0%EA%B5%AC%EC%8B%A4 3. Modrone mini 만들기

연구목표 자율주행 드론 (1) 자유 호버링 (2) 회피, follow me, 어쩌구 저쩌구..

멘토링 (1) 아두이노  픽스호크 (2)ROS 와 시뮬레이션의 사용 (3)Off-board( 라즈베리파이 )  Off-board Control 을 통한 간단한 trajectory control (Outdoor)  기존의 목표였던 autonomous hovering 달성  ( but 실내 optical flow hovering..)

새로운 목표 1. 군집비행이란 ? Autonomous swarm flight of quadcopter 조난자 탐색, 실종 아동 탐색, 화재진압 등등  혼자서 하기 힘든 일을 여럿이서

Researches for Swarm 1.ETH Zurich “Autonomous System Lab” 2.MIT “Computer science and Artificial Intellegience Lab” tacles 3. MIT “Aerospace Controls Laboratory” 4. Penn Engineering “Grasp Laboratory”

ETH Zurich Focus 1)micro helicopter design 2) visual 3D mapping and navigation 3) low power communication including range estimation and 4) multi-robot control under environmental constraints “For full autonomy all the computations will be carried out on an on-board embedded computer” “Energy and weight constraints and limited computing power are major challenges which require novel and efficient computer vision approaches” “ local communication not only for exchanging data between the micro helicopters, but to estimate also their relative arrangement”

ETH Zurich 1.Advance micro helicopter technology in order to build fully autonomous flying robots that are below 500 g, have long flight autonomy and are inherently safe. 2. Development and evaluation of flight stabilization and fully autonomous navigation using monocular vision as the only exteroceptive sensor. 3. Obstacle detection and avoidance using optical flow and structure from motion 4. Vision based SLAM with limited calculation power and memory 5. Low power communication using GSM and local wireless system 6. Distance estimation between air vehicles using local wireless system 7. Optimal control of groups of micro-helicopters under various constraints.

ETH Zurich

Videos SFly: Swarm of Micro Flying Robots (Finalist best video paper award IROS 2012) Swarm robots cooperate with a flying drone Visual Navigation for Flying Robots (Dr. Jürgen Sturm)

MIT Aerospace Controls Laboratory This project aims to develop a multi-agent path planning algorithm that finds collision-free trajectories for a team of mobile robots. Further, we aim to reduce computational complexity by decoupling the multi-agent planning problem into a sequence of simpler single-agent planning problems. This work focuses on solving multi-robot planning problems in continuous spaces with partial observability given a high-level domain description Video

MIT CSAIL “Robot Locomotion Group”

Penn University Grasp Laboratory “scalable sWarms of Autonomous Robots and Mobile Sensors (SWARMS) project.” Drone Swarms: The Buzz of the Future

EPFL Laboratory of Intelligent Systems

What we need 1.Obstacle detection and avoidance 2.Visual Flight stablilization and control 3.Vision based SLAM 4.communication system 5.Distance estimation between vehicles 6.Optimal control of group of quadcopters

What we going to do 1.Obstacle detection and avoidance 2.Visual Flight stablilization and control 팀장 정하기, 팀 정하기, 역할 세분화하기

Obstacle detection and avoidance 1.Vision 을 하기 전에 LiDAR 를 사용하거나 있다고 가정하고 시뮬레이션 2. 각자의 역할 (1) 이웅원 : 관련 논문 및 시뮬레이션 실험 검색 (2) 김만수 : 시뮬레이션 (3) 김연호 : ROS SLAM (4) 장은상 : Ordroid 개발 환경 구성