1. Computer Vision Driven Micro- Aerial Vehicle (MAV): Obstacles Avoidance Lim-Kwan (Kenny) Kong - Graduate Student Dr. Jie Sheng - Faculty Advisor Dr. Ankur Teredesai - Faculty Advisor Lim-Kwan (Kenny) Kong - Graduate Student Dr. Jie Sheng - Faculty Advisor Dr. Ankur Teredesai - Faculty Advisor

2. The Idea Using quad-rotor (quad-copter) Stream video using mono camera Detect static obstacles using computer vision algorithms Avoid the obstacles

3. Steps 1. Reverse engineer the AR.Drone 2. Implement 2-3 obstacle avoidance algorithms 3. Testing 4. Collaboration: 1. Ji’s object tracking algorithm 2. Sid’s Pi-map reduce algorithm

4. Obstacle avoidance Algorithm Figure 2. Optical flow differences [2] Figure 1. The basic program flow

5. Outcome Basic Avoids obstacles in flight Advanced Avoids obstacles in flight while tracking a designated object (Ji) using Pi-map reduce algorithm (Sid).

6. References [1] A. Eresen, N. Imamoglu and M. O. Efe. Autonomous quadrotor flight with vision-based obstacle avoidance in virtual environment. Expert Syst. Appl. 39(1), pp. 894-905. 2012. Available: http://dx.doi.org/10.1016/j.eswa.2011.07.087. DOI: 10.1016/j.eswa.2011.07.087. http://dx.doi.org/10.1016/j.eswa.2011.07.087 [2] W. Benn and S. Lauria. Robot navigation control based on monocular images: An image processing algorithm for obstacle avoidance decisions. Mathematical Problems in Engineering pp. 240476 (14 pp.). 2012. Available: http://dx.doi.org/10.1155/2012/240476. DOI: 10.1155/2012/240476. http://dx.doi.org/10.1155/2012/240476 [3] D. J. LeBlanc and N. H. McClamroch. Adaptive processing for vision-based ranging. Presented at American Control Conference, 1993. [4] Huili Yu, R. W. Beard and J. Byrne. Vision-based local multi-resolution mapping and path planning for miniature air vehicles. Presented at American Control Conference, 2009. ACC '09. 2009,. DOI: 10.1109/ACC.2009.5160065.