Prof. Farshad Khorrami Professor of Electrical & Computer Engineering Control/Robotics Research Laboratory Six Metrotech Center Polytechnic University Brooklyn, NY 11201, USA Email: [email protected] Phone: (718) 260-3227 Fax: (718) 260-3906 Farshad Khorrami received his Bachelor’s degrees in Mathematics and Electrical Engineering in 1982 and 1984 respectively from The Ohio State University. He also received his Master’s degree in Mathematics and Ph.D. in Electrical Engineering in 1984 and 1988 from The Ohio State University. Dr. Khorrami is currently a professor of Electrical & Computer Engineering Department at Polytechnic University where he joined as an assistant professor in Sept. 1988. His research interests include control systems with emphasis on nonlinear systems, robotics and automation, unmanned vehicles (fixed-wing and rotary wing aircrafts as well as underwater vehicles and surface ships), smart structures, large-scale systems and decentralized control, adaptive control, and microprocessor based control and instrumentation. Prof. Khorrami has published more than 180 refereed journal and conference papers in these areas. Springer Verlag published his book on “modelling and adaptive nonlinear control of electric motors” in 2003. He also has twelve U.S. patents on novel smart micro-positioners and actuators, control systems, and wireless sensors and actuators. He has developed the Control/Robotics Research Laboratory at Polytechnic University. The Army Research Office, National Science Foundation, Sandia National Laboratory, Office of Naval Research, Army Research Laboratory, NASA Langley Research Center and several industrial organizations, has supported his research. Prof. Khorrami has served as chairman and program committee member of several international conferences. Deconfliction and Collision Avoidance Algorithms for Unmanned Systems Unmanned vehicles for land, sea, air, and space have numerous military and civilian applications including surveillance, communication relays, rescue, traffic monitoring, border patrol, weather monitoring, transmission line and pipeline monitoring and inspection to name a few. The successful deployment of autonomous vehicles and their effective use in a variety of missions requires several key technologies including reliable obstacle detection sensors, algorithms for path planning and obstacle avoidance sensors, and robust inner-loop dynamic controllers. An important challenge in the development of these key technologies is the tight constraint on payloads (in terms of size, weight, power requirement, etc.) especially on micro aerial vehicles (MAVs). Meeting the payload constraints requires small low-power sensors and algorithms with low computational complexity and memory requirements. This presentation will first provide a broad overview of the challenges and current state-of-the-art MAV obstacle avoidance technologies, both in terms of sensor hardware (cameras, RADAR, LIDAR, etc.) and obstacle detection and avoidance algorithms (optical flow, potential fields, graph theoretic algorithms, etc.). The talk will then focus on a general-purpose path planning and obstacle avoidance technology that we have developed in recent years. This technology utilizes a hierarchical architecture comprising of a Wide-Area Planner (WAP) based on the well-known A* graph-search algorithm and a Local-Area Planner (LAP) based on our low-resource reactive obstacle avoidance algorithm called GODZILA (Game-Theoretic Optimal Deformable Zone with Inertia and Local Approach). The WAP and LAP address the far-field (or global) and the near-field (or local) aspects of path planning and

obstacle avoidance. The WAP utilizes an environment map with large range but low resolution while the LAP uses a finer resolution to focus on local obstacles. The LAP may be utilized alone if payload constraints are extreme. The distinctive feature of the GODZILA algorithm is that no prior knowledge of the environment is required and a map of the environment does not need to be built during navigation. GODZILA follows a purely local approach using current sensor measurements. This minimizes the memory and computational requirements for implementation of the algorithm, a feature that is especially attractive for small autonomous vehicles (specifically MAVs). GODZILA is highly flexible and can operate in dynamic environments (in both two-dimensional and three-dimensional spaces) with moving obstacles or with obstacles with changing sizes. Due to its low computational complexity, GODZILA can be operated at high sampling rates even on small embedded platforms (e.g., around 5Hz is attainable with a Rabbit microprocessor) resulting in a low latency navigation solution capable of reacting quickly to changes in the environment. GODZILA can also be used as the low-level path planner and obstacle avoidance solution for collaborative missions involving multiple agents.