Fast Local Planner for Autonomous Helicopter

One challenge of autonomous flight is creating a system for planning routes and reacting to environments that is fast enough to take full advantage of the high speed of aircraft. This problem of obstacle avoidance is especially relevant when creating a program that lands robotic helicopters in urban environments. Since these areas are highly concentrated with static obstacles like buildings as well as moving ones like cars and people, the helicopter needs to be able to navigate many obstacles while reacting to unforeseen changes quickly and efficiently. Autonomous vehicle company Dragonfly Pictures Incorporated is building a helicopter that is designed to fly in these urban settings. It will be around 300 pounds, have a dual rotor design, and feature LIDAR, camera, and GPS sensors. The University of Pennsylvania has been chosen to design a system for identifying and navigating to safe landing zones. Our system will consist of a module that looks at the whole environment and evaluates the different possible landing zones, a module that plans the best route to the chosen landing area, and a collision avoidance system. My project focuses on designing the third component. It will need to react quickly to the immediate surroundings of the helicopter. Using information from a global planner and data from sensors, the obstacle avoider will generate commands that steer the helicopter away from nearby hazards while still continuing toward its ultimate goal.