Adaptive Path Planning for a Vision-Based quadrotor in an Obstacle Field

This paper demonstrates a real life approach for quadrotor obstacle avoidance in indoor flight. A color-based vision approach for obstacle detection is used to good effect conjointly with an adaptive path planning algorithm. The presented task is to move about a set indoor space while avoiding randomly located obstacles and adapting a path to prevent future confrontation with the obstacles all together. The goal is to complete this task with a solution that is simple and efficient. The result is an adaptive path planning algorithm that evades obstacles when necessary and uses these interactions to find an obstaclefree path with simple logic. The whole task is implemented within Paparazzi, an open source autopilot software. Flight tests are performed in an indoor flight arena with simulated GPS from a camera tracking system. Through these flight tests, the approach proves to be reliable and efficient.