Autonomous uav obstacle avoidance using machine learning from piloted uav flights

Abstract

A machine learning engine may correlate characteristics of obstacles identified during remotely piloted UAV flights with manual course deviations performed for obstacle avoidance. An obstacle detection application may access computer vision footage to determine notable characteristics (e.g. a direction of travel and/or velocity) of obstacles identified during the piloted UAV flights. A deviation characteristics application may access flight path information identify course deviations performed by a pilot in response to the obstacles. A machine learning engine may use the obstacle characteristic data and the deviation characteristics data as training data to generate an optimal course deviation model to use by an autopilot module to autonomously avoid obstacles during autonomous UAV flights. In creating the optimal deviation model, the training data may be processed by the machine learning engine to identify correlations between certain types of manual course deviations performed to avoid certain types of obstacles.