Flight test of Quadcopter Guidance with Vision-Based Reinforcement Learning

Reinforcement Learning (RL) has been applied to teach quadcopters guidance tasks. Most applications rely on position information from an absolute reference system such as Global Positioning System (GPS). The dependence on “absolute position” information is a general limitation in the autonomous flight of Unmanned Aerial Vehicles (UAVs)....

Human demonstrations for fast and safe exploration in reinforcement learning

Reinforcement learning is a promising framework for controlling complex vehicles with a high level of autonomy, since it does not need a dynamic model of the vehicle, and it is able to adapt to changing conditions. When learning from scratch, the performance of a reinforcement learning controller may initially be poor and -for real life...

Self-tuning gains of a quadrotor using a simple model for policy gradient reinforcement learning

Adaptive Path Planning for a Vision-Based quadrotor in an Obstacle Field: Beijing, China

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...