Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones

Deep Learning methods are replacing traditional software methods in solving real-world problems. Cheap and easily available computational power combined with labeled big datasets enabled deep learning algorithms to show their full potential. AlexNet paper(2012; Krizhevsky et al.[9]) showed feeding sufficient data into deep neural networks successfully learned to extract representations better than handcrafted features which let the start an era known as the rise of Deep Learning. Their great success in solving otherwise hard engineering problems such as object detection, voice recognition, chatbots, robotic manipulation and autonomous systems shown they can be applied to various fields thanks to their generalisation capability.[16] Path Planning(Motion Planning) is defined as computing a continuous path from starting position S to destination position D while avoiding any known obstacles in the way.[20] Whether it is in 2D or 3D geometry, any robotic system then will able to follow the computed path to reach it's destination. Real World robotic systems tend to use more explainable and reproducible algorithms based on interval based search (A star or Dijkstra) or sampling-based algorithms. We wanted to show a reward based algorithm that depends on Markov Decision Process(MDP) by trying to maximize cumulative future rewards can also complete long term path planning tasks. Advantage of using this option will allow autonomous robot(in our case simulated quadrotor) to create paths in non holonomic constraints which is something current methods fails to achieve.[1][17]