ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-1-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 145–150, 2021
https://doi.org/10.5194/isprs-annals-V-1-2021-145-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 145–150, 2021
https://doi.org/10.5194/isprs-annals-V-1-2021-145-2021

  17 Jun 2021

17 Jun 2021

DYNAMIC ROUTING FOR NAVIGATION IN CHANGING UNKNOWN MAPS USING DEEP REINFORCEMENT LEARNING

Y. Han and A. Yilmaz Y. Han and A. Yilmaz
  • Photogrammetric Computer Vision Laboratory, The Ohio State University, Columbus, OH 43210, USA

Keywords: Navigation, Dynamic Routing, Reinforcement Learning, Visual Odometry

Abstract. In this work, we propose an approach for an autonomous agent that learns to navigate in an unknown map in a real-world environment. Recognizing that the real-world environment is changing overtime such as road-closure happening due to construction work, a key contribution of our paper is adopt the dynamic adaptation characteristic of the reinforcement learning approach and develop a dynamic routing ability for our agent. Our method is based on the Q-learning algorithm and modifies it into a double-critic Qlearning model (DCQN) that only uses visual input without other aids such as GPS. Our treatment of the problem enables the agent to learn the navigation policy while interacting with the environment. We demonstrate that the agent can learn navigating to the destination kilometers away from the starting point in a real world scenario and has the ability to respond to environment changes while learning to adjust the routing plan dynamically by adjusting the old knowledge. The supplementary video can be accessed at the following link: https://www.youtube.com/watch?v=tknsxVuNwkg.