In this paper is proposed an inclusion of the Social Force Model (SFM) into a concrete Deep Reinforcement Learning (RL) framework for robot navigation. These types of techniques have demonstrated to be useful to deal with different types of environments to achieve a goal. In Deep RL, a description of the world to describe the states and a reward adapted to the environment are crucial elements to get the desire behaviour and achieve a high performance. For this reason, this work adds a dense reward function based on SFM and uses the forces in the states like an additional description. Furthermore, obstacles are added to improve the behaviour of works that only consider moving agents. This SFM inclusion can offer a better description of the obstacles for the navigation. Several simulations have been done to check the effects of these modifications in the average performance.
Effects of a Social Force Model Reward in Robot Navigation Based on Deep Reinforcement Learning
Advs in Intelligent Syst., Computing
Iberian Robotics conference ; 2019 ; Porto, Portugal November 20, 2019 - November 22, 2019
20.11.2019
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Human-Robot Collaborative Navigation Search Using Social Reward Sources
Springer Verlag | 2019
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