Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation

• URL: http://arxiv.org/abs/2109.11288v1
• Date: Thu, 23 Sep 2021 10:50:47 GMT
• Title: Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation
• Authors: Linh K\"astner, Junhui Li, Zhengcheng Shen, and Jens Lambrecht
• Abstract summary: We propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstacle information. We demonstrate that the agent could learn to navigate more safely by keeping an individual safety distance dependent on the semantic information.
• Score: 5.706538676509249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent navigation among social crowds is an essential aspect of mobile robotics for applications such as delivery, health care, or assistance. Deep Reinforcement Learning emerged as an alternative planning method to conservative approaches and promises more efficient and flexible navigation. However, in highly dynamic environments employing different kinds of obstacle classes, safe navigation still presents a grand challenge. In this paper, we propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstacle information. In particular, the agent learns object-specific behavior by contemplating the specific danger zones to enhance safety around vulnerable object classes. We tested the approach against a benchmark obstacle avoidance approach and found an increase in safety. Furthermore, we demonstrate that the agent could learn to navigate more safely by keeping an individual safety distance dependent on the semantic information.

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