Deep Reinforcement Learning Based Mobile Robot Navigation in Crowd Environments

Guang Yang, Yi Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Robots are becoming popular in assisting humans. The mobile robot navigation in human crowd environments has become more important. We propose a deep reinforcement learning-based mobile robot navigation method that takes the observation from the robot's onboard Lidar sensor as input and outputs the velocity control to the robot. A customized deep deterministic policy gradient (DDPG) method is developed that incorporates guiding points to guide the robot toward the global goal. We built a 3D simulation environment using an open dataset of real-world pedestrian trajectories that were collected in a large business center. The neural network models are trained and tested in such environments. We compare the performance of our proposed method with existing algorithms that include a classic motion planner, an existing DDPG method, and a generative adversarial imitation learning (GAIL) method. Using the measurement metrics of success rate, the number of times freezing, and normalized path length, extensive simulation results show that our method outperforms other state-of-the-art approaches in both trained and untrained environments. Our method has also better generalizability compared with the GAIL method.

Original languageEnglish
Title of host publication2024 21st International Conference on Ubiquitous Robots, UR 2024
Pages513-519
Number of pages7
ISBN (Electronic)9798350361070
DOIs
StatePublished - 2024
Event21st International Conference on Ubiquitous Robots, UR 2024 - New York, United States
Duration: 24 Jun 202427 Jun 2024

Publication series

Name2024 21st International Conference on Ubiquitous Robots, UR 2024

Conference

Conference21st International Conference on Ubiquitous Robots, UR 2024
Country/TerritoryUnited States
CityNew York
Period24/06/2427/06/24

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