RMRL: Robot Navigation in Crowd Environments With Risk Map-Based Deep Reinforcement Learning-Reference-Cited by-同舟云学术

RMRL: Robot Navigation in Crowd Environments With Risk Map-Based Deep Reinforcement Learning

Published:2023-12 Issue:12 Volume:8 Page:7930-7937
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Yang Haodong¹^ORCID,Yao Chenpeng¹^ORCID,Liu Chengju¹^ORCID,Chen Qijun¹^ORCID

Affiliation:

1. Department of Electronics and Information Engineering, Tongji University, Shanghai, China

Funder

National Natural Science Foundation of China

Suzhou Key Industry Technological Innovation-Core Technology R&D Program

Special funds for Jiangsu Science and Technology Plan

Suzhou Key Research and Development Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/10287676/10271559.pdf?arnumber=10271559

Reference34 articles.

1. Dynamic Obstacle Avoidance in uncertain environment combining PVOs and Occupancy Grid

2. Set-Based Prediction of Pedestrians in Urban Environments Considering Formalized Traffic Rules

3. DROW: Real-Time Deep Learning-Based Wheelchair Detection in 2-D Range Data

4. Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning

5. Pedestrian-inspired sampling-based multi-robot collision avoidance

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