Navigating Robots in Dynamic Environment With Deep Reinforcement Learning
IEEE Transactions on Intelligent Transportation Systems
; : 1-11, 2022.
Article
in English
| Scopus | ID: covidwho-2136502
ABSTRACT
In the fight against COVID-19, many robots replace human employees in various tasks that involve a risk of infection. Among these tasks, the fundamental problem of navigating robots among crowds, named robot crowd navigation, remains open and challenging. Therefore, we propose HGAT-DRL, a heterogeneous GAT-based deep reinforcement learning algorithm. This algorithm encodes the constrained human-robot-coexisting environment in a heterogeneous graph consisting of four types of nodes. It also constructs an interactive agent-level representation for objects surrounding the robot, and incorporates the kinodynamic constraints from the non-holonomic motion model into the deep reinforcement learning (DRL) framework. Simulation results show that our proposed algorithm achieves a success rate of 92%, at least 6% higher than four baseline algorithms. Furthermore, the hardware experiment on a Fetch robot demonstrates our algorithm’s successful and convenient migration to real robots. IEEE
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Transactions on Intelligent Transportation Systems
Year:
2022
Document Type:
Article
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