Autonomous Navigation Using Deep Reinforcement Learning in ROS

For an autonomous robot to move safely in an environment where people are around and moving dynamically without knowing their goal position, it is required to set navigation rules and human behaviors. This problem is challenging with the highly stochastic behavior of people. Previous methods believe...

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Bibliographic Details
Published inInternational journal of artificial intelligence and machine learning Vol. 11; no. 2; pp. 63 - 70
Main Authors Khekare, Ganesh, Sheikh, Shahrukh
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.07.2021
Subjects
Algorithms
Artificial intelligence
Autonomous navigation
Autonomy
Collision avoidance
Decision making
Deep learning
Human behavior
Localization
Machine learning
Mapping
Neural networks
Norms
Open access publishing
Robots
Sensors
Social norms
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Summary:For an autonomous robot to move safely in an environment where people are around and moving dynamically without knowing their goal position, it is required to set navigation rules and human behaviors. This problem is challenging with the highly stochastic behavior of people. Previous methods believe to provide features of human behavior, but these features vary from person to person. The method focuses on setting social norms that are telling the robot what not to do. With deep reinforcement learning, it has become possible to set a time-efficient navigation scheme that regulates social norms. The solution enables mobile robot full autonomy along with collision avoidance in people rich environment.
ISSN:2642-1577
2642-1585
DOI:10.4018/IJAIML.20210701.oa4