Deep reinforcement learning-based collision avoidance for an autonomous ship

2021 ◽  
Vol 234 ◽  
pp. 109216
Author(s):  
Do-Hyun Chun ◽  
Myung-Il Roh ◽  
Hye-Won Lee ◽  
Jisang Ha ◽  
Donghun Yu
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Autonomous Ship

Robot obstacle avoidance system using deep reinforcement learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaojun Zhu ◽  
Yinghao Liang ◽  
Hanxu Sun ◽  
Xueqian Wang ◽  
Bin Ren
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Environmental Parameters ◽  
Working Environment ◽  
Content Type ◽  
Practical Applications ◽  
Human Operators

Purpose Most manufacturing plants choose the easy way of completely separating human operators from robots to prevent accidents, but as a result, it dramatically affects the overall quality and speed that is expected from human–robot collaboration. It is not an easy task to ensure human safety when he/she has entered a robot’s workspace, and the unstructured nature of those working environments makes it even harder. The purpose of this paper is to propose a real-time robot collision avoidance method to alleviate this problem. Design/methodology/approach In this paper, a model is trained to learn the direct control commands from the raw depth images through self-supervised reinforcement learning algorithm. To reduce the effect of sample inefficiency and safety during initial training, a virtual reality platform is used to simulate a natural working environment and generate obstacle avoidance data for training. To ensure a smooth transfer to a real robot, the automatic domain randomization technique is used to generate randomly distributed environmental parameters through the obstacle avoidance simulation of virtual robots in the virtual environment, contributing to better performance in the natural environment. Findings The method has been tested in both simulations with a real UR3 robot for several practical applications. The results of this paper indicate that the proposed approach can effectively make the robot safety-aware and learn how to divert its trajectory to avoid accidents with humans within the workspace. Research limitations/implications The method has been tested in both simulations with a real UR3 robot in several practical applications. The results indicate that the proposed approach can effectively make the robot be aware of safety and learn how to change its trajectory to avoid accidents with persons within the workspace. Originality/value This paper provides a novel collision avoidance framework that allows robots to work alongside human operators in unstructured and complex environments. The method uses end-to-end policy training to directly extract the optimal path from the visual inputs for the scene.

Deep Reinforcement Learning for Collision Avoidance of Robotic Manipulators

2018 ◽  
Author(s):  
Bianca Sangiovanni ◽  
Angelo Rendiniello ◽  
Gian Paolo Incremona ◽  
Antonella Ferrara ◽  
Marco Piastra
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Robotic Manipulators

Path planning and dynamic collision avoidance algorithm under COLREGs via deep reinforcement learning

2021 ◽  
Author(s):  
Xinli Xu ◽  
Peng Cai ◽  
Zahoor Ahmed ◽  
Vidya Sagar Yellapu ◽  
Weidong Zhang
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Collision Avoidance
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