Multilayered reinforcement learning for complicated collision avoidance problems

2002 ◽  
Author(s):  
T. Fujii ◽  
Y. Arai ◽  
H. Asama ◽  
I. Endo
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Collision Avoidance Problems

scholarly journals A Collision Avoidance Method Based on Deep Reinforcement Learning

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 73
Author(s):  
Shumin Feng ◽  
Bijo Sebastian ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Real World ◽  
Future Research ◽  
Training Process ◽  
Compact Spaces ◽  
Unknown Environment ◽  
The Neural Networks ◽  
Collision Avoidance Problems ◽  
Window Approach

This paper set out to investigate the usefulness of solving collision avoidance problems with the help of deep reinforcement learning in an unknown environment, especially in compact spaces, such as a narrow corridor. This research aims to determine whether a deep reinforcement learning-based collision avoidance method is superior to the traditional methods, such as potential field-based methods and dynamic window approach. Besides, the proposed obstacle avoidance method was developed as one of the capabilities to enable each robot in a novel robotic system, namely the Self-reconfigurable and Transformable Omni-Directional Robotic Modules (STORM), to navigate intelligently and safely in an unknown environment. A well-conceived hardware and software architecture with features that enable further expansion and parallel development designed for the ongoing STORM projects is also presented in this work. A virtual STORM module with skid-steer kinematics was simulated in Gazebo to reduce the gap between the simulations and the real-world implementations. Moreover, comparisons among multiple training runs of the neural networks with different parameters related to balance the exploitation and exploration during the training process, as well as tests and experiments conducted in both simulation and real-world, are presented in detail. Directions for future research are also provided in the paper.

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
Sign in / Sign up

Export Citation Format

Share Document