Navigation and path planning using reinforcement learning for a Roomba robot

2016 ◽  
Author(s):  
Daniel Paul Romero-Marti ◽  
Jose Ignacio Nunez-Varela ◽  
Carlos Soubervielle-Montalvo ◽  
Alfredo Orozco-de-la-Paz
Keyword(s):  
Reinforcement Learning ◽  
Path Planning

UAV online path planning technology based on deep reinforcement learning

2020 ◽  
Author(s):  
Jiaxuan Fan ◽  
Zhenya Wang ◽  
Jinlei Ren ◽  
Ying Lu ◽  
Yiheng Liu
Keyword(s):  
Reinforcement Learning ◽  
Path Planning

scholarly journals Collision-free path planning for welding manipulator via hybrid algorithm of deep reinforcement learning and inverse kinematics

2021 ◽  
Author(s):  
Jie Zhong ◽  
Tao Wang ◽  
Lianglun Cheng
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Free Path ◽  
Inverse Kinematics ◽  
Multiple Dimensions ◽  
Continuous State ◽  
Planning Algorithm ◽  
Convergence Performance ◽  
Path Planner ◽  
Action Spaces

AbstractIn actual welding scenarios, an effective path planner is needed to find a collision-free path in the configuration space for the welding manipulator with obstacles around. However, as a state-of-the-art method, the sampling-based planner only satisfies the probability completeness and its computational complexity is sensitive with state dimension. In this paper, we propose a path planner for welding manipulators based on deep reinforcement learning for solving path planning problems in high-dimensional continuous state and action spaces. Compared with the sampling-based method, it is more robust and is less sensitive with state dimension. In detail, to improve the learning efficiency, we introduce the inverse kinematics module to provide prior knowledge while a gain module is also designed to avoid the local optimal policy, we integrate them into the training algorithm. To evaluate our proposed planning algorithm in multiple dimensions, we conducted multiple sets of path planning experiments for welding manipulators. The results show that our method not only improves the convergence performance but also is superior in terms of optimality and robustness of planning compared with most other planning algorithms.

scholarly journals Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 135513-135523
Author(s):  
Qingfeng Yao ◽  
Zeyu Zheng ◽  
Liang Qi ◽  
Haitao Yuan ◽  
Xiwang Guo ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Potential Field ◽  
Planning Method ◽  
Artificial Potential Field

scholarly journals Real Time Path Planning of Robot using Deep Reinforcement Learning

2020 ◽  
Vol 53 (2) ◽  
pp. 15602-15607
Author(s):  
Jeevan Raajan ◽  
P V Srihari ◽  
Jayadev P Satya ◽  
B Bhikkaji ◽  
Ramkrishna Pasumarthy
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Real Time ◽  
Time Path

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

2013 ◽  
Vol 14 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Xin Ma ◽  
Ya Xu ◽  
Guo-qiang Sun ◽  
Li-xia Deng ◽  
Yi-bin Li
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Mobile Robots
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