Obstacle Avoidance Path Planning for Mobile Robot Based on Ant-Q Reinforcement Learning Algorithm

2007 ◽  
pp. 704-713 ◽  
Author(s):  
Ngo Anh Vien ◽  
Nguyen Hoang Viet ◽  
SeungGwan Lee ◽  
TaeChoong Chung
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Reinforcement Learning Algorithm

scholarly journals A Review of Mobile Robot Path Planning Based on Deep Reinforcement Learning Algorithm

2021 ◽  
Vol 2138 (1) ◽  
pp. 012011
Author(s):  
Yanwei Zhao ◽  
Yinong Zhang ◽  
Shuying Wang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robot ◽  
Video Game ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Basic Knowledge ◽  
Target Point ◽  
Reinforcement Learning Algorithm

Abstract Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.

Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation

2004 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Devendra P. Garg
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
Path Tracking ◽  
Mobile Robot Navigation ◽  
Reinforcement Learning Algorithm

This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot’s path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm.

Deep Deterministic Policy Gradient for Navigation of Mobile Robots

2021 ◽  
Vol 40 (1) ◽  
pp. 349-361
Author(s):  
Junior Costa de Jesus ◽  
Jair Augusto Bottega ◽  
Marco Antonio de Souza Leite Cuadros ◽  
Daniel Fernando Tello Gamarra
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Learning Algorithm ◽  
Mobile Robot Navigation ◽  
Simulated Environments ◽  
The Neural Network ◽  
Policy Gradient ◽  
Reward Functions ◽  
Continuous Actions ◽  
Reinforcement Learning Algorithm

This article describes the use of the Deep Deterministic Policy Gradient network, a deep reinforcement learning algorithm, for mobile robot navigation. The neural network structure has as inputs laser range findings, angular and linear velocities of the robot, and position and orientation of the mobile robot with respect to a goal position. The outputs of the network will be the angular and linear velocities used as control signals for the robot. The experiments demonstrated that deep reinforcement learning’s techniques that uses continuous actions, are efficient for decision-making in a mobile robot. Nevertheless, the design of the reward functions constitutes an important issue in the performance of deep reinforcement learning algorithms. In order to show the performance of the Deep Reinforcement Learning algorithm, we have applied successfully the proposed architecture in simulated environments and in experiments with a real robot.

scholarly journals Improved Path Planning for Indoor Patrol Robot Based on Deep Reinforcement Learning

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 132
Author(s):  
Jianfeng Zheng ◽  
Shuren Mao ◽  
Zhenyu Wu ◽  
Pengcheng Kong ◽  
Hao Qiang
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Target Position ◽  
Convergence Speed ◽  
Position Information ◽  
Image Information ◽  
Navigation Task ◽  
Reinforcement Learning Algorithm

To solve the problems of poor exploration ability and convergence speed of traditional deep reinforcement learning in the navigation task of the patrol robot under indoor specified routes, an improved deep reinforcement learning algorithm based on Pan/Tilt/Zoom(PTZ) image information was proposed in this paper. The obtained symmetric image information and target position information are taken as the input of the network, the speed of the robot is taken as the output of the next action, and the circular route with boundary is taken as the test. The improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by about 40%, and the loss function is more stable.

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