Reinforcement Learning for Mobile Robot Obstacle Avoidance Under Dynamic Environments

2018 ◽  
pp. 441-453 ◽  
Author(s):  
Liwei Huang ◽  
Hong Qu ◽  
Mingsheng Fu ◽  
Wu Deng
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Dynamic Environments

A Task of Miniature Mobile Robot Learning for Obstacle Avoidance through Neural Networks

2012 ◽  
Vol 151 ◽  
pp. 498-502
Author(s):  
Jin Xue Zhang ◽  
Hai Zhu Pan
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Robot Learning ◽  
Q Learning ◽  
Robot Systems ◽  
The Neural Networks ◽  
Behavior Based

This paper is concerned with Q-learning , a very popular algorithm for reinforcement learning ,for obstacle avoidance through neural networks. The principle tells that the focus always must be on both ecological nice tasks and behaviours when designing on robot. Many robot systems have used behavior-based systems since the 1980’s.In this paper, the Khepera robot is trained through the proposed algorithm of Q-learning using the neural networks for the task of obstacle avoidance. In experiments with real and simulated robots, the neural networks approach can be used to make it possible for Q-learning to handle changes in the environment.

scholarly journals Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning

2020 ◽  
Vol 5 (4) ◽  
pp. 6932-6939
Author(s):  
Binyu Wang ◽  
Zhe Liu ◽  
Qingbiao Li ◽  
Amanda Prorok
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robot ◽  
Dynamic Environments ◽  
Robot Path Planning ◽  
Robot Path

scholarly journals Multimodal Deep Reinforcement Learning with Auxiliary Task for Obstacle Avoidance of Indoor Mobile Robot

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1363
Author(s):  
Hailuo Song ◽  
Ao Li ◽  
Tong Wang ◽  
Minghui Wang
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Representation Learning ◽  
Velocity Estimation ◽  
Depth Images ◽  
Indoor Mobile Robot ◽  
Multimodal Information ◽  
Testing Environments ◽  
Accumulated Reward

It is an essential capability of indoor mobile robots to avoid various kinds of obstacles. Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great capability for learning control policies in robotics by using different sensors. However, due to the complexity of indoor environment and the heterogeneity of different sensor modalities, it remains an open challenge to obtain reliable and robust multimodal information for obstacle avoidance. In this work, we propose a novel multimodal DRL method with auxiliary task (MDRLAT) for obstacle avoidance of indoor mobile robot. In MDRLAT, a powerful bilinear fusion module is proposed to fully capture the complementary information from two-dimensional (2D) laser range findings and depth images, and the generated multimodal representation is subsequently fed into dueling double deep Q-network to output control commands for mobile robot. In addition, an auxiliary task of velocity estimation is introduced to further improve representation learning in DRL. Experimental results show that MDRLAT achieves remarkable performance in terms of average accumulated reward, convergence speed, and success rate. Moreover, experiments in both virtual and real-world testing environments further demonstrate the outstanding generalization capability of our method.

scholarly journals Cash Collection Model of Electric Power Business Office Based on Computer Algorithm

2022 ◽  
Vol 2146 (1) ◽  
pp. 012023
Author(s):  
Binghua Guo ◽  
Nan Guo
Keyword(s):  
Mobile Robot ◽  
Medical Treatment ◽  
Electric Power ◽  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Main Trend ◽  
Intelligent Robot ◽  
Treatment Service ◽  
Intelligent Algorithms

Abstract With the continuous development of intelligent algorithms, mobile robot (hereinafter referred to as MR) technology is gradually mature, which has been widely used in a variety of industries, such as industry, agriculture, medical treatment, service and so on. With the improvement of intelligent level, people have higher and higher requirements for MRs, which requires MRs to constantly adapt to different environments, especially dynamic environments. In the dynamic environment, obstacle avoidance technology has become the focus of intelligent robot research, which needs to continuously develop a variety of algorithms. By combining a variety of algorithms, we can realize obstacle avoidance and PP (hereinafter referred to as PP) of MR, which can realize obstacle avoidance more efficiently, in real time and intelligently. Multi algorithm fusion of MR has become the main trend of obstacle avoidance in the future, which will realize PP and optimization. Firstly, this paper analyzes the differences between traditional algorithms and intelligent algorithms. Then, the kinematics model and PP algorithm of MR are analyzed. Finally, the simulation is carried out.

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