Bioinspired Neural Network-Based Q-Learning Approach for Robot Path Planning in Unknown Environments

Ni, Jianjun; Li, Xinyun; Hua, Mingang; Yang, Simon X.

doi:10.2316/journal.206.2016.6.206-4526

Cited by 15 publications

(15 citation statements)

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“…where γ is the discount factor that controls the importance of immediate and future rewards. Q-learning is a model-free method of RL, where the action-value function Q (s, a) = max π E[R t |s t , a t , π] is used to represent the expectation of R t for every action-state pair (s t , a t ) and the function E[•] denotes the expected value of a random variable [39,40]. Q-learning approach is based on the optimization of the action-value function, which is calculated as follows:…”

Section: Deep Reinforcement Learning (Drl)mentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

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112

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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Section: Deep Reinforcement Learning (Drl)mentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

Self Cite

112

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“…To solve these problems, an improved Q-learning algorithm based on a bioinspired neural network (BNN) is proposed for robot path planning in this paper. Ni et al 11 have used bio inspired neural network (BNN) with Q learning technique for path planning of robot in an unknown environment. Long and Nan 12 used Fuzzy Wavelet Neural Networks method for control and tracking of non Holonomic mobile manipulator robot.…”

Section: Analysis Of Various Ai Techniques Used For Navigationmentioning

confidence: 99%

Advancement in navigational path planning of robots using various artificial and computing techniques

Parhi¹

2018

IRATJ

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In this paper, a review analysis has been carried out on navigational methodologies of robots with the help of various artificial intelligence techniques such as Fuzzy Logic, Neural Network, Genetic Algorithm, Particle Swarm Optimisation (PSO) and other Artificial Intelligence techniques. During the survey analysis a systematic review has been done and shown how various artificial intelligence techniques can be successfully used for control and navigation of various types of robots subjected to various environmental conditions.

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“…The terminal sliding-mode control to robotic manipulator along with actuator dynamics is developed by using RBFNs [25] Neural networks are also have been used in robot path planning. A modified Q-learning algorithm which is inspired from the biological neural network for robot path planning by Ni et al [26]a combination of fuzzy, wavelets and neural network for the tracking and control of mobile robots [27]. The artificial neural networks with different activation functions have been reported for mobile robot control, tracking and also have achieved a better result [28][29].…”

Section: Neural Network In Robotic Manipulatorsmentioning

confidence: 99%

Robotic Manipulator Control by Using Machine Learning Algorithms, A Review

Danthala¹,

Tjprc²

2018

IJMPERD

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Robotic field has emerged as the most important technological development in human life. Robotic systems are used as surgical equipment as the design precision of systems has become very accurate. In this scenario the robotic manipulator design is crucial. Artificial neural networks have become popular in the field of robotics. In designing the robots and robotic manipulators the machine learning techniques is playing a vital role in giving the high accurate output. In this paper, different machine learning algorithms are discussed in the scenario of robotic system design.

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Bioinspired Neural Network-Based Q-Learning Approach for Robot Path Planning in Unknown Environments

Cited by 15 publications

References 0 publications

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Advancement in navigational path planning of robots using various artificial and computing techniques

Robotic Manipulator Control by Using Machine Learning Algorithms, A Review

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