Research on Motion Control for a Mobile Robot Using Learning Control Method

Zheng, Yinqiang; Hu, Xueyang

doi:10.2478/amns.2021.1.00038

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…ey put forward the three change strategies [18]. However, practice shows that the cognitive and practical forms of integration are more common to adopt the approach of simple superposition; that is, some colleges and universities cope with the rigid requirements of the education department by adding some innovation and entrepreneurship elective courses or adapting the original courses such as career guidance courses as a type of "foreign object" is outside of professional education and is a simple superposition [19].…”

Section: Related Workmentioning

confidence: 99%

Innovation and Entrepreneurship of College Students Based on Random Matrix Big Data Analysis Model Educational Ecological Model Optimization

Chai

Zhou

2022

Mathematical Problems in Engineering

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In this paper, the random matrix big data analysis model is thoroughly studied and constructed, the ecological model of college students’ innovation and entrepreneurship education is analyzed, and the optimization model of college students’ Innovation and entrepreneurship education environmental model based on the random matrix big data analysis model is designed. This paper briefly explains the random matrix and its M-P rate theory deduces the idea of feature extraction by the difference of eigenvalue limit spectrum distribution between different nonrandom matrices and random matrices, gives the data matrix representation method of FEMPL and the specific feature composition basis, and describes the steps of FEMPL feature extraction. A performance model for predicting the running time of Hadoop jobs is constructed using a random matrix. In this paper, innovation and entrepreneurship education has been carried out gradually, and the innovation and entrepreneurship education curriculum, platform, and mechanism have been progressively established. However, there is still a gap between the proper level of innovation and entrepreneurship education development. This study takes education ecology as the research perspective, analyzes the ecosystem of typical schools of innovation and entrepreneurship education, summarizes the dimensions and parameters of the invention and entrepreneurship education ecosystem, constructs an ecological model of innovation and entrepreneurship education for college students, and analyzes the problems and causes of the current innovation and entrepreneurship education ecology for college students based on the model, to propose specific strategies to promote the ecological development of innovation and entrepreneurship education for college students.

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Section: Related Workmentioning

confidence: 99%

Innovation and Entrepreneurship of College Students Based on Random Matrix Big Data Analysis Model Educational Ecological Model Optimization

Chai

Zhou

2022

Mathematical Problems in Engineering

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“…In the work of [32], a method based on support vector machine (SVM) was proposed for the application to the mobile robot's precise motion control. The control algorithm was implemented on the ARM9 control board.…”

Section: Classification Methods For Mobile Robot Navigationmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

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Machine learning (ML) is a branch of artificial intelligence that has been developing at a dynamic pace in recent years. ML is also linked with Big Data, which are huge datasets that need special tools and approaches to process them. ML algorithms make use of data to learn how to perform specific tasks or make appropriate decisions. This paper presents a comprehensive survey of recent ML approaches that have been applied to the task of mobile robot control, and they are divided into the following: supervised learning, unsupervised learning, and reinforcement learning. The distinction of ML methods applied to wheeled mobile robots and to walking robots is also presented in the paper. The strengths and weaknesses of the compared methods are formulated, and future prospects are proposed. The results of the carried out literature review enable one to state the ML methods that have been applied to different tasks, such as the following: position estimation, environment mapping, SLAM, terrain classification, obstacle avoidance, path following, learning to walk, and multirobot coordination. The survey allowed us to associate the most commonly used ML algorithms with mobile robotic tasks. There still exist many open questions and challenges such as the following: complex ML algorithms and limited computational resources on board a mobile robot; decision making and motion control in real time; the adaptability of the algorithms to changing environments; the acquisition of large volumes of valuable data; and the assurance of safety and reliability of a robot’s operation. The development of ML algorithms for nature-inspired walking robots also seems to be a challenging research issue as there exists a very limited amount of such solutions in the recent literature.

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