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A simple and highly portable MATLAB interface for learning robotics

SN APPLIED SCIENCES 2019年第8期1卷 890页

作者： Calusdian, James Yun, Xiaoping Naval Postgrad Sch Dept Elect & Comp Engn Monterey CA 93943 USA

In order to attract engineering students from across a wide range of disciplines and subject areas to the study of robotics, it was highly desirable to have an easy-to-use MATLAB-based interface for students to use to program robots and conduct robot experiments. Undoubtedly, MATLAB is one of the most prevalent programming languages engineering students use for modeling, analysis, and data visualization. In this paper, a simple and highly portable MATLAB interface is introduced to support laboratory instruction and experimentation in robotics. This robot interface leverages student's prior knowledge and skills in MATLAB so that it is easy for them to begin working and learning about many of the fascinating aspects of robotics. The interface was specifically developed for the Pioneer family of mobile robots, which uses a client-server communication model based on the Advanced Robotics Control and Operations software (ARCOS). The interface client was developed using basic MATLAB functions and communicates directly with the ARCOS server operating on many Pioneer robots. The interface is very light weight and highly portable to any computing platform that supports MATLAB.

关键词： Learning environments Devices for learning Robotics Hardware/software interfaces programming languages and software

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On the Suitability of LSP and DAP for Domain-Specific languages

On the Suitability of LSP and DAP for Domain-Specific Langua...

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ACM/IEEE International Conference on Model Driven Engineering languages and Systems (MODELS)

作者： Enet, Josselin Bousse, Erwan Tisi, Massimo Sunye, Gerson Nantes Univ CNRS Ecole Cent Nantes IMT AtlantiqueLS2NUMR 6004 Nantes France

ISBN: (纸本)9798350324983

Domain-Specific languages (DSLs) help manage the growing complexity of systems by facilitating their description and execution or simulation via tailored languages. A large part of the development costs of a DSL comes from building the associated tools it requires, such as an editor or a debugger. To reduce these costs, the Language Server Protocol (LSP) and Debug Adapter Protocol (DAP) enable the creation of generic tooling interfaces which rely on standardized services exposed by languages. However, as these protocols have been designed for General Purpose languages (GPLs), their applicability to DSLs has no yet been extensively studied. In this paper, we analyze both LSP and DAP, with an emphasis regarding their relevance for the development of tooling for DSLs. We provide both a high-level insight into these protocols, such as a dependency graph of their services, and a more fine-grained qualitative analysis of each service. We show that while some services defined by these two protocols can be provided by any DSL, others make strong assumptions on the concepts that should be part of the considered DSL. Conversely, domain-specific concepts available in some DSLs are not exploitable through these protocols, thus reducing the capabilities of generic tools.

关键词： programming languages and software Algorithm/protocol design and analysis Testing and Debugging Document and Text Editing

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ADAPTIVE BIASES AND GAINS FOR BACKPROPAGATION ALGORITHM

ADAPTIVE BIASES AND GAINS FOR BACKPROPAGATION ALGORITHM

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IFIP WG10.3 Working Conference on Applications in Parallel and Distributed Computing

作者： CHEN, T TAKAGI, M

ISBN: (纸本)0444818707

Generalized backpropagation algorithm with adaptive gains and biases for a four-layer feedforward network is proposed. The learning times on the case study of XOR classification problem are improved significantly by the introduction of adaptive gains and biases. Experiments also show that the learning ratios using only adaptive biases, comparing to basic backpropagation algorithm, keep to change stably, while that using gains solely change dramatically on different step sizes. However, both of the learning times are shorten to about 20 similar to 60%, comparing to that of basic backpropagation learning times. The best results are obtained by the combination of adaptive bias and gain.

关键词： ARTIFICIAL INTELLIGENCE, LEARNING programming languages and software PATTERN RECOGNITION, APPLICATIONS

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