18259111695

@jeffreychan

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关注的仓库(58)

    Watch 18259111695 / DynamicPathFollowingRobot

    This project explores making a wireless automatic guided robot which does not require any kind of intrusive modifications to be made to the environment, apart from installing an overhead camera. Generally environments that make use of automatic guided vehicles (AGVs) have to plan the path(s) where the robots should go before installing the tracks, like magnetic strips or metal tracks; this is an investment even before using the robots. If any change to the path(s) is required to be made, then more cost is incurred. In this paper a four wheeled differential drive robot has been controlled wirelessly to follow paths drawn on a graphical user interface within a workspace of 1.8m by 1.4m. The robot is controlled by correcting its orientation through visual feedback from a camera. Error analysis was performed to investigate how well the robot followed the path drawn. The estimated error of the robot is within a few centimeters of the path and can be reduced by modifying various thresholds.

    最近更新: 接近6年前

    Watch 18259111695 / GroundProj

    Nonlinear Autonomous Systems Labratory

    最近更新: 接近6年前

    Watch 18259111695 / gcmpc

    Guaranteed Cost Model Predictive Control Implementation

    最近更新: 接近6年前

    Watch 18259111695 / europilot

    A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.

    最近更新: 接近6年前

    Watch 18259111695 / Autoware

    Open-source software for urban autonomous driving

    最近更新: 接近6年前

    Watch 18259111695 / Vehicle_Dynamics

    Vehicle Dynamics and Control Simulation in Matlab

    最近更新: 接近6年前

    Watch 18259111695 / AirSim

    Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research

    最近更新: 接近6年前

    Watch 18259111695 / coursera_machine_learning

    About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

    最近更新: 接近6年前

    Watch 18259111695 / DMPC_intersection

    intelligent and networked control for multi vehicles on intersection

    最近更新: 接近6年前

    Watch 18259111695 / DMPC_for_platoons

    最近更新: 接近6年前

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