출간 정보
- 2026.10.26 전자책 출간
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파일 정보
- EPUB
- 약 21.9만 자
- 42.0MB
지원 환경
- 앱
- 웹
- PC뷰어
- PAPER
ISBN
9791143831668
UCI
-
Learning Physical AI with RoboCo: Robot Design and Python Control
작품 정보
Learn robot control by watching a robot move—then change what makes it move.
Physical AI with RoboCo: Robot Design and Python Control is a hands-on introduction to robot motion and programming using the physics-based environment of Steam's RoboCo. Rather than starting with a pile of parts or pages of code, it begins with a ready-to-run four-wheel robot.
Use the W, S, A, and D keys to drive forward, reverse, and steer. Explore turning in place and diagonal movement. Then follow the same actions into Python scripts to see how Controls Mapping, DC Motor, Servo Motor, Port, Input, and the author's Educational Library work together to produce motion.
Across five parts and 25 chapters, you'll progress from your first drive to the logic behind motor control, keyboard input, reusable driving functions, and controlled experiments. Change one setting at a time—speed, acceleration, braking, or steering—and compare what happens. When the robot behaves unexpectedly, use settings and logs to investigate the cause instead of guessing.
The final project brings these skills together in a mobile information guide robot that follows a route, displays information, and returns.
The book focuses on observable behavior, repeatable experiments, and practical troubleshooting. RoboCo is a physics simulation, not a real robot, so its results should not be interpreted as guarantees of real-world hardware performance. Its value here is the opportunity to experiment with movement and control in a consistent learning environment.
Who it's for: Students, self-directed learners, makers, developers exploring robotics, and educators looking for a structured way to teach robot control with Python.
Learning environment: RoboCo software is required to reproduce the hands-on exercises and is separate from the purchase of this eBook. Companion robot files, starter scripts, and educational-library materials referenced by the book should be obtained through the author's confirmed distribution channels.
Physical AI with RoboCo: Robot Design and Python Control is a hands-on introduction to robot motion and programming using the physics-based environment of Steam's RoboCo. Rather than starting with a pile of parts or pages of code, it begins with a ready-to-run four-wheel robot.
Use the W, S, A, and D keys to drive forward, reverse, and steer. Explore turning in place and diagonal movement. Then follow the same actions into Python scripts to see how Controls Mapping, DC Motor, Servo Motor, Port, Input, and the author's Educational Library work together to produce motion.
Across five parts and 25 chapters, you'll progress from your first drive to the logic behind motor control, keyboard input, reusable driving functions, and controlled experiments. Change one setting at a time—speed, acceleration, braking, or steering—and compare what happens. When the robot behaves unexpectedly, use settings and logs to investigate the cause instead of guessing.
The final project brings these skills together in a mobile information guide robot that follows a route, displays information, and returns.
The book focuses on observable behavior, repeatable experiments, and practical troubleshooting. RoboCo is a physics simulation, not a real robot, so its results should not be interpreted as guarantees of real-world hardware performance. Its value here is the opportunity to experiment with movement and control in a consistent learning environment.
Who it's for: Students, self-directed learners, makers, developers exploring robotics, and educators looking for a structured way to teach robot control with Python.
Learning environment: RoboCo software is required to reproduce the hands-on exercises and is separate from the purchase of this eBook. Companion robot files, starter scripts, and educational-library materials referenced by the book should be obtained through the author's confirmed distribution channels.
작가 소개
Ronnie Kim is a full-stack software engineer and senior software/hardware architect with more than 20 years of experience in software development. His work spans software architecture, technical evaluation, new feature planning, and AI-driven business and development transformation.
He has also spent more than two decades studying robotics, including robot design and control, sensors, motors, actuators, and the connections between software and hardware. He previously competed in robotics competitions and received awards. His current work explores how sensing, system state, decision-making, control, actuation, and feedback come together in physical AI and robotic systems.
His teaching approach starts with a working robot. Instead of introducing complex theory or long programs first, he encourages learners to observe movement, trace it back to controls and code, change one parameter at a time, and compare the results.
He writes practical books about AI, robotics, software development, and technology education. His previously published books in Korean cover AI-era education, software careers, Arduino-based environmental sensing, and game-based learning with Entry and Arduino.
He has also spent more than two decades studying robotics, including robot design and control, sensors, motors, actuators, and the connections between software and hardware. He previously competed in robotics competitions and received awards. His current work explores how sensing, system state, decision-making, control, actuation, and feedback come together in physical AI and robotic systems.
His teaching approach starts with a working robot. Instead of introducing complex theory or long programs first, he encourages learners to observe movement, trace it back to controls and code, change one parameter at a time, and compare the results.
He writes practical books about AI, robotics, software development, and technology education. His previously published books in Korean cover AI-era education, software careers, Arduino-based environmental sensing, and game-based learning with Entry and Arduino.
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