Autonomous Mobile Robots: From Theory to Practice
Master the fundamentals of mobile robot design, including locomotion, perception, localization, and navigation in complex environments.
Master the fundamentals of mobile robot design, including locomotion, perception, localization, and navigation in complex environments.
Dive deep into the world of autonomous mobile robots with this comprehensive course from ETH Zurich. Explore the evolution of robots from stationary factory workhorses to complex machines capable of navigating our daily environments. This course covers essential concepts and algorithms for developing autonomous mobile robots, with a focus on locomotion, kinematics, environment perception, probabilistic map-based localization and mapping, and motion planning. You'll study various robot types, including wheeled robots, legged robots, and drones, gaining a holistic understanding of mobile robotics. The curriculum closely follows the renowned textbook "Introduction to Autonomous Mobile Robots" by Siegwart, Nourbakhsh, and Scaramuzza, ensuring a solid theoretical foundation complemented by practical insights. By the end of the course, you'll be equipped to design and implement autonomous mobile robots capable of operating in complex real-world scenarios.
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What you'll learn
Understand and apply key concepts in mobile robot locomotion and kinematics
Design and implement environment perception systems for autonomous robots
Develop probabilistic map-based localization and mapping (SLAM) algorithms
Create effective motion planning strategies for complex environments
Apply robotics principles to different types of robots (wheeled, legged, aerial)
Analyze and solve challenges in autonomous navigation and obstacle avoidance
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This comprehensive course on autonomous mobile robots covers fundamental concepts and advanced algorithms over 15 weeks. The curriculum is structured to provide a deep understanding of mobile robot design and operation in complex environments. Key topics include: mobile robot locomotion and kinematics, exploring how different robot types (wheeled, legged, aerial) move and interact with their environment; environment perception, focusing on sensor technologies and data interpretation; probabilistic map-based localization and mapping (SLAM), essential for robot navigation in unknown environments; and motion planning, enabling robots to navigate efficiently and safely. The course emphasizes both theoretical foundations and practical applications, preparing students to design and implement autonomous mobile robots for real-world scenarios. By combining lectures with exercises, students gain hands-on experience in applying concepts to various robot types, from ground-based wheeled robots to more complex legged robots and drones.
Fee Structure
Instructors
1 Course
Visionary Pioneer in Autonomous Systems and Robotics Innovation
Roland Siegwart, a distinguished Professor of Autonomous Systems at ETH Zurich since 2006, has shaped the landscape of robotics and autonomous systems through his remarkable career spanning academia, research, and entrepreneurship. After completing his studies in mechanics and mechatronics, he established himself as a leading figure in robotics research, serving as a professor at EPFL Lausanne for ten years and holding visiting positions at Stanford University and NASA Ames. His expertise in creating intelligent robots for complex environments has led to groundbreaking developments in personal and service robots, inspection devices, autonomous micro-aircraft, and walking robots. As Vice President of Research and Corporate Relations at ETH Zurich from 2010 to 2014, he initiated crucial programs like the Pioneer Fellowship and Innovation and Entrepreneurship Lab. His entrepreneurial spirit has resulted in co-founding multiple successful spin-off companies, while his academic leadership has earned him membership in the Swiss Academy of Engineering Sciences, IEEE Fellow status, and a position as an officer of the International Federation of Robotics Research. Siegwart's influence extends to organizing major robotics conferences, including IROS 2002 and ISRR 2009, and his research continues to push the boundaries of autonomous systems technology.
1 Course
Pioneering Expert in Robotic Vision and Autonomous Systems
Margarita Chli is a distinguished Professor in Robotic Vision who currently serves as director of the Vision for Robotics Lab at both the University of Cyprus and ETH Zurich. Her remarkable journey began at the University of Cambridge, where she earned her Bachelor's and Master's degrees in Information and Computing Engineering from Trinity College. After completing her Ph.D. at Imperial College London in 2009, focusing on Information Theory for efficient Simultaneous Localization And Mapping (SLAM), she joined ETH Zurich's Autonomous Systems Lab, eventually becoming Lab Deputy Director. Her groundbreaking work contributed to the first vision-based autonomous flight of a small helicopter and the demonstration of collaborative robotic perception for drone swarms
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4.6 course rating
9 ratings
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