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enpm661-planning-for-autonomous-robots icon enpm661-planning-for-autonomous-robots

Planning is a fundamental capability needed to realize autonomous robots. Planning in the context of autonomous robots is carried out at multiple different levels. At the top level, task planning is performed to identify and sequence the tasks needed to meet mission requirements. At the next level, planning is performed to determine a sequence of motion goals that satisfy individual task goals and constraints. Finally, at the lowest level, trajectory planning is performed to determine actuator actions to realize the motion goals. Different algorithms are used to achieve planning at different levels. This graduate course will introduce planning techniques for realizing autonomous robots. In addition to covering traditional motion planning techniques, this course will emphasize the role of physics in the planning process. This course will also discuss how the planning component is integrated with control component. Mobile robots will be used as examples to illustrate the concepts during this course. However, techniques introduced in the course will be equally applicable to robot manipulators.

enpm673-perception-for-autonomous-robots icon enpm673-perception-for-autonomous-robots

This class teaches Image Processing and Computer Vision techniques for Mobile Robots. The class covers three topics: Image Processing (Image Enhancement, Filtering, Advanced Edge and Texture), 3D Vision (3D Geometry from Multiple view geometry, Motion Processing and Stereo) and an Introduction to Image Segmentation and Object Recognition. Students will be introduced to a number of existing software toolboxes from Vision and Robotics, and will implement a number of smaller projects in Matlab.

pidcontrollertdd icon pidcontrollertdd

This repository consist of an experiment in Test Driven Development with simple PID controller along with a partner for ENPM808X course

universal_robot icon universal_robot

ROS-Industrial Universal Robots support (http://wiki.ros.org/universal_robot)

walker icon walker

Modify the TurtleBot simulation from the lecture and implement a simple walker algorithm much like a Roomba robot vacuum cleaner. The robot should move forward until it reaches an obstacle (but not colliding), then rotate in place until the way ahead is clear, then move forward again and repeat.

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