Instructor: Jeff Trinkle
Class time: T, R: 10:45-11:59am (New York City time). ZOOM LINK
Office hours: M, W: 2:30-3:30pm or by appointment. ZOOM LINK
Email: jeff.trinkle@lehigh.edu
Course Description:
This course will cover fundamental concepts, mathematics, and algorithms most relevant to serial chain robots, i.e., legs, arms, spines, fingers, and toes. The main topics to be studied are configuration space, position and orientation representations of bodies, forward and inverse kinematic maps, trajectory generation, coordinated linkage motions, basic legged locomotion and grasping. Semester-long projects will build on the fundamentals to explore topics of interest to students. Lab-type assignments will be done using virtual robots in simulation.
The 498 (graduate) version of this class will differ from the 398 (undergraduate) version by one or more of several possible extensions designed by the student with the approval of the instructor. These extensions include, but are not limited to, extra homework assignments, a more extensive project, an in-class presentation on a topic of useful results from the literature.
Prerequisites: basic programming ability (e.g., CSE 007 or CSE 012) and permission of the instructor. Students who are interested in taking the course should send a paragraph of what they'd hope to get from the course to the instructor.
Text: Lynch and Park, "Modern Robotics," Cambridge University Press, 2017.
Click here to download free version.
Simulation tool: Below are two animations made with CopelliaSim:
A simple arm-type robot executing a linear motion in its workspace. As the arm moves, notice that the origin of the coordinate frame attached to the end of the arm moves on a straight line and that the moving coordinate frame does not rotate. Kinematic models and trajector generation techniques are used to design such motions.
A mobile manipulator controlled to move along a straight line. In this video, the two-fingered, parallel-jaw gripper is be moved to achieve a final position and orientation (final pose). The videos show how the robot's base and arm coordinate move the gripper to the final pose. In the top video, the robot begins in an initial pose known exactly. In the bottom video, the robot's initial pose is not known exactly, so the control algorithm uses pose sensors to eliminate the error and achieve the same final pose.
Useful Links: