There has been a recent explosion of interest in autonomous robots, ranging from Google and Uber’s self-driving cars to Amazon’s delivery drones. Planning the motions of these robots so they satisfy physical constraints and do not collide with obstacles is a challenge.
This course is an introduction to algorithmic techniques for robot motion planning. Topics will include configuration space representations, road map methods, cell decomposition methods, collision detection, sampling-based path planning, non-holonomic motion planning, and multiple robot coordination.
We will motivate these techniques by applications of motion planning to mobile robots and robot manipulators, assembly and manipulation planning, computer aided design, and computer games.
Objectives of Course:
The primary objectives of the course are to introduce students to fundamental mathematical concepts and algorithmic approaches for robot motion planning. Through written home work and programming assignments, students will learn to implement basic motion planners for autonomous robots and will be exposed to open-source motion planning software.
- You are excited about developing motion planners for robotics and automation.
- You are a senior undergraduate student, postgraduate student or faculty member in
engineering and computer science.
- You are engineers and researchers from industry, government organizations, and R&D
laboratories working on robotics
For One-time Registration into the GIAN Portal click here.
Maximum number of participants: 70
- Student Participants: Rs. 1000
- Faculty Participants: Rs. 4000
- Government Research Organization Participants: Rs. 6000
- Industry Participants: Rs. 8000
The above fee is towards participation in the course, the course material, computer use for
tutorials and assignments, and laboratory equipment usage charges.
The participants may be provided with hostel accommodation, depending on the availability, on payment basis. Click here to know more.
Sourav Rakshit, Course co-ordinator
Phone: (044) 2257 4693