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This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects
On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.
Week 1- Introduction
Robots are programmable machines that influence every aspect of a human's work and have a high potential to replace humans from performing a range of tasks. For example, it is becoming possible for computers to assist our daily driving. The topics include:
Week 2- Configuring Space for Motion Planning
In this week, the students will learn about C-Space i.e., Configuration Space. C-space is the space that provides possible positions for the robot to move. The topics include:
Week 3- Random Sampling-Based Motion Planning
In this week, the students will learn about sampling-based motion planning. This will solve the navigation queries. Instead of depending on the entire map of the C-space, the robot depends on the procedures that decide if the robot’s configuration is approaching an obstacle or not. The topics include
Week 4- Robot Operating System
In this week, the students will learn about ROS. ROS is a robotics middleware that manages the complexity and heterogeneity of the hardware and applications. Also, it performs low-level device control, implementation of commonly-used functionality, message-passing between processes, and package management. The topics include:
Week 5- Motion Planning with Non-Holonomic Robots
In this week, the students will learn about motion planning with non-holonomic robots. Non-Holonomic robots are built in such a way that they only travel in one direction along a given axis. To put it in simple words, Non-Holonomic robots can only move forward, backward, or sideways. The topics for this week include:
Week 6- Mobile Robot Collision Detection
In this week, the students will learn about Mobile Robot collision detection. The robot will detect a collision and will change its trajectory to escape the contact as fast as possible and move away safely. The topics for this week include:
Week 7- Hierarchical Planning for Autonomous Robots
In this week, the students will learn about Hierarchical Planning for Autonomous Robots. Hierarchical planning optimizes the global path and it requires only a considerable amount of time for the path replanning operations. The topics for this week include:
Week 8- Trajectory Planning
In this week, the students will learn about Trajectory Planning. Trajectory planning plays a major role in robotics and paves way for autonomous vehicles. It is basically the movement of robots from point A to point B by avoiding obstacles over time. The topics for this week include:
Week 9- Planning Algorithm
The topics for this week include:
Week 10- Planning in Unstructured Environments
In this week, the students will learn about planning in unstructured environments. Unstructured environments include off-roads, parking lots, etc. In such an environment, the robots should be able to identify the optimal path between the start and the goal path. So, for the robots to perform this, a suitable path planning algorithm is required. The topics for this week include:
Week 11- Reinforcement Learning for Planning
In this week the students will learn about Reinforcement Learning for Planning. Basically, it is a machine learning method that has increased applications in robot path planning. The robot will explore its surrounding environment and learn using the trial and error process. The machine learning method has an advantage in path planning and requires less prior information. The topics for this week include:
Week 12- Conclusion
The topics for this week include:
Our courses have been designed by industry experts to help students achieve their dream careers
Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.
Design Implementation and Comparison of the Graph based Trajectory Planners
Trajectory Planning with Optimization Approach for Autonomous Car in Urban Area
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