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Path Planning & Trajectory Optimization Using C++ & ROS in Delhi

A 3 month course which will introduce you to path planning and trajectory optimization techniques which can be implemented in autonomous vehicles

12 weeks long course | 100% Online

Learn from leading experts in the industry

Project based learning with 2 industry level projects that learners can showcase on LinkedIn.

Learn Key Tools & Technologies C++, ROS, Eclipse ADORe

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Syllabus

This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

Course Overview

  • The students will gain a thorough knowledge of Robot Motion Planning.
  • During the coursework, the students will learn the following concepts:
    • Configuration space for motion planning
    • Random sampling-based motion planning 
    • Motion planning with non-holonomic robots
    • Trajectory planning
    • Reinforcement learning for planning 
  • During the coursework, the students will work on the Robot Programming Environment - ROS, Simulation Environment - RVIZ, and C++ Programming.
  • The students are exposed to the modern trends and standard practices being followed in the industry right now.
  • This course forms the foundation for anyone wanting to pursue a career in the domain.

Course Syllabus in Delhi

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:

  • Graph-Based Algorithms
  • Breadth-First Search Algorithm
  • Depth-First Search Algorithm

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:

  • How to Use the Configuration Space?
  • Representing Configuration Space as a Graph
  • Planning using Visibility Graph
  • Finding the Shortest Path.
  • Dijkstra’s Algorithm, A*, Bellman-Ford Algorithm

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

  • Various Types of Rapidly Exploring Random Tree(RRT)
  • Application of RRTs
  • Path Planning using the RRT Algorithm
  • Setting up the Ubuntu Environment

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:

  • Setting up ROS
  • Following Instructions on the ROS Website
  • Adding ROS to the Docker Container
  • Introduction to Cmake
  • Programming using ROS
  • Introduction to 3-D Visualization Tool - Rviz
  • Difference between
    • ROS/RTOS
    • ROS1/ROS2
  • DDS
  • Middleware

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:

  • Path and Speed Planning
  • Trajectory Representations
    • Splines
    • Clothoid
    • Bezier Curves
    • Polynomials
  • Introduction to Frenet Frame
  • Planning in Frenet Frame
  • Boundary Value Constraint Problem and Methods
  • Pointwise Constraint Problem and Methods

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:

  • Collision Detection for Static Obstacles
  • Motion Prediction for Dynamic Obstacles
  • Motion Prediction in Frenet Frame with Kalman Filters
  • Collision Prediction for Dynamic Obstacles

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:

  • Route Planning, A*, D*, D* lite
  • HD Maps, SD Maps
  • Behavior Planning - State Machines, Decision Tree, Behavior Tree, etc.
  • Behavior and Motion Planning Integration

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:

  • Polynomial Planners
  • Motion Planning with Differential Constraints
  • Lattice Planners
  • Collision Checking
  • Trajectory Selection (Cost Functions)

Week 9- Planning Algorithm

The topics for this week include:

  • Vehicle and Tire Model
  • Optimal Control
  • MPC Planners

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:

  • Unstructured Planner: Hybrid A*
  • Parking Planner
  • Automated Driving Open Research (ADORe)

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:

  • Machine Learning
  • Markov Decision Process
  • Policy Evaluation
  • Value iteration
  • Reinforcement Learning
  • On/Off Policy, Model-based/Model-free Monte Carlo
  • Bellman Optimality, SARSA
  • Q-learning, Epsilon Greedy
  • Decision Making for AVs

Week 12- Conclusion

The topics for this week include:

  • Overview of the Topics Learned
  • Paper Review
  • Non-Traditional Applications

Our courses have been designed by industry experts to help students achieve their dream careers

Industry Projects in Delhi

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

A project on the design and implementation of different graph-based trajectory planers in a partially known static environment. Students will design different graph-based algorithm and test their performances in partially known static environment. In partially known static environments only static obstacles are present but the layout of the environment is changing as the agent acquires new information.

Trajectory Planning with Optimization Approach for Autonomous Car in Urban Area

A project on the design and implementation of a motion planner for an autonomous car in a realistic dynamic environment. The motion planner must plan a collision-free trajectory for the vehicle that leads it through a given destination by considering other road users (e.g. other vehicles on the traffic network).

Our courses have been designed by industry experts to help students achieve their dream careers

Ratings & Reviews by Learners

Skill-Lync has received honest feedback from our learners around the globe.

Google Rating
4.6

Learn to Use C++ and ROS for Path Planning and Trajectory Optimization at Skill-Lync in Delhi

This C++ course in Delhi provides you with hands-on training in path planning and trajectory optimization.

The Path Planning and Trajectory Optimization Using C++ and ROS Course in Delhi

This C++ course in Delhi acquaints you with some essential learnings in configuring spaces for motion planning. As the course advances, you learn more about robot operating systems, trajectory planning, and reinforcement learning for planning. You also get practical experience in using different software, including  C++, ROS, and Eclipse ADORe. The course is well-designed to develop your understanding of Robot Motion Planning.

As one of the best online courses, the curriculum includes two industry-level projects to make you job-ready. It is carefully curated by an industry expert with four years of industrial experience adhering to industry standards. With the Skill-Lync C++ course in Delhi, you will get a wholesome learning environment bundled with an intensive curriculum, practical learning sessions, and experienced staff.

FAQs on the Path Planning and Trajectory Optimization Using C++ and ROS Course in Delhi

Why should I choose the Path Planning and Trajectory Optimization Using C++ and ROS course by Skill-Lync in Delhi? 

Skill-Lync is one of the best institutes for C++ in Delhi. Powered by industry-level projects and expert guidance, the course provides practical training in  C++, ROS, and Eclipse ADORe.

What are the prerequisites for taking up the Path Planning and Trajectory Optimization Using C++ and ROS course by Skill-Lync in Delhi?

The course is specially designed for students and graduates fo electrical and related engineering streams.

What is the program fee for Skill-Lync's Path Planning and Trajectory Optimization Using C++ and ROS course in Delhi? 

This C++ training course has three fee plans: Basic, Pro, and Premium. The Basic plan offers 2 months' access at Rs 7000 per month for 3 months. The Pro plan provides 4 months' access at Rs 10,000 per month for 3 months, and the Premium plan offers lifetime access at Rs 15,000 per month for 3 months. 

What are the benefits of choosing the Path Planning and Trajectory Optimization Using C++ and ROS course by Skill-Lync in Delhi?

Pursuing a C++ course at Skill-Lync would offer many benefits to you.

  • Industry-oriented curriculum.
  • Hands-on experience in solving industry projects.
  • Email and forum support from the technical support team to clear your doubts.
  • A certificate of completion for all participants and a merit certificate for the top 5% of the scorers.

What are the career prospects after completing the Path Planning and Trajectory Optimization Using C++ and ROS course by Skill-Lync in Delhi?

Following are some jobs that you can apply for after the course:

  • Robotics System Engineer
  • Robotics Test Engineer
  • Embedded System Software Developer
  • C++ Developer-Automotive

After completing the Path Planning and Trajectory Optimization Using C++ and ROS course by Skill-Lync in Delhi, what is the expected salary range? 

The average annual salary of a robotics and automation engineer in India is close to INR 4.4 LPA. The salary ranges from INR 4.3 LPA to 18 LPA depending on your skills and expertise.

Can you tell me more about Skill-Lync?

Skill-Lync is among India's leading EdTech platforms dedicated to transforming engineering education. We equip young engineers with the latest skill sets and cutting-edge tools in new-age technologies.

The brainchild of two engineers, Skill-Lync, is on a mission to bridge the skill gap between aspiring professionals and the industry's demands through job-oriented courses.

Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.

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1 industry expert

Our instructors are industry experts along with a passion to teach.

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4 years in the experience range

Instructors with 4 years extensive industry experience.

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Areas of expertise

  • RADAR

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