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Path Planning & Trajectory Optimization Using C++ & ROS

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

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

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

An integrated course in the electrical domain, path planning and trajectory optimization using C++ and ROS develops contemporary technology skills.

The present-day C++ courses utilize technological advances and knowledge for path planning and trajectory optimization. As a student, you can learn how programming languages can be used to work on these areas and produce solutions that can be utilized in the field of robotics and automobiles.

This course will help you design autonomous solutions such as robots. Charged with various exciting topics and projects, it is spread across three months, and once completed, the knowledge gained will help you generate outcomes that will be immensely useful in the field of electronics.

The course begins with some essential learnings in configuring spaces for motion planning but soon advances to robot operating systems and trajectory planning and reinforcement learning for planning. To get hands-on experience with these concepts, you will also work on two exclusive projects which will sharpen your understanding from a practical perspective and help you gain knowledge about the software solutions that can be used to program an outcome for path planning and trajectory optimization.

Who Should Take This Course?

It is recommended that the following category of students undertake the C++ online course:

  • If you are a graduate with a Bachelor’s degree in ECE, mechanical engineering, or EEE.
  • If you are someone who is desirous of gaining deep knowledge from a practical as well as a theoretical standpoint concerning robotic motion planning and are ambitious about establishing a career in this field.
  • If you are well-versed with the basic knowledge of Python and programming with C++ in conjunction with some basic mathematical understanding of relevant concepts.

The course will prove to be a boon to your learning needs if you are passionate about the dynamics of motion planning and trajectory optimization. 

What Will You Learn?

As one of the best online courses for C programming, you stand to learn various skills after completing the program. Not only will you be able to program efficiently with the help of C++, but you will also be able to gain knowledge of various algorithms and Python. Furthermore, you will be able to tap the potential of machine learning with robotic skills to design autonomous vehicles. The course will allow you to use a graph-based approach towards algorithms and search for them, besides the use of configuration space for motion planning.

During the course, you will also gain knowledge of the types of Rapidly exploring Random Tree and their applications in this domain and programs using robotic operating systems. The course will introduce you to many terms and concepts pertaining to this field, which will earmark your understanding and growth in this domain.

Skills You Will Gain

The C++ online certification course is designed to offer skilful learning and understanding, highlighted in the points below:

  • Detection of collision and prediction of motion
  • Planning on hierarchical lines while making autonomous robots
  • The different steps involved in trajectory planning
  • Algorithms that help in path planning
  • The typical machine learning methods which are used in robotic path planning

Key Highlights of the Program

  • The course content is spread across three months for intensive learning and training.
  • You will receive a certificate at the end of the course, and students securing the top 5% ranks will also get a merit certificate.
  • This course prepares you to meet the challenging needs of the evolving environment in the technology domain.
  • With this course, you can start your career in motion planning and robotic operating systems.

Career Opportunities after Taking the Course

Once you complete the course, tremendous job opportunities are waiting for you. As someone with knowledge in path planning and trajectory optimization, you can find employment in manufacturing plants, medical establishments, laboratories, mining, automation, aerospace engineering, life science, agricultural engineering, and much more.

In addition, there is a good demand for people in this field in the gaming arena and various types of manufacturing units. Working in this field, you can find placement in various job profiles, including robotics test engineer, robotics system engineer, robotics technicians, aerospace robotics engineer, quality assurance technician, and more. Many companies in and outside India will be willing to hire candidates emerging in this field.

FAQ’s on Path Planning & Trajectory Optimization Using C++ & ROS

Q: Can anyone join this course?

A: The ideal candidates for this course are those who understand C++ or Python and some basic mathematical concepts. Candidates hailing from such backgrounds can easily make it in this line.

Q: Will I be able to get an e-verification after completing the course?

A: Yes, students will get an e-verified profile after completing the course, which they can share on relevant websites such as LinkedIn.

Q: Will I get any support if I have doubts?

A: Yes, students can approach a skilled expert through email support, group video support, forum support, and telephone support to get their doubts cleared.

Q: What will be the mode of delivery of the course?

A: The course will be delivered to the students through online mode.

Q: Is there any use in doing this course if I want to get a job afterwards?

A: The course introduces various software and tools useful in tackling real-life situations, including making autonomous cars, robots, and more.

Q: Which software is taught during the course?

A: Among the few software taught to you during the course are C++, ROS, Eclipse ADORe, and more.

Q: Will I get to work on any projects during the course?

A: Yes, you will work on two design and implementation courses while undergoing the program.

Q: Can students from a non-electrical background start this course?

A: The course is highly focused and intended for you if you are a graduate in mechanical engineering or hold a Bachelor’s degree in ECE, EEE, and more.

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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