Course overview
Motion planning addresses the geometric problem of computing collision-free, low cost, less distant & identification of optimal path for a robot or chain of robots to travel from start point to an endpoint. In coordination with finding the optimal path, motion planning also focus on robots velocity, acceleration & more.
If you wonder where does motion planning is practically applicable? Take an example of a floor cleaning robot-like Roomba, how it can plan its path to clean the floor by avoiding static obstacles such as a chair, table, wall or sofa. Or the advanced autonomous robot like Tesla Level 5 Auto Pilot plans the path, speed, steering & more parameters with hundreds of static obstacles such as trees, road barricades, traffic lights, speed limits, lane marking, traffic markings & dynamic obstacles such as moving vehicles and more.
This course on Motion planning algorithms will be taught with the practical cases on wheeled robot navigation. That is, programming the motion planning algorithms to navigate the robot autonomously in a given environment and achieving the robot's motion by making decisions on the path that the robot should follow to avoid obstacles & the velocity at which the robot should move. You will build projects such as floor cleaning robots & wheeled robots to apply the algorithms such as, Djikstra's, A*, RRT, RRT* and control the robot's motion with control algorithms such as PID, P, I, D, PD, PI controllers.
By the end of this course, you will gain a detailed understanding of motion planning algorithms with hands-on projects. You will gain the confidence & knowledge to be self-sufficient in solving more similar problems in academia or industry.
*** This course is taught on the fundamentals which are covered in the below-mentioned courses. One should mandatorily complete the foundation programs from Decibels to enrol in this course on "Motion Planning Algorithms for Robots using ROS"
Week 1
Introduction to Mobile Robot, Motion Planning and Dijkstra's Path Planning Algorithm
Introduction to mobile robot, types of mobile robots (Wheeled, Tracked, Legged, AIr-based & Water-based robot), types of wheeled robots and their locomotion (Single wheeled, two-wheeled, three-wheeled, Four-wheeled & five or more wheeled robots), Types of wheels (Standard/Fixed, Orientable Ball and Omni wheel). Introduction to a sensor and its types (Active and Passive). Introduction to an Odometry sensor need, working, and error propagation. Introduction to Motion Planning and its applications. Introduction to path planning, configuration space, obstacle representation, Graph, cost, and graph search. Introduction grid-based path planning, Dijkstra’s algorithm, and pseudo-code of Dijkstra’s algorithm. Programming Dijkstra’s algorithm to find the shortest path in the maze, visualization of the path in python, and results discussion.
Week 2
Astar Path Planning and Mobile Robot Motion Control in a Maze
Introduction Astar algorithm and pseudo code of Astar algorithm. Programming Astar algorithm to find the shortest path in the maze, visualization of the path in python, and results discussion. Introduction to control systems (Open and closed loop), types of controllers in ROS (effort, joint state, Position, Velocity, and Joint Trajectory Controllers), Inertia, Damping, Stiffness, and Mobile Robot description in rviz & gazebo. Introduction to Mobile Robot motion control to control the robot's velocity to control robot’s translation and rotation. Programming motion control algorithm to move the robot to the desired point and Mobile robot motion visualization by representing the maze in gazebo.
Week 3
Introduction to Sampling Based Path Planning, RRT and RRT* Path Planning Algorithms
Introduction to Sampling-based path planning and comparison between grid-based & Sampling-based path planning. Introduction to rapidly exploring random tree (RRT), rapidly exploring random tree star (RRT*) and pseudo code of RRT & RRT*. Programming RRT & RRT* algorithm by representing the environment and cylindrical obstacles. Analysis of RRT algorithm output based on the tree size, no of nodes, and path resolution. Analysis of RRT* algorithm output based on the tree size, no of nodes, maximum iteration, search radius, and path resolution.
Week 4
Introduction PID Controller and PID based Mobile Robot Motion Control
Introduction to Proportional, Integral, Derivative, PI, PD, and PID controllers. Programming Proportional, Integral, Derivative, PI, PD, and PID controllers for motion control of the mobile robot to visualize the robot's motion based on the environment and obstacles represented in gazebo.
Course notes
Course name |
Autonomous Robot Motion Planning using ROS |
Mode of delivery |
Online (Recorded format) |
Start & end date |
Immediately |
Course duration & access duration |
17+ hours & course access is limited to 120 days |
Software/tools/libraries/ workbenches used |
ROS, Python, Rviz & Gazebo |
Applicable for |
This course is for engineers who would like to align & prepare themselves for the industrial revolution (4.0). The skills you gain in this program are applicable in Robot design, Autonomous systems, Automation, Kinematics, Motion control etc. |
Certification by & Host details |
Decibels Lab Pvt Ltd
(Recognised as Start-up by Department for Promotion of Industry and Internal Trade Ministry of Commerce & Industry Government of India) (Certificate Number: DIPP45372) |
Prerequisites: Exposure, Operating system & hardware |
It is mandatory to complete below programs which are hosted by Decibels
Operating System: Ubuntu 16 ROS version: ROS Kinetic Computer hardware: Intel core i5 or AMD equivalent / 8 GB RAM / 2 GB Graphics card (Suggested) |
Doubt clarification |
Addressed during via Discussions option available at each lesson In our LMS. If the query needs a detailed discussion, we will support you through the zoom meeting + Dedicated support for queries. |
Certification |
Participants will receive a Linkedin shareable digital Course completion certificate. (Verifiable certificate)
*** Students can opt for this course as a 4-week Internship |
Course enrolment
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Motion Planning Algorithms for Robots in ROS
₹4,999.00
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