Autonomous Mobile Robots Training

Live Online & Classroom Enterprise Training

Basic concepts and algorithms for locomotion, perception, and intelligent navigation.

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What is Autonomous Mobile Robots Course about?

This course aims to offer the fundamental principles and algorithms needed to create mobile robots that can behave autonomously in complicated contexts. Mobile robot locomotion and kinematics, environment sensing, probabilistic map-based localisation and mapping, and motion planning are highlighted.

What are the objectives of Autonomous Mobile Robots Course ?

How to import data sets, clean and prepare data for analysis, summarise data, and build data pipelines
Be able to describe the basic concepts and algorithms required for mobile robot locomotion, environment perception, probabilistic map-based localisation and mapping, and motion planning
Be able to apply these concepts for the design and implementation of autonomous mobile robots acting in the complex environment

Who is Autonomous Mobile Robots Course for?

Service engineers of Autonomous Mobile Robots (security and inspection robots)
Installers of Autonomous Mobile Robots (security and inspection robots)
Operators of large groups of security robots
Security system designers

What are the prerequisites for Autonomous Mobile Robots Course?

Good basic mathematics
Physics
system modelling and control.

Available Training Modes

Live Online Training

5 Days

Self-Paced Training

50 Hours

Course Outline Expand All

Expand All

Module 1 - Introduction and Motivation

Overview of mobile robots and their applications in various industries.

Core concepts of autonomy and mobility in robots.

Historical development and future trends in mobile robotics

Module 2 - Locomotion

Types of Locomotion

Wheeled Locomotion

Legged Locomotion

Mechanisms and Actuation

Overview of actuators (electric, hydraulic, and pneumatic).

Design considerations for efficient locomotion.

Module 3 - Mobile Robot Kinematics

Kinematic Models of Mobile Robots

Wheel-Terrain Interaction

Holonomic and Non-Holonomic Constraints

Motion Control

Case studies of kinematic models in real-world robots

Module 4 - Perception I

Introduction to Robot Perception

Proximity Sensors

Data Acquisition

Visual Sensors

Data Acquisition

Module 5 - Perception II

Laser Rangefinders and LiDAR

IMU (Inertial Measurement Units)

Sensor Fusion

Mapping the Environment

Module 6 - Perception III

Advanced Perception with AI

Point Cloud Processing

Semantic Understanding

Real-Time Perception

Module 7 - Localization I

Probabilistic Robotics

Bayesian Filters

Global vs. Local Localization

Landmark-Based Localization

Module 8 - Localization II

Advanced Localization Techniques

Monte Carlo Localization (MCL)

Localization in Dynamic Environments

Sensor Fusion in Localization

Case studies of localization systems in commercial autonomous robots

Module 9 - SLAM I

Mathematical Foundations

Sensor-Based SLAM

Feature-Based SLAM

Module 10 - SLAM II

Graph-Based SLAM

Grid-Based SLAM

Visual SLAM

Real-Time SLAM

Emerging Trends

Module 11 - Planning I

Graph-Based Path Planning

Sampling-Based Path Planning

Path Smoothing and Optimization

Module 11 - Planning II

Dynamic Path Planning

Multi-Robot Path Planning

Behavior-Based Planning

Real-Time Path Planning

Path Optimization

Applications and Tools

Who is the instructor for this training?

The trainer for this Autonomous Mobile Robots Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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