TEB Local Planner – Navigation

Master Robot Navigation: A Comprehensive Guide to the TEB Local Planner

Have you ever watched a robot glide through a cluttered room, effortlessly avoiding furniture and people, and wondered how it achieves such fluid motion? The secret often lies in a sophisticated algorithm working behind the scenes. This 4-month (16-week) self-study course is your deep dive into one of the most powerful and widely used of these algorithms: the Timed Elastic Band (TEB) Local Planner.

Designed for motivated beginners and intermediate learners, this comprehensive program will guide you from the foundational principles to advanced applications of the TEB Local Planner. This algorithm is a cornerstone of modern robotics, enabling automated systems to perform local trajectory optimization with remarkable efficiency. It empowers robots to navigate complex, dynamic environments smoothly by calculating the optimal short-term path. Throughout this course, we will explore the theoretical underpinnings of TEB, master its implementation in ROS/ROS2, learn the art of parameter tuning for peak performance, and apply our knowledge through practical, hands-on examples that culminate in a final capstone project. By the end, you won’t just understand TEB; you will have the confidence and skill to implement the TEB Local Planner in your own robotics projects.

Primary Learning Objectives

Upon successful completion of this course, you will be able to:

Master the core concepts of local planning and articulate the unique role of the TEB Local Planner within the ROS navigation stack.
Decode the mathematical foundations of the Timed Elastic Band formulation, including cost functions and graph-based optimization.
Implement, configure, and launch the TEB Local Planner within a ROS or ROS2 environment from scratch.
Skillfully tune TEB parameters to optimize performance for various robotic platforms and challenging navigation scenarios.
Confidently debug and troubleshoot common issues encountered when deploying the TEB Local Planner.
Integrate TEB into a complete robotic navigation system for robust, real-world applications.
Design, develop, and execute a final project that showcases your proficiency in TEB-based robot navigation.

A computer with Ubuntu 20.04 (or later) installed (virtual machine or dual boot).
ROS Noetic or ROS2 Foxy/Humble installed and configured.
Gazebo simulator installed for virtual testing environments.
A foundational understanding of the Linux command line.
Familiarity with C++ or Python programming (intermediate level is recommended).
* Optional: A physical robot (e.g., TurtleBot3, LIMO) for real-world testing. All course examples can be completed entirely in simulation.

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Course Content Breakdown

Weeks 1-2: Foundations of Local Planning and Your Introduction to TEB

Lesson 1: Introduction to Robot Navigation
We begin by painting the big picture. Robot navigation isn’t a single task but a symphony of systems working together. We’ll dissect the navigation stack, focusing on the critical difference between a global planner (like a GPS route for a long road trip) and a local planner (like your immediate steering and braking to avoid a pothole). You’ll learn the challenges local planners face, such as handling unseen dynamic obstacles and adhering to the robot’s physical limitations.

Lesson 2: Overview of the TEB Local Planner
Here, you’ll meet the star of the show. We introduce the core concept behind the TEB Local Planner: treating the robot’s potential path as a flexible, “elastic band” that is continuously optimized. This metaphor is key to understanding its power. The band stretches towards the goal while being repelled by obstacles, all while considering the passage of time. This unique approach allows for incredibly smooth, human-like trajectories that other planners struggle to replicate. In a hands-on example, you’ll run a pre-configured TEB stack in Gazebo and witness this elegant motion firsthand.

Weeks 3-4: Demystifying the Mathematics of the TEB Local Planner

Lesson 3: Trajectory Representation and Discretization
To truly master TEB, we must peek under the hood. In this lesson, we explore how a smooth, continuous path is represented in a way a computer can understand: as a sequence of discrete poses (points) in space and time. This process, called discretization, turns the elastic band into a series of connect-the-dots points. The time interval between these points is crucial, as it dictates the robot’s velocity and acceleration, making the trajectory physically realistic.

Lesson 4: The Scoring System: Cost Functions
How does TEB decide which path is best? Through cost functions. Think of it as a scoring system where a lower score is better. A trajectory that gets too close to a wall receives a high penalty score. A path that deviates too far from the global plan also gets penalized. We’ll delve into the mathematics of how these costs, especially for obstacle avoidance and path following, are formulated to guide the optimization process toward the safest and most efficient trajectory.

Weeks 5-6: Adhering to Reality with Kinematics and Dynamics

Lesson 5: Kinematic and Dynamic Constraints
A planned trajectory is useless if the robot can’t physically execute it. This is where kinematic and dynamic constraints come in. A car-like robot, for instance, has non-holonomic constraints—it can’t simply move sideways. Similarly, every robot has limits on its maximum velocity and acceleration. TEB cleverly incorporates these real-world limitations into its cost functions, ensuring every generated plan is not just optimal, but also achievable. You’ll experiment by changing these parameters in simulation and observing how the robot’s behavior becomes more or less aggressive.

Lesson 6: Advanced Cost Functions and the Optimization Problem
Building on our knowledge, we explore other important costs, such as maintaining a minimum turning radius or optimizing for the shortest path time. We’ll then tie everything together to understand how TEB formulates the entire setup as a complex graph-based optimization problem, which it then solves in real-time to generate commands for the robot’s motors.

Your Journey to Becoming a TEB Local Planner Expert

This course is more than a series of lectures; it’s a structured journey toward mastery. By progressing through these modules, you will build a robust and intuitive understanding of one of robotics’ most essential algorithms. You’ll move from theoretical knowledge to practical application, gaining the skills to configure, tune, and deploy the TEB Local Planner on any robotic platform. Upon completion, you will be equipped to build navigation systems that are not just functional, but truly intelligent, smooth, and efficient.