How do I install teb_local_planner in ROS Noetic?

Use the rosdep command from the README, but check the ROS wiki for updated tutorials as compatibility may vary; it's primarily tested with Melodic.

teb_local_planner vs DWA local planner: which is better for dynamic environments?

teb_local_planner excels with optimization-based trajectory planning for kinodynamic constraints, while DWA is simpler but may struggle with complex obstacles and robot dynamics.

Can teb_local_planner handle multi-robot navigation?

No, it's designed for single-robot scenarios; multi-robot coordination requires additional layers as it lacks built-in support for agent interactions.

What are the key parameters to tune for obstacle avoidance?

Focus on parameters like obstacle weight, optimization horizons, and velocity limits; the ROS wiki provides guidance, but expect iterative testing for your specific setup.

Does teb_local_planner support 3D navigation or only 2D?

It integrates with ROS costmaps for obstacle representation, which are typically 2D, so it's not suited for native 3D path planning without extensions.

How to cite teb_local_planner in academic papers?

Cite one of the listed publications from the README, such as the 2017 Robotics and Autonomous Systems paper, to acknowledge the underlying research and development.

teb_local_planner

BSD-3-ClauseC++0.1.3

An optimal trajectory planner for mobile robots using Timed Elastic Bands to optimize paths considering time, obstacles, and kinodynamic constraints.

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What is teb_local_planner?

teb_local_planner is a ROS package that implements a local trajectory planner for mobile robots using the Timed Elastic Band method. It optimizes robot paths in real-time considering execution time, obstacle avoidance, and kinodynamic constraints like velocity and acceleration limits. The planner serves as a plugin to ROS's standard navigation stack, providing improved performance over traditional approaches.

Target Audience

Robotics researchers and developers working on autonomous mobile robot navigation, particularly those using ROS and needing real-time trajectory optimization with dynamic constraints.

Value Proposition

Developers choose teb_local_planner for its optimal trajectory planning that respects both environmental obstacles and robot dynamics, its support for distinctive topologies enabling better path choices, and its proven performance in research and real-world applications with extensive academic validation.

Overview

An optimal trajectory planner considering distinctive topologies for mobile robots based on Timed-Elastic-Bands (ROS Package)

Use Cases

Best For

Autonomous mobile robot navigation in cluttered environments

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1.3k stars594 forks0 contributors

Real-time trajectory optimization with dynamic constraints

Car-like robot motion planning with Ackermann steering

Research projects requiring optimal control for robot trajectories

ROS-based navigation stacks needing improved local planning

Robotics applications requiring obstacle avoidance with kinodynamic compliance

Not Ideal For

Projects not using ROS or its standard 2D navigation stack
Static, obstacle-free environments where basic geometric planners suffice
Applications with tight real-time constraints on low-power embedded systems
Teams needing out-of-the-box navigation with minimal parameter tuning

Pros & Cons

Pros

Real-Time Trajectory Optimization

Implements Timed Elastic Band to continuously optimize paths at runtime, balancing execution time and obstacle separation as highlighted in the README.

Kinodynamic Constraint Compliance

Respects velocity, acceleration, and other dynamic limits, ensuring physically feasible motions for various robot types.

Distinctive Topology Support

Plans and optimizes trajectories considering multiple path topologies, improving navigation in complex, cluttered environments.

Car-Like Robot Specialization

Offers specialized optimization for non-holonomic vehicles with Ackermann steering, a key feature noted in the documentation.

Cons

Complex Dependency Management

Relies on multiple third-party libraries like Eigen, g2o, and Boost with mixed licenses, complicating deployment and license compliance.

Parameter Tuning Overhead

Requires extensive configuration and tuning of numerous parameters for optimal performance, which can be time-consuming and error-prone.

ROS Version Fragility

Build status focused on melodic-devel indicates potential compatibility issues with newer ROS distributions, limiting long-term support.

Frequently Asked Questions

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#mobile-robots

#mobile-robotics

#motion-planning

#robot-operating-system

#autonomous-navigation

#path-planning

#ros

#trajectory-optimization