The Marathon 2: A Navigation System

By Steve Macenski, Francisco Martín, Ruffin White, Jonatan Ginés Clavero. IEEE/RJS International Conference on Intelligent RObots and Systems, 2020

432 citations recorded by the source corpus.

Developments in mobile robot navigation have enabled robots to operate in warehouses, retail stores, and on sidewalks around pedestrians. Various navigation solutions have been proposed, though few as widely adopted as ROS (Robot Operating System) Navigation. 10 years on, it is still one of the most popular navigation solutions1. Yet, ROS Navigation has failed to keep up with modern trends. We propose the new navigation solution, Navigation2, which builds on the successful legacy of ROS Navigation. Navigation2 uses a behavior tree for navigator task orchestration and employs new methods designed for dynamic environments applicable to a wider variety of modern sensors. It is built on top of ROS2, a secure message passing framework suitable for safety critical applications and program lifecycle management. We present experiments in a campus setting utilizing Navigation2 to operate safely alongside students over a marathon as an extension of the experiment proposed in Eppstein et al. [1]. The Navigation2 system is freely available at https://github.com/ros-planning/navigation2 with a rich community and instructions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/334f8115db21bcb4232b4ed39bf0347663f5b8d3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. William Woodall
  2. Steve Macenski
  3. Chris Lalancette
  4. Christian Smith
  5. Petter Ögren
  6. Brian P. Gerkey
  7. Tully Foote

In Computer Vision and Pattern Recognition

  1. Steve Macenski
  2. Petter Ögren
  3. Brian P. Gerkey
  4. Tully Foote
  5. Khac Duc Do
  6. Francesco Bullo

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