Package Summary
| Tags | No category tags. |
| Version | 0.4.5 |
| License | Apache-2.0 |
| Build type | AMENT_CMAKE |
| Use | RECOMMENDED |
Repository Summary
| Checkout URI | https://github.com/ros-planning/navigation2.git |
| VCS Type | git |
| VCS Version | foxy-devel |
| Last Updated | 2021-01-01 |
| Dev Status | DEVELOPED |
| CI status | No Continuous Integration |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- Steve Macenski
- Carlos Orduno
Authors
Navfn Planner
The NavfnPlanner is a plugin for the Nav2 Planner server.
It provides the equivalent functionality to a GlobalPlanner in ROS1 MoveBase.
Status
Currently, NavfnPlanner's core algorithm is a direct port from the ROS1 MoveBase Navfn planner. The Navfn planning algorithm is based on the Global Dynamic Window Approach.
Characteristics
In Dijkstra mode (use_astar = false) Dijkstra's search algorithm is guaranteed to find the shortest path under any condition.
In A* mode (use_astar = true) A*'s search algorithm is not guaranteed to find the shortest path, however it uses a heuristic to expand the potential field towards the goal.
The Navfn planner assumes a circular robot and operates on a costmap.
Next Steps
- Implement additional planners based on optimal control, potential field or other graph search algorithms that require transformation of the world model to other representations (topological, tree map, etc.) to confirm sufficient generalization. Issue #225
- Implement planners for non-holonomic robots. Issue #225
Wiki Tutorials
Source Tutorials
Package Dependencies
System Dependencies
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged nav2_navfn_planner at answers.ros.org
Package Summary
| Tags | No category tags. |
| Version | 0.2.6 |
| License | Apache-2.0 |
| Build type | AMENT_CMAKE |
| Use | RECOMMENDED |
Repository Summary
| Checkout URI | https://github.com/ros-planning/navigation2.git |
| VCS Type | git |
| VCS Version | dashing-devel |
| Last Updated | 2020-12-28 |
| Dev Status | DEVELOPED |
| CI status | No Continuous Integration |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- Steve Macenski
- Carlos Orduno
Authors
Navfn Planner
The NavfnPlanner is a planning module that implements the nav2_behavior_tree::ComputePathToPose interface.
A planning module implementing the nav2_behavior_tree::ComputePathToPose interface is responsible for generating a feasible path given start and end robot poses. It provides the equivalent functionality to a GlobalPlanner in ROS1 MoveBase.
Status
Currently, NavfnPlanner's core algorithm is a direct port from the ROS1 MoveBase Navfn planner. The Navfn planning algorithm is based on the Global Dynamic Window Approach.
Characteristics
In Dijkstra mode (use_astar = false) Dijkstra's search algorithm is guaranteed to find the shortest path under any condition.
In A* mode (use_astar = true) A*'s search algorithm is not guaranteed to find the shortest path, however it uses a heuristic to expand the potential field towards the goal.
The Navfn planner assumes a circular robot and operates on a costmap.
Task Interface
The Navigation System is composed of three tasks: NavigateToPose, ComputePathToPose and FollowPathToPose.
The DijkstraPlanner implements the Task Server interface for ComputePathToPose, specifically derives from nav2_behavior_tree::ComputePathToPoseTaskServer. The client to DijkstraPlanner is 'NavigateToPoseTask', which periodically sends requests to the path planner.
Next Steps
- Refactor Navfn. Currently difficult to modify/extend. Issue #244
- Implement additional planners based on optimal control, potential field or other graph search algorithms that require transformation of the world model to other representations (topological, tree map, etc.) to confirm sufficient generalization. Issue #225
- Implement planners for non-holonomic robots. Issue #225
Wiki Tutorials
Source Tutorials
Package Dependencies
System Dependencies
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged nav2_navfn_planner at answers.ros.org
Package Summary
| Tags | No category tags. |
| Version | 0.1.7 |
| License | Apache License 2.0 |
| Build type | AMENT_CMAKE |
| Use | RECOMMENDED |
Repository Summary
| Checkout URI | https://github.com/ros-planning/navigation2.git |
| VCS Type | git |
| VCS Version | crystal-devel |
| Last Updated | 2019-03-13 |
| Dev Status | DEVELOPED |
| CI status | No Continuous Integration |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- Steve Macenski
- Carlos Orduno
Authors
Navfn Planner
The NavfnPlanner is a planning module that implements the nav2_tasks::ComputePathToPose interface.
A planning module implementing the nav2_tasks::ComputePathToPose interface is responsible for generating a feasible path given start and end robot poses. It provides the equivalent functionality to a GlobalPlanner in ROS1 MoveBase.
Status
Currently, NavfnPlanner's core algorithm is a direct port from the ROS1 MoveBase Navfn planner. The Navfn planning algorithm is based on the Global Dynamic Window Approach.
Characteristics
In Dijkstra mode (use_astar = false) Dijkstra's search algorithm is guaranteed to find the shortest path under any condition.
In A* mode (use_astar = true) A*'s search algorithm is not guaranteed to find the shortest path, however it uses a heuristic to expand the potential field towards the goal.
The Navfn planner assumes a circular robot and operates on a costmap.
Task Interface
The Navigation System is composed of three tasks: NavigateToPose, ComputePathToPose and FollowPathToPose.
The DijkstraPlanner implements the Task Server interface for ComputePathToPose, specifically derives from nav2_tasks::ComputePathToPoseTaskServer. The client to DijkstraPlanner is 'NavigateToPoseTask', which periodically sends requests to the path planner.
Next Steps
- Refactor Navfn. Currently difficult to modify/extend. Issue #244
- Implement additional planners based on optimal control, potential field or other graph search algorithms that require transformation of the world model to other representations (topological, tree map, etc.) to confirm sufficient generalization. Issue #225
- Implement planners for non-holonomic robots. Issue #225
Wiki Tutorials
Source Tutorials
Package Dependencies
System Dependencies
Dependant Packages
| Name | Repo | Deps |
|---|---|---|
| navigation2 | github-ros-planning-navigation2 |
Launch files
Messages
Services
Plugins
Recent questions tagged nav2_navfn_planner at answers.ros.org
Package Summary
| Tags | No category tags. |
| Version | 0.3.5 |
| License | Apache-2.0 |
| Build type | AMENT_CMAKE |
| Use | RECOMMENDED |
Repository Summary
| Checkout URI | https://github.com/ros-planning/navigation2.git |
| VCS Type | git |
| VCS Version | eloquent-devel |
| Last Updated | 2021-01-04 |
| Dev Status | DEVELOPED |
| CI status | No Continuous Integration |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- Steve Macenski
- Carlos Orduno
Authors
Navfn Planner
The NavfnPlanner is a plugin for the Nav2 Planner server.
It provides the equivalent functionality to a GlobalPlanner in ROS1 MoveBase.
Status
Currently, NavfnPlanner's core algorithm is a direct port from the ROS1 MoveBase Navfn planner. The Navfn planning algorithm is based on the Global Dynamic Window Approach.
Characteristics
In Dijkstra mode (use_astar = false) Dijkstra's search algorithm is guaranteed to find the shortest path under any condition.
In A* mode (use_astar = true) A*'s search algorithm is not guaranteed to find the shortest path, however it uses a heuristic to expand the potential field towards the goal.
The Navfn planner assumes a circular robot and operates on a costmap.
Next Steps
- Implement additional planners based on optimal control, potential field or other graph search algorithms that require transformation of the world model to other representations (topological, tree map, etc.) to confirm sufficient generalization. Issue #225
- Implement planners for non-holonomic robots. Issue #225