NavFn Planner + DWB Controller

This experiment evaluates the robot’s navigation capabilities using various combinations of global planners and local controllers from the Nav2 stack. Each combination was tested under three distinct scenarios:

1. Straight-Line Movement

2. Navigating Static Obstacles

3. Navigating Dynamic Obstacles

This configuration employs the NavFn Planner for global path planning and the DWB Local Planner for local trajectory adjustments.

Configuration Details

Component	Plugin/Server	Type	Description
Planner Server	nav2_navfn_planner/NavfnPlanner	Global Planner	Computes the shortest path from start to goal using Dijkstra’s algorithm on a costmap.
Controller Server	dwb_core::DWBLocalPlanner	Local Controller	Evaluates possible trajectories and selects the one that optimally balances progress, speed, and obstacle avoidance.

Observations and Results

1. Straight-Line Movement - The robot adhered closely to the planned trajectory with minimal drift. - Smooth motion was achieved by tuning parameters such as max_velocity and yaw_goal_tolerance.

   

   Note

   The scene is speed-forwarded and does not reflect the true speed (0.26 m/s).

2. Static Obstacles - The robot slowed down at the junction and adjusted its speed. - Trajectory adjustments were made by the robot, and it remained on the global path. - Minor path deviations were corrected by the local controller.

   

3. Dynamic Obstacles - The robot successfully responded to a moving cube as a placeholder for a moving person but exhibited slight delays when encountering faster objects. - The robot did not collide with the moving cube. - The robot did not maintain a safe distance, likely due to suboptimal tuning of parameters such as inflation_radius, PathDist.scale, or obstacle_max_range in the local and global costmaps.

   

Performance Summary

Performance Summary

Scenario	Performance
Straight-Line Movement	Smooth and precise navigation.
Static Obstacles	Reliable obstacle avoidance with minor deviations.
Dynamic Obstacles	Adequate responsiveness to slow-moving obstacles; improvement needed for fast-moving objects and maintaining a safe distance.

Future Considerations

• The TEB Local Planner could be explored for enhanced handling of dynamic obstacles.

• The Theta* Global Planner may be utilized for more direct and efficient path generation.