SMAC Planner + MPPI Controller

Navigation Performance: Testing Smac Planner + MPPI Controller

The combination of the Smac Planner and MPPI Controller represents an advanced setup designed to handle complex navigation scenarios. While the Smac Planner excels at generating smooth, kinematic-aware paths, the MPPI Controller provides real-time trajectory optimization and dynamic obstacle handling.

Testing Scenarios and Observations

1. Straight-Line Movement - The robot performed satisfactorily, following a smooth and direct path. - No significant deviations or delays were observed.

   

2. Navigating Static Obstacles - The Smac Planner generated a longer path to avoid obstacles. - As the robot approached the goal, it took additional time to stabilize. - The robot struggled slightly when passing through narrow gaps between walls, experiencing delays due to repeated replanning.

   

3. Navigating Dynamic Obstacles - The robot detected a moving wheelchair but was less robust in avoiding it compared to the NavFn + MPPI combination. - Multiple replanning attempts were necessary to successfully navigate around the obstacle. - Despite these challenges, the robot ultimately reached the goal.

   

Performance Summary

Performance Summary

Scenario	Performance
Straight-Line Movement	Smooth and efficient navigation.
Static Obstacles	Planned longer paths; delays in narrow gaps and stabilizing near the goal.
Dynamic Obstacles	Detected moving objects but struggled with robustness; required multiple replans.

Conclusion

The combination of Smac Planner and MPPI Controller demonstrates strong potential for complex environments, particularly in scenarios requiring kinematic awareness and smooth trajectory optimization. However, challenges remain:

• Static Obstacles: Path planning and stabilization need improvement for close proximities.

• Dynamic Obstacles: Responsiveness to fast-moving objects requires further tuning.

Note

Future Improvements - Optimize Smac Planner parameters for shorter paths and quicker stabilization. - Fine-tune MPPI cost weights for better responsiveness to dynamic obstacles.