What's the most reliable path planning stack for obstacle avoidance in mobile robots?

There isn’t a single universally “most reliable” stack, but for mobile robot obstacle avoidance the most dependable real-world choice is usually:

Best practical answer

ROS 2 + Nav2 (Navigation2) + a good local planner + robust costmaps + LiDAR-based perception

That combination is the most widely used open-source stack for autonomous mobile robots because it’s:

• actively maintained,
• modular,
• field-tested,
• compatible with many robot bases and sensors,
• and has mature obstacle avoidance behaviors.

Typical reliable setup

A strong production-oriented stack looks like this:

• Framework: ROS 2
• Navigation: Nav2
• Global planning: A* / Smac Planner / Theta* variants
• Local planning / obstacle avoidance:
  • DWB for simpler, well-understood motion,
  • TEB for smoother trajectories,
  • MPC-based controllers if you need tighter dynamic handling,
• Obstacle representation: 2D costmaps or voxel maps
• Sensors: 2D LiDAR plus wheel odometry; 3D LiDAR or depth camera if the environment is more complex
• Localization: AMCL or SLAM toolbox / lidar SLAM, depending on whether the map is known

Why Nav2 is usually the go-to

Nav2 is reliable because it separates responsibilities:

• mapping/localization,
• global path planning,
• local obstacle avoidance,
• recovery behaviors.

That separation makes failures easier to diagnose and tune.

If you want the “safest” obstacle avoidance behavior

For many indoor mobile robots, this is a strong combo:

• 2D LiDAR
• Nav2 costmaps
• DWB controller
• careful inflation layer tuning
• collision checker + recovery behaviors

This is often more predictable than more aggressive planners.

If your environment is dynamic or cluttered

Consider:

• TEB or MPC local control
• dynamic obstacle tracking
• 3D sensing
• possibly a behavior tree that can replan aggressively

What I’d recommend by use case

• Indoor AGV / warehouse robot: ROS 2 + Nav2 + LiDAR + DWB
• Need smoother motion around people/objects: Nav2 + TEB or MPC
• Outdoor / rough terrain: usually a different stack, often involving autonomy frameworks + terrain-aware planning, not just standard Nav2
• High safety-critical deployments: use a certified safety controller, not just the navigation stack alone

Bottom line

If you mean open-source and widely proven, the most reliable general-purpose stack is:

ROS 2 + Nav2 + LiDAR-based costmaps + DWB or TEB local planner

If you want, I can also give you:

1. a best stack by robot type (AGV, AMR, drone, outdoor rover), or
2. a side-by-side comparison of Nav2 vs Move Base vs proprietary options.