The final mission. You have all the pieces—Perception, SLAM, Planning, and Control. Now, you must integrate them into a single, cohesive, intelligent system.
1Bridging the Gap
Your capstone project requires the integration of four distinct modules. Mapping (using a SLAM algorithm like Cartographer), Localization (using AMCL or the SLAM output), Global Planning (using A* or Dijkstra), and Local Planning (using DWA or TEB). The challenge is ensuring these modules communicate through a shared Coordination Layer, usually managed by the ROS Navigation Stack (nav2).
2The Art of Recovery
In the real world, things go wrong. A hallway might be too narrow, or a sensor might glitch. Your system must include Recovery Behaviors. When the planner cannot find a valid path, the robot shouldn't just quit. It should execute a series of tiered responses: first, clear the local costmap; second, rotate in place to find a new perspective; and third, perform a safe back-up maneuver. This resilience is what separates a toy from a professional autonomous machine.
3Mission Execution
The final test is the Autonomous Mission. You will provide the robot with a single 'Goal Pose' (a coordinate and orientation) on a blank map. The robot must move out, discover obstacles, update its map in real-time, and navigate safely to the target. Success is defined by the robot's ability to reach the goal within a specific time limit and without any 'Collisions' or 'Manual Interventions'. This is the benchmark for modern robotics engineering.
