Start by clarifying requirements (warehouse layout, human traffic, safety constraints) and then propose a layered architecture with sensors, perception, planning, and control. Emphasize safety through redundancy and dynamic replanning, and walk through a main control loop that integrates sensing, planning, and actuation.
Pro tip: Highlight the importance of fail-safe behaviors and graceful degradation—e.g., if a sensor fails, the robot should slow down or stop safely. Also, mention how you would validate the system through simulation and real-world testing with edge cases.
Ask about environment size, human density, speed requirements, safety standards, and payload. This shapes sensor and algorithm choices.
Choose a combination of sensors (e.g., LiDAR, depth cameras, ultrasonic) for 360° coverage and redundancy. Describe perception modules for human detection and tracking.
Outline modules: sensor interface, perception, world model (e.g., occupancy grid, costmap), planner, and controller. Explain key data structures like dynamic costmaps and human trajectory predictions.
Use global planner (e.g., A*) for static map and local planner (e.g., DWA, TEB) for dynamic obstacles. Incorporate human motion prediction and social navigation norms.
Present a loop that senses, updates world model, replans if needed, and sends velocity commands, with safety checks and fallback behaviors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.