Start by clarifying requirements and constraints (e.g., robot type, environment, safety guarantees) to show you can handle ambiguity. Then propose a layered architecture with sensing, perception, planning, and control, and discuss trade-offs between different approaches. Finally, walk through a concrete example or pseudocode to demonstrate feasibility.
Pro tip: Emphasize safety-critical design: include redundancy, fail-safes, and real-time constraints, and discuss how you would validate the system through simulation and testing. This shows maturity beyond just algorithmic details.
Ask questions to understand the robot's form factor, operating environment, human detection range, safety standards, and performance constraints (e.g., latency, power).
Outline the main components: sensors (e.g., LiDAR, cameras, ultrasonic), perception (detection, tracking, prediction), planning (path planning, obstacle avoidance), and control (actuation).
Describe specific techniques for each component, such as YOLO for human detection, Kalman filters for tracking, and potential fields or RRT* for path planning. Mention trade-offs (e.g., accuracy vs. speed).
Explain fail-safes: emergency stop, redundant sensors, watchdog timers, and graceful degradation. Discuss how to ensure real-time performance and safety certification.
Propose simulation (e.g., Gazebo, CARLA) and real-world testing, including edge cases (crowded spaces, occlusions). Mention metrics for success (e.g., detection rate, false positives).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.