Xingzhi Qian

Course Portfolio Overview

This page summarizes my 12-lab project series in Cornell ECE5160 Fast Robots. Across the course, I developed and tested an embedded robotic system that integrates sensing, wireless communication, motor control, state estimation, mapping, localization, path planning, and autonomous navigation on a real mobile robot platform.

The course is structured as a progressive robotics development sequence. Early labs focus on board setup, Bluetooth communication, IMU and ToF sensing, and motor characterization. Later labs build toward closed-loop control, Kalman-filter-based state estimation, mapping, Bayes-filter-based localization, and final path planning and execution.

Technical Development Flow

Technical Highlights

• Integrated IMU, ToF sensing, motor control, BLE communication, onboard data logging, and offline Python analysis on an Artemis Nano-based robot.
• Implemented PID-based distance and yaw control with gyro-bias calibration, derivative damping, and anti-windup protection.
• Built Kalman-filter-based state estimation from noisy ToF measurements and robot dynamics identified through step-response experiments.
• Performed yaw-controlled 360-degree mapping scans and Bayes-filter-based grid localization.
• Integrated mapping, localization, path planning, and state-machine-based motion control into final autonomous navigation experiments.

Acknowledgements

I would like to thank Prof. E. Farrell Helbling and the TA team for their guidance throughout the course. Through this course, I gained hands-on experience in embedded robotics, sensing, state estimation, control, localization, and autonomous navigation.

Labs

• Lab 1: The Artemis Board and Bluetooth Board setup, BLE communication, and basic embedded system interaction.
• Lab 2: IMU IMU data acquisition, calibration, and orientation sensing.
• Lab 3: ToF Time-of-Flight distance sensing and sensor data characterization.
• Lab 4: Motors and Open-Loop Control Motor driver integration, PWM control, and open-loop motion experiments.
• Lab 5: Linear PID Control and Linear Interpolation Closed-loop distance control and interpolation-based sensing/control updates.
• Lab 6: Orientation Control Yaw estimation, gyro calibration, and closed-loop orientation control.
• Lab 7: Kalman Filter State estimation from noisy ToF measurements and robot motion dynamics.
• Lab 8: Stunts! High-speed control, stunt execution, state-machine logic, and onboard logging.
• Lab 9: Mapping Yaw-controlled 360-degree scanning and environment map generation.
• Lab 10: Grid Localization Using Bayes Filter Probabilistic localization with Bayes filtering and grid-based belief updates.
• Lab 11: Localization on the Real Robot Real-robot localization using sensor observations and probabilistic inference.
• Lab 12: Path Planning and Execution Final integration of localization, path planning, motion execution, and analysis.