Autonomous Rescue Vehicle
National Second Prize winner. Features YOLOv11 object detection and FSM control.
As the team leader, I led our group to win the National Second Prize in the China Undergraduate Engineering Practice and Innovation Ability Competition. We developed an autonomous vehicle capable of real-time target recognition and precise retrieval in complex environments.
Project Image: Autonomous Rescue Vehicle
Competition & Team
Achievement: The team at the award ceremony, winning the National Second Prize and Provincial First Prize.
System Testing & Preparation
Due to strict competition regulations, recording was restricted during the official match. The following video demonstrates our system’s autonomous logic during the intensive testing and preparation phase.
Preparation Phase: Testing the YOLOv11 multi-class recognition and the FSM-based autonomous retrieval logic in a simulated environment.
Technical Key Points
- Vision: Trained a custom YOLOv11 model to achieve robust real-time recognition under highly variable lighting conditions.
- Control: Orchestrated a complex Finite State Machine (FSM) to handle target alignment, servo-based grasping, and safe delivery sequences.
- Integration: Processed visual telemetry via STM32 (C/C++) with optimized serial communication (UART) for low-latency response.