Unmanned Express Delivery System

projects

1. Hardware Selection and Configuration

The team has selected and configured a complete hardware system for the express delivery robot:

1.1 Mobile Chassis

We selected the AgileX HUNTER SE Ackermann chassis as the mobile core of the robot. Key parameters and features are as follows:

Property	Specification
Dimensions	820 × 640 × 310 mm
Wheelbase	550 mm
Weight	42 kg
Max Load Capacity	50 kg (Meets package transport weight requirements)
Power	24V Lithium Battery (30Ah/60Ah) + 2 × 350W Brushless DC Drive Motors
Steering	150W Brushless DC Motor + 1:4 Reduction Gearbox
Steering Type	Front-wheel Ackermann steering
Steering Accuracy	0.5°
Max Inner Steering Angle	22°
Max Speed (No Load)	4.8 m/s
Min Turning Radius	1.9 m
Max Climbing Angle	20°
Min Ground Clearance	120 mm
Operating Temperature	-10 ~ 45°C (Adaptable to various competition environments)
Control & Security	Anti-collision beam, supports remote control (2.4G, max 200m) and command control
Communication Interface	CAN

1.2 Robotic Arm

The PIPER robotic arm was ultimately selected for grasping and placing packages. Core parameters:

Property	Specification
Degrees of Freedom	6 DOF
Effective Payload	1.5 kg
Body Weight	4.2 kg
Repeat Positioning Accuracy	±0.1 mm
Working Radius	626.75 mm (High grasping accuracy)
Power Consumption	Max ≤ 120W, Comprehensive ≤ 40W
Communication Method	CAN (Compatible with chassis communication)
Operating Temperature	-20 ~ 50°C
Operating Humidity	25% - 85% (Non-condensing)
Base Mounting Dimensions	70 mm × 70 mm × M5 × 4 (Easy integration)
Hardware Limitation	Gripper max opening: 70mm vs. Target diameter: 65mm (Demands high algorithm accuracy)

1.3 Computing, Sensing & Auxiliary Devices

Component	Description
Computing Core	11th Gen Intel Core i7 NUC, ensuring algorithm execution and data processing efficiency.
Visual Perception	Hikvision industrial camera, adaptable to various lighting conditions, providing clear images for object detection.
Mapping & Localization	MID360 LiDAR paired with an Inertial Measurement Unit (IMU) for environmental mapping and robot localization.
Auxiliary Structures	3D-printed Coke placement rack, tailored for specific grasping/placing target storage needs.

2. Algorithm Design and Implementation

2.1 Technical Implementation: Navigation and Localization

The overall solution for autonomous navigation and localization is based on ROS1, utilizing the FAST-LIO algorithm for mapping and localization, and the Nav2 navigation stack for path planning and motion control. FAST-LIO2 employs tightly coupled LiDAR-inertial fusion for high-precision mapping with mid360 LiDAR and IMU. Multi-sensor fusion (LiDAR, IMU, chassis odometry) constructs a tf tree for robust relocalization. Nav2 uses a Behavior Tree architecture with Dijkstra for global planning and TEB for local planning, optimizing trajectories for time, smoothness, obstacles, and kinematics. An event-driven module coordinates waypoint navigation with robotic arm interactions via YAML configuration and topic publishing.

2.2 Technical Implementation: Robotic Arm Grasping and Placing Control

Coordinate systems are established using tf2, including chassis, robotic arm origin, end-effector, camera, and gripper frames. Object detection uses YOLOv11 for recognition with transfer learning and secondary confirmations like color analysis. AprilTag recognition provides 3D positioning, and point cloud segmentation with EKF fuses 2D and 3D data for precise coordinates. MoveIt! handles trajectory planning with URDF models and RRT algorithms for collision-free paths, adjusting control parameters for velocity and acceleration limits.

3. Reference Projects and Open Source

During development, we referenced:

1. FAST_Lio2: https://github.com/hku-mars/FAST_LIO
2. Fast_Lio_Localization: https://github.com/HViktorTsoi/FAST_LIO_LOCALIZATION
3. YOLO v11: https://github.com/ultralytics/ultralytics
4. Piper SDK: https://github.com/agilexrobotics/piper_ros
5. Hunter SE ROS Driver: https://github.com/agilexrobotics/hunter_ros