Precision Measurement of Rolling Resistance Coefficient for AGV Polyurethane Wheels

Importance of AGV Wheel Performance in Automated Car Factory Logistics

In modern automated car factory logistics, the Automated Guided Vehicle (AGV) has become a crucial tool for intelligent material handling. As the only point of contact between AGV chassis and the floor, the AGV polyurethane wheel directly influences energy efficiency, navigation precision, and equipment wear.

One critical parameter to evaluate AGV wheel performance is the rolling resistance coefficient, which is essential for designing high-efficiency AGV systems. However, inaccuracies in traditional measurement techniques have hindered widespread optimization.

Technical Challenges and Breakthroughs

Traditional testing methods face two main limitations:

1. Material deformation:
   Polyurethane materials exhibit strong nonlinear strain characteristics. Changes in contact area due to varying loads make static friction measurements unreliable under real AGV conditions.

2. Kinematic inconsistencies:
   Due to slippage and vibration, the motor speed often deviates from the actual wheel speed, leading to measurement errors that affect motion modeling and control, especially in AGV systems for automotive manufacturing.

This new method introduces a dual-correction mechanism—disturbance factor and pure rolling factor—to dynamically and precisely measure the rolling resistance coefficient of polyurethane wheels. The accuracy improves by more than 40% compared to traditional approaches.

Measurement Method: Three-Step Approach

Step 1: Disturbance Factor Calculation
High-precision force sensors are used under various AGV operating conditions (0.2–1.5 m/s, 50–1000 kg):

• Compare static and uniform-speed μ values;

• When Δμ < ε (threshold, e.g., 0.05), the system is deemed stable;

• Calculate δ as the disturbance correction factor to eliminate vibration effects.

Step 2: Pure Rolling Factor
Using a dual-encoder system:

• ωₘ from the motor-side encoder;

• ωᵣ from the wheel-shaft encoder;

• Calculate α = ωᵣ / ωₘ, a key indicator of slip presence in real-world AGV deployments.

Step 3: Final Calculation
The refined rolling resistance coefficient is derived as:
μ = μₛ × α ± μₛ × δ

Table: Reference value of typical polyurethane wheel perturbation factor

This hybrid approach is ideal for AGV digital twin models, improving both physical accuracy and simulation reliability.

Application Scenarios in Smart Automotive Logistics

1. Wheel Selection for AGVs

• High μ (0.08–0.12): Heavy-load areas and high-friction zones;

• Low μ (0.03–0.06): High-speed, empty-load routes in AGV logistics.

2. Energy Consumption Estimation
Apply F = μ × m × g to simulate AGV energy requirements more accurately.

3. Navigation Control Tuning
Fine-tune PID control based on real friction data:

• Improve stop precision to ±2mm;

• Reduce cargo sway and handling loss.

Industry Value Summary