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FH-JQ1004
Feihong
The Large-Scale Robot Testing Bench is a high-performance robotic mobility testing platform engineered for humanoid robots, biped robots, quadruped robots, exoskeleton systems, and advanced robotic locomotion research.
Featuring a 4.5 m × 3 m extra-large treadmill area, ±15° bidirectional incline simulation, and 0–40 km/h speed range, the platform enables engineers to evaluate robot walking, running, climbing, balance control, endurance, energy consumption, and motion stability under highly repeatable laboratory conditions.
Designed for robotics manufacturers, AI laboratories, universities, research institutes, and autonomous mobility developers, the system combines industrial-grade reliability with comprehensive safety protection for long-duration testing and algorithm validation.
The platform supports incline adjustment from -15° to +15°, allowing engineers to simulate:
Uphill locomotion
Downhill descent control
Uneven terrain adaptation
Dynamic balance recovery
Drive system load variation
Testing Objective:
Evaluate robot stability, traction performance, actuator efficiency, and gait adaptability across different terrain conditions.
With a speed range of 0–40 km/h, the system covers:
Low-speed precision walking
Human-like gait validation
Jogging and running tests
High-speed mobility evaluation
Emergency maneuver verification
Testing Objective:
Measure robot performance across the entire operational speed envelope while ensuring repeatable testing conditions.
The generous 4.5 m × 3 m running surface accommodates:
Full-size humanoid robots
Heavy robotic platforms
Exoskeleton systems
Biomimetic robots
Multi-legged robotic systems
Maximum load capacity:
230 kg
Testing Objective:
Support testing of larger robots without motion restrictions.
The integrated 15.6-inch touchscreen displays:
Speed
Incline angle
Distance
Running time
Heart-rate data (for human-assisted testing)
The system records:
Speed curves
Incline curves
Physiological monitoring curves
Up to 4 hours of continuous data logging.
Testing Objective:
Provide quantitative data for robot control optimization, AI training, and performance validation.
The testing bench supports:
Servo motor control
Automated test execution
Remote control operation
Robotics R&D integration
Customized software interfaces
Suitable for:
AI locomotion algorithm training
Reinforcement learning experiments
Autonomous navigation validation
Robot certification preparation
Safety features include:
Four emergency stop switches
Dual remote-control emergency stop
Safety rails and guard structures
Robot safety harness compatibility
Industrial electrical protection architecture
Testing Objective:
Protect expensive robotic prototypes while ensuring operator safety during dynamic testing.
Walking stability
Running performance
Balance recovery
Terrain adaptation
Fall prevention algorithm validation
Dynamic gait optimization
Slope traversal
High-speed locomotion
Endurance testing
Assisted walking evaluation
Rehabilitation research
Load-bearing validation
Motion control development
AI training and validation
Reinforcement learning testing
Energy efficiency studies
Item | Specification |
|---|---|
Overall Dimensions | 4.95 m × 4.37 m × 2.9 m |
Treadmill Area | 4.5 m × 3.0 m |
Drive System | Servo Motor Control |
Incline Range | -15° to +15° |
Speed Range | 0–40 km/h |
Speed Accuracy | 0.1 km/h |
Maximum Load Capacity | 230 kg |
Display | 15.6-inch Touchscreen |
Data Recording | Speed, Incline, Heart Rate Curves |
Noise Level | ≤90 dB |
Power Supply | 380V ±15% |
Rated Current | ≥50A |
Field testing is important, but environmental variables often make results difficult to reproduce. A robot testing treadmill provides controlled and repeatable conditions for evaluating gait stability, balance algorithms, locomotion efficiency, and motion control performance before deployment in real-world environments.
The -15° to +15° incline simulation allows engineers to evaluate how a robot responds to uphill and downhill conditions. This helps verify actuator output, braking strategies, balance control algorithms, energy consumption, and foot-ground interaction under varying terrain loads.
The system can monitor and record:
Walking/running speed
Distance traveled
Incline angle
Endurance duration
Motion stability
Gait consistency
Dynamic balance behavior
Energy efficiency trends
These datasets are critical for robot control system optimization.
Yes. The platform provides a repeatable training environment that is ideal for:
Reinforcement learning
Robot motion planning
Autonomous navigation development
Dynamic balance training
Human-like gait generation
The stable testing conditions improve algorithm validation efficiency and reduce development cycles.
The platform integrates multiple safety layers including:
Four physical emergency stop switches
Dual remote emergency stop controls
Safety rail structures
Harness compatibility
Industrial electrical protection
These measures help prevent prototype damage during unexpected falls, software faults, or extreme testing conditions.
Typical users include:
Humanoid robot manufacturers
Robotics startups
AI robotics companies
University robotics laboratories
Government research institutes
Autonomous mobility developers
Exoskeleton manufacturers
Industrial robot R&D centers
The platform is particularly suitable for organizations developing next-generation humanoid robots and advanced locomotion systems.