“Why has the power output of a photovoltaic plant dropped by 20% after just one year of operation?”
When inspectors climbed onto the rooftop, they found the solar panels blanketed in thick dust.
Dust is far from a minor issue. Long-term dust accumulation can reduce power generation efficiency by 15%–30%, while partial shading may trigger hotspot effects and even cause permanent damage to PV modules.
As a result, solar panel cleaning has become an essential part of photovoltaic plant maintenance.
Traditional manual cleaning is not only a high-risk operation at height, but also comes with high labor costs, low cleaning frequency, and inconsistent cleaning quality.
As a result, autonomous solar cleaning robots have been increasingly adopted by photovoltaic plants as a potential solution.
However, the reality has proven far from ideal.
In real-world operations, many cleaning robots still experience:
Under cloudy conditions, low-light environments, nighttime operations, or complex obstructions, some systems may even fail to operate reliably and are forced to shut down.
This raises a key question:
Why do autonomous solar cleaning robots still struggle with reliable navigation?
Think of them as two different “guides”:
GNSS provides the robot’s position on the map, while INS tracks how it is moving.
When GNSS and INS are integrated, they complement each other’s strengths.
GNSS continuously provides position corrections for INS, while INS maintains navigation output when GNSS signals are blocked, temporarily interrupted, or unavailable.
As a result, the system achieves:
Scenario A | Rail-Mounted Cleaning Robots
Some rail-mounted cleaning robots rely solely on standalone GNSS positioning for navigation. Once satellite signals are obstructed by PV mounting structures, positioning drift can occur, causing the robot to deviate from its intended path.
Results:
Scenario B | Rail-Free Panel-Climbing Robots
Some rail-free solutions rely on standard GPS and ultrasonic sensors for navigation. When operating on inclined panel surfaces, wheel slippage can easily cause odometer failure, while panel gaps and shadows further reduce the accuracy of visual recognition systems.
Results:
The challenge lies not in the robot itself, but in the limitations of conventional navigation technologies.
To address the navigation challenges of autonomous solar cleaning robots, Archimedes Innovation developed the PBOX, an industrial-grade tightly coupled GNSS/INS navigation receiver designed specifically for photovoltaic cleaning applications.
Maintains reliable operation even under weak satellite signals, with obstruction resistance improved by more than three times.
Integrates GNSS, IMU, wheel odometry, and vehicle motion data to maintain accurate positioning during wheel slippage, vibration, and turning maneuvers.
Automatically identifies panel surfaces, gaps, and mounting structures while dynamically correcting driving trajectories to help prevent falls and rollovers.
Operating temperature: -40°C to +85°C
Protection rating: IP67
Reliable operation in darkness, rain, snow, and dusty environments.
Designed for stable and reliable autonomous operation.
Field Performance Comparison
Customer Case
A 100 MW ground-mounted photovoltaic power plant deployed 12 rail-free cleaning robots.
After adopting the GNSS/INS integrated navigation solution:
The improved cleaning efficiency also contributed to measurable gains in annual power generation.
With the Archimedes Innovation PBOX integrated navigation receiver, future photovoltaic power plants will no longer rely on risky rooftop cleaning operations or unstable cleaning robots.
Instead, they will move toward:
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