Evaluation of Cleaning Robots in Desert Environments

Soiling is a critical factor that can lead to over 1% daily power loss in photovoltaic systems and is highly dependent on local climate conditions. This issue is particularly severe in the UAE, where the harsh desert climate heightens soiling via airborne sand and industrial emissions. Low rainfall and high humidity worsen surface contamination. Strategies such as robotic cleaning and anti-soiling coatings are employed to mitigate these effects. This study assesses the efficacy of different robotic cleaners on fixed and tracker-mounted PV structures in desert environments. Fixed structures showing non-uniform soiling were evaluated based on Pmax, while uniform soiling was seen on trackers and was evaluated via Isc measurements. Modules cleaned daily with Robot A (bristle-based) on fixed structures had a soiling loss of 2.6%. On trackers, modules cleaned daily with Robot B (microfiber) showed a soiling loss of 3.3%, while Robot C (bristle-based) showed a soiling loss of 4.3%. In contrast, uncleaned modules (over 106 days) mounted on fixed tilt and trackers showed a 9% soiling loss. This study highlights the effectiveness of robotic cleaning in significantly reducing soiling losses and maintaining module performance. Our findings suggest that microfiber-based cleaning robots are more efficient and less abrasive than those with brush bristles. However, despite causing slight surface wear, brush-bristle robots might still be the preferred choice in areas like Dubai, where heavy soiling significantly impacts PV power generation. Future research is needed to evaluate their long-term cost-effectiveness and performance compared to other cleaning technologies in desert climates.

Authors

Institutions

Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/pvsc59419.2025.11133412
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of Cleaning Robots in Desert Environments

Vivian Alberts, S. Bhaduri, Hebatalla Alhamadani, Sgouris Sgouridis et al.
Robotic Path Planning Algorithms
article

Evaluation of Cleaning Robots in Desert Environments

Vivian Alberts, S. Bhaduri, Hebatalla Alhamadani, Sgouris Sgouridis, Baloji Adothu, Prashanth Gabbadi, Shahzada Pamir, Yogesh Senthilkumar
article en

Abstract

Soiling is a critical factor that can lead to over 1% daily power loss in photovoltaic systems and is highly dependent on local climate conditions. This issue is particularly severe in the UAE, where the harsh desert climate heightens soiling via airborne sand and industrial emissions. Low rainfall and high humidity worsen surface contamination. Strategies such as robotic cleaning and anti-soiling coatings are employed to mitigate these effects. This study assesses the efficacy of different robotic cleaners on fixed and tracker-mounted PV structures in desert environments. Fixed structures showing non-uniform soiling were evaluated based on Pmax, while uniform soiling was seen on trackers and was evaluated via Isc measurements. Modules cleaned daily with Robot A (bristle-based) on fixed structures had a soiling loss of 2.6%. On trackers, modules cleaned daily with Robot B (microfiber) showed a soiling loss of 3.3%, while Robot C (bristle-based) showed a soiling loss of 4.3%. In contrast, uncleaned modules (over 106 days) mounted on fixed tilt and trackers showed a 9% soiling loss. This study highlights the effectiveness of robotic cleaning in significantly reducing soiling losses and maintaining module performance. Our findings suggest that microfiber-based cleaning robots are more efficient and less abrasive than those with brush bristles. However, despite causing slight surface wear, brush-bristle robots might still be the preferred choice in areas like Dubai, where heavy soiling significantly impacts PV power generation. Future research is needed to evaluate their long-term cost-effectiveness and performance compared to other cleaning technologies in desert climates.

Dubai Health Authority (AE)
Openalex Percentile: Top 78%
Robotic Path Planning Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Evaluation of Cleaning Robots in Desert Environments — Vivian Alberts, S. Bhaduri, et al. · (2025) | TGRS Research Map | TGRS