CSIRO has trialled autonomous, AI-enabled robots to inspect and monitor large-scale solar farms, aiming to reduce safety risks for workers and lower the cost of maintaining electricity output across sprawling sites.
The national science agency said the trial repurposed autonomous robots originally designed for use in the mining industry, adapting them to navigate “baked, uneven ground” typical of utility-scale solar farms. In the absence of automation, panel inspection and fault-finding is typically performed on foot, which CSIRO said brings significant cost and safety risks.
According to CSIRO, large-scale solar farms can produce more than 500MW of electricity, which it said is enough to power over 300,000 homes. Maintaining that output requires consistent inspection and monitoring of photovoltaic (PV) panels as well as repairs to racking and supporting materials.
CSIRO said the robotic system can autonomously navigate solar farms across different terrains, build site maps to digitise conditions, avoid hazards and use AI to interpret scenes. It is designed to detect a range of faults including dust build-up, insect nests or bird droppings, physical damage, loose nuts or bolts, hotspots in panels or electrical connectors, and wiring requiring repair.
The robots carry a sensor suite including Light Detection and Ranging (LiDAR) for 3D perception, RGB cameras for visual inspection, and thermal infrared cameras to identify electrical faults and hotspots.
CSIRO said using robots for inspection could reduce the need for workers to conduct long inspections on foot, improve efficiency and safety, and support earlier identification of faults that can degrade performance and shorten asset life.
Kenrick Anderson, CSIRO senior photovoltaic engineer, said hotspots can reduce PV panel efficiency over time due to electrical and thermal imbalances. “If solar farms cost less to run, and can be more consistent in their energy output, this increases the stability of the grid,” he said.
Peyman Moghadam, CSIRO senior principal research scientist, said the aim is to combine data from robots, fixed sensors and field systems to support proactive maintenance decisions and infrastructure resilience. He said the work aligns with CSIRO’s broader focus on robotics and AI for critical infrastructure and energy transition goals.
Ross Dungavell, CSIRO senior robotics engineer, said the approach could help address labour availability in remote regions and harsh conditions, with robots logging and storing captured data for analysis.
CSIRO said it is continuing to trial the robotic and AI systems at pilot sites in Australia, with the stated goal of working with industry to make the capability more widely available and improve renewable energy reliability.
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