Autonomous Excavators Dig Around the Clock in Real Deployment
Researchers from Baidu Research Robotics and Auto-Driving Lab and the University of Maryland, College Park, have introduced an autonomous excavator system (that can perform material loading tasks for a long duration without any human intervention while offering performance closely equivalent to that of an experienced human operator. AES is among the world's first uncrewed excavation systems to have been deployed in real-world scenarios and continuously operating for over 24 hours, bringing about industry-leading benefits in terms of enhanced safety and productivity. In collaboration with a leading construction machinery company, Baidu demonstrated its Autonomous Excavator System to perform material loading and dumping with no human intervention at bauma CHINA 2020, an international trade fair for construction machinery, building material machines, mining machines, and construction vehicles.
The researchers described their methodology in a research paper published on June 30, 2021, in Science Robotics.
Excavators are vital for infrastructure construction, mining, and rescue applications. The global market size for excavators was USD 44.12 billion in 2018 and is expected to grow to USD 63.14 billion by 2026. Given this projected market increase, construction companies worldwide are facing hiring shortages for skilled heavy machinery operators, particularly excavators. Additionally, COVID-19 continues to exacerbate the labor shortage crisis. Another contributing factor is the hazardous and toxic work environments that can impact the health and safety of on-site human operators, including cave-ins, ground collapses, or other excavation accidents that cause approximately 200 casualties per year in the US alone.
While most industry robots are comparatively smaller and function in more predictable environments, excavator robots are required to operate in an extensive range of hazardous environmental conditions. They must be able to identify target materials, avoid obstacles, handle uncontrollable environments, and continue running under difficult weather conditions. AES uses accurate and real-time algorithms for perception, planning, and control alongside a new architecture to incorporate these capabilities for autonomous operation. Multiple sensors, including LiDAR, cameras, and proprioceptive sensors, are integrated for the perception module to perceive the 3D environment and identify target materials, along with advanced algorithms such as a dedusting neural network to generate clean images.
With this modular design, the AES architecture can be effectively utilized by excavators of all sizes, including 6.5 and 7.5 metric ton compact excavators, 33.5 metric ton standard excavators, and 49 metric ton large excavators, and is suitable for diverse applications.