Video: Autonomous excavator shows promise in unmanned material loading tests

Video: Autonomous excavator shows promise in unmanned material loading tests

Researchers say these excavators can offer performance close to that of an experienced operator.


Researchers from Baidu Research Robotics and Auto-Driving Lab (RAL), Sunnyvale, California, and the University of Maryland, College Park, Maryland, have introduced an autonomous excavator system (AES). According to RAL and the university, the AES can perform material loading tasks for a long duration without any human intervention while offering performance close to that of an experienced human operator.

The researchers say AES is among the world's first uncrewed excavation systems to have been deployed in real-world scenarios and operated continuously for over 24 hours. The ability to operate without a human in the cab can help contractors enhance safety and increase productivity.

The researchers described their methodology in a research paper published on June 30 in Science Robotics

"This work presents an efficient, robust and general autonomous system architecture that enables excavators of various sizes to perform material loading tasks in the real world autonomously," Liangjun Zhang, the report’s corresponding author and the head of the Baidu Research Robotics and Auto-Driving Lab, says.

Construction companies worldwide are facing hiring shortages for skilled heavy machinery operators, particularly those who operate excavators. Additionally, COVID-19 continues to exacerbate the labor shortage crisis and cave-ins, ground collapses and other excavation accidents cause approximately 200 casualties per year in the U.S. alone.

Excavator robots can provide solutions to meet these needs, researchers say, making the development of systems such as AES a growing trend alongside the implementation of other robots in manufacturing, warehouses and autonomous vehicles.

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 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. These excavators are also suitable for diverse applications.

To evaluate the efficiency and robustness of AES, researchers teamed up with a leading equipment manufacturing company to deploy the system at a waste disposal site. Despite the challenging assignment, AES was able to continuously operate for more than 24 hours without any human intervention. AES has also been tested in winter weather conditions, where vaporization can pose a threat towards the sensing performance of LiDAR. The amount of materials excavated, in both wet and dry form, was 67.1 cubic meters per hour for a compact excavator, which is in line with the performance of a traditional human operator. "AES performs consistently and reliably over a long time, while the performance of human operators can be uncertain," Zhang says.

Researchers also set up 10 different scenarios at a closed testing field to see how the system performed in numerous real-world tasks. After testing a variety of large, medium-sized, and compact excavators, AES was ultimately proven to match the average efficiency of a human operator in terms of the amount of materials excavated per hour.

"This represents a key step moving towards deploying robots with long operating periods, even in uncontrolled indoor and outdoor environments," Dinesh Manocha, professor of computer science and electrical and computer engineering at the University of Maryland, says.

Going forward, Baidu Research RAL says it will continue to refine core modules of AES and further explore scenarios where extreme weather or environmental conditions may be present.

Baidu has been collaborating with several of the world's leading construction machinery companies to automate traditional heavy construction machinery with AES.

"We aim to leverage our robust and secure platform, infused with our powerful AI and cloud capabilities to transform the construction industry," Haifeng Wang, CTO of Baidu, says.

Watch a video of the AES in action, courtesy of Baidu Research RAL: