When most of us hear about Autonomous Vehicles, we think passenger vehicles and imagine a future where cars will drive us home from the pub, or take the kids to soccer practice. The reality is, the commercial world is also looking to capitilise on the technology to make business operations more efficient and safer.
In a new blog post from Chinese search giant Baidu, we learn that researchers in their Robotics and Auto-Driving Lab (RAL) in partnership with the University of Maryland, College Park, have introduced an autonomous excavator system (AES) 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.
“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.”
Dr. Liangjun Zhang, corresponding author and the Head of Baidu Research Robotics and Auto-Driving Lab.
COVID-19 has exacerbated labor shortages, providing an increased opportunity for the autonomous machines to help. This is an industry that often involves toxic 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 U.S. alone.
While most industry robots are comparatively smaller and function in more predictable environments, excavator robots face an extensive range of challenging environments they need to navigate. Not only do they first need to identify target materials, avoid obstacles, handle uncontrollable environments, but they need to continue operating under difficult weather conditions.
The Baidu solution for 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. These inputs are processed 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.
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, a toxic and harmful real-world scenario where automation is in strong demand.
AES was able to continuously operate for more than 24 hours without any human intervention.
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.”
Dr. Liangjun Zhang, corresponding author and the Head of Baidu Research Robotics and Auto-Driving Lab.
It is clear that if this technology can run 24hrs a day, 7 days a week, it will dramatically outperform humans that need to take breaks and even those that work across shifts, still require some downtime to swap humans. Parts of the world are facing growth challenges and having tasks be automated and repeated reliably hour after hour is something humans operators will struggle to compete with economically.
This is certainly an area to watch over the coming months and years. You can watch the demonstration in the video published below.
