SUSTAINABLE
AUTONOMY
SMART-HEAVY
MACHINERY
LOW-IMPACT
CONSTRUCTION
THE XSCAVE PROJECT
Earth-Moving, Forestry, and Urban Logistics are sectors where increased autonomy can spur drastic economic growth along with
addressing some core societal (e.g. address labor shortage) and environmental problems (e.g. minimize soil damage, fuel
consumption). Yet there are persisting challenges related to variations of tasks/environments that are intricately linked to the terrainmachine contact encountered during navigation and manipulation. For example, an Excavator machine used in Earth-Moving needs to adapt to different types of terrain (ground) underneath (loose soil, rocks of different shapes and sizes), for scooping. Such task and environment adaptation require machines to modify their “perception-to-action” mapping based on online observations from different sensing modalities.
XSCAVE will leverage the exceptional representation and approximation capabilities of deep neural networks to automatically learn the terrain/specific adaptation of excavation, forwarding, and navigation strategies from data. The overall objective of XSCAVE is (i) to develop capabilities for learning performant (high-speed), safe (stable, contact-aware), and explainable perception-to-action models for terrain adaptive excavation and navigation strategies and (ii) demonstrate step-change in autonomy for Excavation, Forwarding and Navigation tasks prevalent in Earth-Moving, Forestry and Logistics industries.
To this end, XSCAVE aims to re-imagine deep-learned models as neural networks augmented with parameterized structured priors derived from physics, optimization, and classical search to bring domain knowledge into the learning pipeline. The fundamental innovations at the algorithmic level will translate to unprecedented ability for the machines to plan, control and adapt their actions depending on the task and terrain contact conditions. The end-results will be demonstrated in partnership with Novatron (earth-moving), Komatsu (forestry), and Clevon (outdoor logistic vehicles).
About THE PROJECT
Heavy machinery is often used in harsh environments, such as excavations, forests or urban traffic in northern weather conditions. Self-driving vehicles need to operate on icy roads in winter, and excavators need to be able to cope on terrain with loose soil. All kinds of unpredictable environmental conditions – such as ice, snow, mud and unmovable large rocks – make the use of self-driving technology difficult and dangerous, as an autonomous vehicle may not be able to cope with rapid and unpredictable changes in the weather.
An international team of researchers, led by the Institute of Technology of the University of Tartu, will look for AI-based ways to ensure the safety and reliability of self-driving heavy vehicles in such conditions. The aim of the project is to improve autonomous operations in land management, forestry and logistics to reduce the fuel consumption of vehicles and environmental impacts such as soil damage.
PROJECT PARTNERS
Participating organizations include Aalto University, Algoryx, Forschungszentrum Informatik, Clevon, Czech Technical University in Prague, Komatsu Forest, Novatron, Tampere University, Toshiba Europe, Umeå University, and University of Tartu.
