Explainable, Safe, Contact-Aware Planning and Control for Heavy Machinery Manipulation and Navigation

Project Information

XSCAVE

Grant agreement ID: 101189836

DOI

10.3030/101189836

EC signature date 4 November 2024

Start date 1 December 2024

End date 30 November 2028

Funded under

• Digital, Industry and Space

Total cost

€ 7 975 530,00

EU contribution

€ 7 975 530,00

Coordinated by

TARTU ULIKOOL
Estonia

Periodic Reporting for period 1 - XSCAVE (Explainable, Safe, Contact-Aware Planning and Control for Heavy Machinery Manipulation and Navigation)

Reporting period: 2024-12-01 to 2026-05-31

Summary of the context and overall objectives of the 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 terrain-machine 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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

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.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

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).