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

Projektinformationen

XSCAVE

ID Finanzhilfevereinbarung: 101189836

DOI

10.3030/101189836

EK-Unterschriftsdatum 4 November 2024

Startdatum 1 Dezember 2024

Enddatum 30 November 2028

Finanziert unter

• Digital, Industry and Space

Gesamtkosten

€ 7 975 530,00

EU-Beitrag

€ 7 975 530,00

Koordiniert durch

TARTU ULIKOOL
Estonia

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

Berichtszeitraum: 2024-12-01 bis 2026-05-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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).