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

Información del proyecto

XSCAVE

Identificador del acuerdo de subvención: 101189836

DOI

10.3030/101189836

Fecha de la firma de la CE 4 Noviembre 2024

Fecha de inicio 1 Diciembre 2024

Fecha de finalización 30 Noviembre 2028

Financiado con arreglo a

• Digital, Industry and Space

Coste total

€ 7 975 530,00

Aportación de la UE

€ 7 975 530,00

Coordinado por

TARTU ULIKOOL
Estonia

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

Período documentado: 2024-12-01 hasta 2026-05-31

Resumen del contexto y de los objetivos generales del proyecto

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.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

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.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

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).