Self-organizing control for unmanned ground vehicles

The process of Self-organisation (SO) is pervasive in nature, being observed across physical, biological, chemical, social, as well as technological systems. It enables continuous adaptation of a system to its dynamic environment, balancing real-world challenges such as finding the shortest path or the division of labour. These models and principles have been integrated into state-of-the-art algorithms and computational approaches, though primarily within the field of software agents. SO is achieved through interactions at the device level but manifests at the system level, offering a method to achieve system-wide control through the actions of individual participants. Key advantages include scalability (as the process is independent of system size) and robustness (as the collective continuously adapts without a single point of failure). This paper explores how existing SO approaches can be combined to optimise the performance of e.g., a fleet of logistic assets, ensuring that the preferences of individual fleet owners are respected while allowing their assets to collaborate through SO in non- permanent cooperating groups. The requirements, constraints and operational paradigms of the (civilian) logistic industry are found to be applicable in the military domain as well, whether for supply and logistics operations or to facilitate SO of assets on the battlefield. Insights from self-organising logistics demonstrate how assets from different units or branches can collaborate on the battlefield while respecting the priorities of the deploying units. This article presents the insights gained, the opportunities envisioned, and the proposed approaches.

Authors

Institutions

Publication Details

Published
2024-11-13
DOI
https://doi.org/10.1117/12.3033891
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Self-organizing control for unmanned ground vehicles

Hanno Hildmann, Ruben Fransen, Marijn v. Adrichem, Hannah Onverwagt
Robotic Path Planning Algorithms
article

Self-organizing control for unmanned ground vehicles

Hanno Hildmann, Ruben Fransen, Marijn v. Adrichem, Hannah Onverwagt
article en

Abstract

The process of Self-organisation (SO) is pervasive in nature, being observed across physical, biological, chemical, social, as well as technological systems. It enables continuous adaptation of a system to its dynamic environment, balancing real-world challenges such as finding the shortest path or the division of labour. These models and principles have been integrated into state-of-the-art algorithms and computational approaches, though primarily within the field of software agents. SO is achieved through interactions at the device level but manifests at the system level, offering a method to achieve system-wide control through the actions of individual participants. Key advantages include scalability (as the process is independent of system size) and robustness (as the collective continuously adapts without a single point of failure). This paper explores how existing SO approaches can be combined to optimise the performance of e.g., a fleet of logistic assets, ensuring that the preferences of individual fleet owners are respected while allowing their assets to collaborate through SO in non- permanent cooperating groups. The requirements, constraints and operational paradigms of the (civilian) logistic industry are found to be applicable in the military domain as well, whether for supply and logistics operations or to facilitate SO of assets on the battlefield. Insights from self-organising logistics demonstrate how assets from different units or branches can collaborate on the battlefield while respecting the priorities of the deploying units. This article presents the insights gained, the opportunities envisioned, and the proposed approaches.

Netherlands Organisation for Applied Scientific Research (NL), The Hague University of Applied Sciences (NL)
Openalex Percentile: Top 80%
Robotic Path Planning Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.