Ultra-Reduced-Impact-Encased-Logging (URIEL): propose a new method for selective sustainable logging and post-harvest silvicultural treatment in tropical forest using airborne robotics systems

Computer Science > Artificial Intelligence [Submitted on 26 May 2026] Ultra-Reduced-Impact-Encased-Logging (URIEL): propose a new method for selective sustainable logging and post-harvest silvicultural treatment in tropical forest using airborne robotics systems Daniel Albiero, Gelton Fernando de Morais, Daniela Han, Flávio Roberto de Freitas Gonçalves, Artur Vitório Andrade Santos, Wesllen Lins de Araújo, Alessandra Maia Freire, Cláudio Kiyoshi Umezu, Mateus Peressin, Francesco Toscano, Admilson Írio Ribeiro, Alfeu J. Sguarezi Filho, Américo Ferraz Dias Neto, Angel Pontin Garcia Tropical forests worldwide are under intense deforestation pressure driven by economic and political interests, and scientific evidence suggests this deforestation contributes to climate change. This paper proposes a novel logging method for tropical forests, Ultra-Reduced-Impact-Encased-Logging (URIEL). This new method is based on heli-logging techniques combined with intensive use of robotics and AI integrated with post-harvest silvicultural treatments performed by drones. The concept of appropriate equipment for this method was developed, dimensions were determined, details were completed in a digital proof of concept, and an effective digital simulation and economic feasibility analysis were carried out for various helicopter-timber-distance combinations. The results demonstrated that a URIEL method has high economic viability and makes it possible to virtually eliminate collateral damage to forests while maintaining ecosystem services. The main conclusion of this paper is that, despite the satisfactory scientific and technological results, the feasibility of a Uriel method depends on the integration of stakeholders intrinsic to the context: high-tech industry; political governments; certified logging companies; and native populations. Comments: 196 pages, 40 figures, A revolutionary technology to help protect tropical forests. It was developed, scaled, detailed, calculated, and simulated in an advanced computational environment, com viabilidade econômica e social. "E pur si muove" Subjects: Artificial Intelligence (cs.AI); Robotics (cs.RO) MSC classes: I.2.8, I.2.9, I.2.10, I.2.11, I.2.4, I.2.1 Cite as: arXiv:2605.28883 [cs.AI]   (or arXiv:2605.28883v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.28883 Focus to learn more Submission history From: Daniel Albiero [view email] [v1] Tue, 26 May 2026 20:30:42 UTC (13,169 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)