ACR-Drone: Adaptive Contextual Reasoning for Enhanced Self-Iterative Drone Autonomy

ACR-Drone: Adaptive Contextual Reasoning for Enhanced Self-Iterative Drone Autonomy | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 20 March 2026 V1 Latest version Share on ACR-Drone: Adaptive Contextual Reasoning for Enhanced Self-Iterative Drone Autonomy Authors : Bowen Mu 0009-0003-6723-0580 [email protected] and Xiaoxuan Xue Authors Info & Affiliations https://doi.org/10.22541/au.177401870.07626516/v1 64 views 27 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Achieving fully autonomous embodied agents, particularly drones, in complex and dynamic real-world environments remains a significant challenge. While Large Language Models (LLMs) have advanced decision-making, existing systems struggle with extreme dynamic changes, instruction ambiguities, or complex failure modes requiring deeper scene understanding. Specifically, the efficiency of self-correction systems relies on intelligently filtering and distilling contextual information for LLMs to provide precise diagnostic evidence. This paper introduces ACR-Drone, an embodied drone autonomous task planning and self-iterative improvement system. ACR-Drone significantly enhances environmental comprehension and self-correction through an Adaptive Contextual Reasoning (ACR) mechanism. It integrates novel components such as dynamic semantic anchor points for initial Behavior Tree (BT) generation, an Adaptive Contextual Monitoring and Execution module featuring Context-Aware Filtering and Predictive State Reasoning, an Embodied Knowledge Base, and Hierarchical BT Refinement with Contextual Constraints for targeted modifications. Rigorous experimental validation, adhering to established task benchmarks, demonstrates that ACR-Drone consistently achieves superior overall task success rates, improved failure diagnosis, enhanced refinement efficiency, and greater robustness in both simulated and real-world environments, without requiring fine-tuning of the underlying LLMs. Our system's proactive detection capabilities and reduced refinement cycle times underscore the profound benefits of adaptive contextual reasoning for advanced drone autonomy. Supplementary Material File (acr_drone.pdf) Download 1.96 MB Information & Authors Information Version history V1 Version 1 20 March 2026 Copyright This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License Keywords adaptive contextual reasoning drones large language models self-correction task planning Authors Affiliations Bowen Mu 0009-0003-6723-0580 [email protected] Kunming University of Science and Technology View all articles by this author Xiaoxuan Xue Kunming University of Science and Technology View all articles by this author Metrics & Citations Metrics Article Usage 64 views 27 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Bowen Mu, Xiaoxuan Xue. ACR-Drone: Adaptive Contextual Reasoning for Enhanced Self-Iterative Drone Autonomy. Authorea . 20 March 2026. DOI: https://doi.org/10.22541/au.177401870.07626516/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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