发表机构
SUNY Binghamton University（纽约州立大学宾汉姆顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对热带大型农场水果采摘依赖人工、成本高的问题，提出IVG-UAV系统，集成语音识别、视觉成熟度分类与自适应路径规划，并在Gazebo中仿真验证，实现自主采摘。
AI 中文摘要
在热带地区，农业部门的水果采摘仍高度依赖人工劳动。在大型农场中，这种依赖性往往导致显著的劳动力成本和物流复杂性。本项目介绍了一种语音控制的无人机（UAV）系统的开发与仿真，该系统旨在实现广阔种植园中采摘任务的自动化。所提出的系统将基于Whisper[1]和LLM的语音识别、基于计算机视觉的成熟度分类以及自适应路径规划集成在一个统一框架中。整个系统在Gazebo仿真环境中进行建模和验证，从而能够在受控的农业场景下进行性能评估。
英文摘要
In tropical regions, their agricultural sectors remain highly dependent on manual labor for fruit harvesting. On large-scale farms, this dependency often results in significant labor cost and logistic complexities. This project presents the development and simulation of a voice-controlled Unmanned Aerial Vehicle (UAV) system designed to automate harvesting tasks in extensive plantations. The proposed system integrates speech recognition using Whisper [1] and LLM, computer vision-based ripeness classification, and adaptive path planning within a unified framework. The entire system is modeled and validated in a Gazebo simulation environment, allowing performance evaluation under controlled agricultural scenarios
