发表机构
Bangladesh Air Force Shaheen College Kurmitola; St.Joseph Higher Secondary School; Greenland Residential School（孟加拉国空军沙欣学院库尔米托拉分校; 圣约瑟夫高等中学; 格陵兰寄宿学校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Zephyron提出一种太阳能辅助移动机械臂的集成设计框架，通过可复现的组件基线与分析评估支持多模态环境侦察和分布式视觉推理，强调实验验证的必要性。
AI 中文摘要
环境侦察需要移动平台在有限能量和通信条件下携带传感器、保持测量背景并返回可解释的证据。我们提出了Zephyron的基于文献的工程设计，这是一种四轮漫游车，配备前部机械臂、环境传感器、分布式计算机视觉、本地记录以及抬高的后部太阳能模块。该设计保留了原型布局，但用明确的组件和几何基线取代了不支持的数值假设。可复现的检索获得了5,000条记录（4,858条唯一）用于筛选，随后对主要文献和制造商文档进行了定向审查。基线使用165毫米车轮、12公斤质量预算、72瓦时电池能量基础和20瓦光伏模块。在滚动阻力系数为0.04的情况下，在均匀载荷分担下，以10度坡度稳定爬升每轮需要约0.517牛·米。一个示例性的40瓦运动负载在57.6瓦时可用能量下提供1.44小时，而25%的行驶占空比在没有太阳能输入的情况下提供4.19小时；这些是计算场景，而非实测性能。传感器模型展示了积分时间、校准、温度和通信延迟如何约束解释，而一种质量感知的停止-采样策略将这些约束与任务执行联系起来。轻量级检测器、基于参考的传感器学习和可执行的数据完整性检查定义了一条可复现的机器学习评估路径。贡献是一个可追溯的设计和评估框架，包含可编辑的3D模型、子系统图和可复现的分析数据。在分配有效载荷、续航、检测或现场操作等级之前，需要进行实验验证。
英文摘要
Environmental reconnaissance needs mobile platforms that carry sensors, preserve measurement context, and return interpretable evidence under limited energy and communication. We present a literature-informed engineering design for Zephyron, a four-wheel rover with a front manipulator, environmental sensors, distributed computer vision, local recording, and a raised rear solar module. The design keeps the prototype layout but replaces unsupported numerical assumptions with an explicit component and geometry baseline. A reproducible search retrieved 5,000 records (4,858 unique) for screening, followed by targeted review of primary literature and manufacturer documentation. The baseline uses 165 mm wheels, a 12 kg mass budget, a 72 Wh battery-energy basis, and a 20 W photovoltaic module. With rolling-resistance coefficient 0.04, steady ascent of a 10 degree grade needs about 0.517 N m per wheel under equal load sharing. An illustrative 40 W motion load gives 1.44 h from 57.6 Wh usable energy, and a 25 percent driving duty gives 4.19 h without solar input; these are calculated scenarios, not measured performance. Sensor models show how integration time, calibration, temperature, and communication delay constrain interpretation, and a quality-aware stop-and-sample policy links these constraints to mission execution. Lightweight detectors, reference-based sensor learning, and executable data-integrity checks define a reproducible machine-learning evaluation pathway. The contribution is a traceable design and evaluation framework with editable 3D models, subsystem diagrams, and reproducible analytical data. Experimental validation is required before assigning payload, endurance, detection, or field-operating ratings.
