Zephyron rover design guides solar-powered mobile environmental sensing
Zephyron: Integrated Design and Analytical Evaluation of a Solar-Assisted Mobile Manipulator for Multimodal Environmental Reconnaissance and Distributed Visual Inference
Robotics
Summary
Collecting environmental data remotely needs robots that sense, move, and save energy smartly. The paper describes Zephyron, a small four-wheel rover with sensors, a manipulator arm, solar panels, and cameras that analyze surroundings. The authors carefully modeled its parts, energy use, and sensors using published data rather than guesses. Their work offers clear designs and calculations to plan missions, but it still needs real-world testing to confirm performance.
What this means in practice
- •For environmental monitoring teams: Plan and evaluate solar-assisted rovers for remote sensing missions with integrated data processing and stable energy use.
- •For robotics developers: Use Zephyron’s explicit design and evaluation framework to build and test mobile manipulators with energy and sensor constraints.
Authors
Sabik Bin Sultan, Shafi Bin Sultan, Safwan Sadad
Abstract
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.