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An Active Stability Control System for Robots in Vertical Farms
ME-B- 20
Liel Azran; Lielazran24@gmail.com Avihi Meshulam; Avihi.m123@gmail.com
Advisor: Dr. Chen Giladi
SCE - Shamoon College of Engineering, Ashdod
This project presents the ongoing development of an active stability control system for robots operating in multilayered environments, as in vertical robotics, active stability control, and vertical farms, and in reinforcement learning (RL) solutions with six effective, low-maintenance designs. Three structural prototypes—square, circular, and hexagonal—have been developed, emphasizing modularity, manufacturability, and adaptability. As part of the control approach, RL algorithms are being implemented to enable the system to adapt dynamically to disturbances and operational changes commonly encountered in dense agricultural settings. Our project is in its experimental phase, and laboratory tests are underway under static and dynamic conditions. As yet, no conclusive performance results are available. The overall goal is to deliver a robust, energy-efficient, and reliable stability control solution that improves agricultural robots' safety, precision, and productivity during tasks like seeding, monitoring, and harvesting in vertical farm environments.
Keywords: adaptive algorithms, dynamic stabilization, Kalman filter, orientation sensors, robotics, servo motors, Stewart platform, vertical farms
Vertical Farming Resource Management Using Intelligent Technologies
ME-B-21
Oriel Solomon; orielsolomon@gmail.com Lidor Harush; lidorh@gmail.com
Advisor: Dr. Chen Giladi
SCE - Shamoon College of Engineering, Ashdod
This project presents a design for an intelligent monitoring system for vertical farming, using a cable- driven robot that collects ‘red, green and blue’ (RGB) images for 3D reconstruction via “Structure from Motion” (SfM). It addresses key challenges in precision, scalability, and real-time visualization. Our system is modular and adaptable to various greenhouse layouts, with future potential for additional sensors. Color space transformations (e.g., HSV, Lab) enhance plant stress detection. Currently in design and simulation stages, this project is focusing on mechanical prototyping and algorithm development, laying the foundation for a low-cost, scalable, vision-based monitoring platform for smart agriculture.
Keywords: 3D reconstruction, agricultural monitoring, cable-driven modular system design, color space transformation, precision agriculture, RGB imaging, robotics, scalable sensing, SfM, vertical farming