https://ir.cut.ac.zw/xmlui/handle/123456789/47 Fri, 17 Jul 2026 00:14:43 GMT 2026-07-17T00:14:43Z https://ir.cut.ac.zw/xmlui/handle/123456789/812 Implementation of robotics in improving safety and efficiency in mining operations Simango, Doubt; Jinya, Last E; Musaidzi, H; Makusha, D. T. Mining operations present hazardous conditions, particularly in post-blast environments where the accumulation of toxic gases poses significant risks to worker safety. This study presents a robotic system designed to autonomously navigate underground mining terrains and detect harmful gases such as methane, carbon monoxide, and carbon dioxide. The robot is built with a rocker-bogie mechanism for stability on uneven surfaces and uses IR sensors for obstacle detection. Gas detection is carried out using a sensor-integrated circuit. A GSM module is used to transmit the gas concentration data to surface-level receivers. Simulation results confirmed the robot's capability to navigate autonomously and detect gas presence with reliable accuracy. Wed, 01 Jan 2025 00:00:00 GMT https://ir.cut.ac.zw/xmlui/handle/123456789/812 2025-01-01T00:00:00Z https://ir.cut.ac.zw/xmlui/handle/123456789/811 AI and computer vision-based pest and disease detection for greenhouse crops in Zimbabwe Simango, Doubt; Mahungwe, Panashe G.; Makusha, D. T.; Musaidzi, H. Pests and diseases remain a major constraint to agricultural productivity in Zimbabwe, causing significant losses and limiting farmers’ income. In greenhouse farming, early detection of infestations is critical to ensure healthy crops and reduce pesticide overuse. This study presents a computer-vision system powered by artificial intelligence (AI) for early identification of common pests and diseases in greenhouse crops. A convolutional neural network (CNN) trained on a regionally curated dataset of healthy and diseased plant images automatically classifies visual symptoms from digital photographs. The system is implemented through a MATLAB desktop application, enabling offline classification for users with limited internet access. The CNN achieved 83 % training accuracy and 82 % validation accuracy, with high precision and recall across multiple crop categories. Testing confirmed reliable detection of leaf curl, septoria leaf spot, and related infections in tomatoes and peppers. This work demonstrates that locally trained deep-learning models can effectively support greenhouse farmers in Zimbabwe, enhancing early response and minimizing losses due to pest and disease outbreaks. Wed, 01 Jan 2025 00:00:00 GMT https://ir.cut.ac.zw/xmlui/handle/123456789/811 2025-01-01T00:00:00Z https://ir.cut.ac.zw/xmlui/handle/123456789/810 Autonomous disinfection robot with Robotic Arm for precision cleaning Simango, Doubt; Ndavambi, Denzel C.; Chihota, Kelvin; Makusha, D. T. Hospital-acquired infections (HAIs) remain a major challenge to patient safety and healthcare efficiency. This project presents the design and implementation of an autonomous disinfection robot equipped with a 6-degree-of-freedom robotic arm for precision sanitization in healthcare environments. The robot integrates a LiDAR-based SLAM system for navigation, mecanum wheels for omnidirectional mobility, and an electrostatic spray system for targeted disinfection. A database-linked user interface allows authorized personnel to monitor, schedule, and control disinfection processes remotely. Simulation and prototype results confirm that the system effectively maps environments, avoids obstacles, and performs adaptive surface cleaning with minimal human intervention. Wed, 01 Jan 2025 00:00:00 GMT https://ir.cut.ac.zw/xmlui/handle/123456789/810 2025-01-01T00:00:00Z https://ir.cut.ac.zw/xmlui/handle/123456789/806 Autonomous Assistive Exoskeleton Control System Integrating Eeg/Emg Intent Decoding Simango, D.; Muchenje, Sean T.; Makusha, D. T.; Sibanda, Brian Mobility impairments caused by spinal cord injuries, stroke, and degenerative disorders limit independ ence for millions worldwide. This study presents a low-cost lower-limb exoskeleton that decodes user intention in real time using electroencephalographic (EEG) and electromyographic (EMG) signals. The system employs a PIC16F877A microcontroller executing adaptive impedance control and gait-phase recognition, with a Kalman filter combining inertial, torque, and bio-signal inputs to generate smooth hip–knee trajectories. Bench-top and hardware-in-the-loop simulations classified four locomotor commands – stand, sit, walk, stop – with 96 % accuracy and a maximum 85 ms latency. The lightweight 3.8 kg aluminium–carbon frame, powered by back drivable BLDC motors, reproduced natural hip excursions of 15–35° at 1.2 s cadence. The results validate the feasibility of embedding edge AI for assistive gait restoration in resource-constrained clinical contexts. Keywords: exoskeleton, EEG, EMG, intent recognition, adaptive control, rehabilitation. Wed, 01 Jan 2025 00:00:00 GMT https://ir.cut.ac.zw/xmlui/handle/123456789/806 2025-01-01T00:00:00Z