International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 01 | Jan 2026
p-ISSN: 2395-0072
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Design and Implementation of a Semi-Intelligent UAV with Real-Time Video Streaming, Obstacle Detection, and Payload Control Rajendra Khule1 , Tanu Girsawade2, Krunal Wade3, Shraddha Duryodhan4, Sayali Kodhe5, Anurag Somkuwar6 1Dept of Electronics and Telecommunication, KDK College of Engineering, Maharashtra, India 23456Dept of Electronics and Telecommunication, KDK College of Engineering, Maharashtra, India
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Abstract- This paper presents the design and
or lack onboard real-time processing capabilities. Emerging low-cost microcontrollers, such as the ESP32-CAM, present promising alternatives by enabling lightweight image acquisition and wireless streaming without the need for high-performance processors like the Raspberry Pi. However, achieving an optimal balance between cost, flight stability, sensing precision, and autonomous functionality remains a persistent challenge.
implementation of a semi-intelligent Unmanned Aerial Vehicle (UAV) that combines manual flight control with autonomous features using low-cost embedded hardware. The system utilizes an ESP32-CAM module for real-time video streaming and basic image processing, while a Pixhawk or similar flight controller manages stabilization and navigation. A telemetry module ensures continuous data exchange between the UAV and the ground station, providing live updates of altitude, GPS coordinates, battery status, and system health. Integrated ultrasonic sensors enable obstacle detection and avoidance, improving flight safety during semi-autonomous operations. Additionally, a servo mechanism supports payload release or adjustable camera control for multi-purpose missions. The proposed UAV architecture demonstrates how affordable microcontrollers and sensors can be integrated to create a versatile, semi-intelligent drone platform suitable for surveillance, agricultural monitoring, disaster management, and rescue operations.
This review synthesizes recent advancements in UAV development, with a focus on autonomous navigation, obstacle avoidance, intelligent video transmission, and payload management. It critically examines existing architectures, identifies limitations in current implementations, and explores how semi-intelligent UAV frameworks can deliver robust performance while remaining accessible to researchers and developers with constrained resources.
2. LITERATURE REVIEW
Key Words: UAV, ESP32-CAM, Telemetry, Autonomous Drone, Obstacle Detection, Flight Controller.
Vision-based navigation has become essential for UAV operation in GPS-denied and complex environments. Arafat et al. [1] comprehensively reviewed visual localization, mapping, obstacle avoidance, and path planning techniques, highlighting their importance for autonomous navigation. Intelligent decision-making approaches using reinforcement learning were surveyed by Al Mahamid et al. [2], who classified RL algorithms for UAV navigation tasks. Increasing autonomy levels impose higher computational demands, and Mejias et al. [3] analyzed UAS task classifications and autonomy levels, emphasizing suitable embedded computation architectures. Semi-autonomous navigation concepts have also been applied to practical use cases such as medical delivery, where Praveena et al. [23] demonstrated ESP32-CAM–based live video streaming, GPS tracking, and telemetry-enabled semi-autonomous drone operation.
1. INTRODUCTION Unmanned Aerial Vehicles (UAVs) have undergone significant transformation over the past decade, evolving from exclusive military assets into versatile platforms widely adopted across civilian, commercial, and academic domains. Applications in precision agriculture, environmental surveillance, disaster response, and real-time monitoring increasingly depend on UAVs for their ability to access remote or hazardous environments and deliver timely, highresolution data. The growing demand for intelligent UAV systems has catalyzed the integration of advanced sensing technologies, embedded computing modules, and semi-autonomous control mechanisms into compact and cost-effective aerial platforms. These innovations aim to enhance operational efficiency, situational awareness, and decision-making capabilities while maintaining affordability and ease of deployment.
Vision-based autonomous landing, surveillance, and obstacle avoidance systems have been widely explored using low-cost embedded platforms. Xin et al. [4] reviewed vision-based autonomous landing techniques across static, dynamic, and complex scenarios. Embedded vision-based obstacle avoidance using camera and ultrasonic sensors was demonstrated by Rahman and Sasonko [21], employing PID control for real-time navigation. Smart surveillance applications combining AI and IoT were presented by Anis et
Despite the proliferation of commercial drones, many research-grade UAV systems remain prohibitively expensive
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