## 1 Introduction  
Drones have transformed numerous industries by performing tasks in hazardous or inaccessible environments [1]. Indoor inspection drones, in particular, play a critical role by providing essential data through visual, thermal, and structural analyses, thereby supporting effective decision-making in industrial operations [2, 3, 4, 5]. However, indoor environments present unique challenges such as limited space, the absence of GPS signals, and dynamic obstacles. These constraints necessitate advanced collision avoidance mechanisms to ensure safe and reliable navigation [6, 7]. Existing drone technologies offer various obstacle detection and avoidance mechanisms. These typically involve the use of sensors such as LiDAR, ultrasonic sensors, and cameras, combined with algorithms that process environmental data to predict and prevent collisions [7, 8]. Despite these developments, significant challenges remain in indoor applications. The dynamic nature of indoor environments, characterized by narrow spaces, moving objects, and unpredictable obstacles, renders many existing systems insufficient. Moreover, the confined spatial layout, lack of GPS-based positioning, and the need for rapid maneuvering further complicate safe navigation [9]. Addressing these issues is essential to ensure the safety of the drone, its environment, and personnel involved in inspection operations. In a related study, [PERSON] and [PERSON], [PERSON] [10] proposed a Time-of-Flight distance sensor-based construction equipment activity detection method in dynamic site environments. Their method demonstrated the ability of ToF sensors to robustly detect movements and interactions of machinery under challenging conditions. This further validates the effectiveness of ToF sensors in complex, cluttered spaces, highlighting their suitability not only for static obstacle detection but also for dynamic activity monitoring. The insights from their work underscore the adaptability and reliability of ToF technology, particularly in environments where conventional sensors may struggle due to lighting variability or spatial constraints. Recent research [11] has placed a particular focus on Sense-and-Avoid (SAA) mechanisms, which aim to enable drones to autonomously detect and avoid obstacles in real time. Approaches such as the Fast Obstacle Avoidance Motion (FOAM) algorithm integrate data from sensors like monocular cameras and LiDAR to create a 2D probabilistic occupancy map that helps estimate free spaces for obstacle avoidance. While effective to a degree, these systems tend to be bulky, complex, and consume considerable power, making them less suitable for lightweight, energy-efficient indoor drones requiring real-time operation [12; 13; 14; 15]. [PERSON] and [PERSON] [16] introduced an omnidirectional proximity sensor system utilizing optical Time-of-Flight (ToF) sensors, which enabled accurate real-time obstacle detection. While their system provided efficient omnidirectional coverage, its primary focus was on obstacle detection rather than active avoidance. This study builds upon their work by proposing a more cost-effective and power-efficient solution suitable for indoor inspection drones. In addition to detecting potential obstacles, the system introduced here incorporates real-time collision avoidance mechanisms. Manual flight with teleoperation is essential for indoor inspection tasks, where human judgment and flexibility play a critical role in navigating complex environments [17; 18]. However, to enhance safety and operational efficiency during such flights, a reliable obstacle detection and avoidance system is necessary. This research addresses these challenges by proposing a novel solution: the integration of lightweight, low-power, and compact Time-of-Flight (ToF) distance sensors to enable full 360-degree horizontal obstacle detection and collision avoidance.