Surveillance robots are increasingly useful in environments where direct human presence is unsafe, inconvenient or inefficient. A camera-controlled mobile robot can provide live visual feedback to an operator, while artificial intelligence can support safer movement by detecting obstacles, objects and human presence from the captured video stream. This paper converts the proposed student-level concept of an AI assistant-based surveillance robot into a structured research design suitable for journal submission. The proposed system integrates a mobile robotic chassis, DC motors, a motor driver, a microcontroller or embedded board, a camera module, wireless communication, power supply and a lightweight computer-vision layer. The robot is designed to operate in manual and AI-assisted modes. In manual mode, a user controls the robot through a web or mobile interface while receiving live camera feedback. In AI-assisted mode, selected video frames are processed using image-processing or object-detection algorithms to generate alerts and support collision avoidance. The paper presents the problem background, literature-based motivation, system architecture, hardware and software requirements, working principle, implementation methodology, testing framework, applications, limitations and future development scope. Since the base project document does not contain raw experimental measurements, the discussion is framed as a prototype design and validation framework rather than as a completed empirical performance report. The study concludes that a low-cost AI-assisted surveillance robot is technically feasible for indoor monitoring, academic demonstration, laboratory inspection and preliminary security applications, provided that limitations related to latency, low-light operation, battery capacity and embedded processing power are addressed through systematic testing and future upgrades.
Bajaj et al. (2026) studied this question.