Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network

Area of Science:

• Robotics
• Artificial Intelligence
• Computer Vision

Background:

• Autonomous robots are crucial for tasks like disaster response.
• Vision-based systems enhance robot perception and interaction.
• Efficient navigation and target identification are key challenges.

Purpose of the Study:

• To propose an intelligent surveillance robot architecture.
• To enable autonomous operation and image acquisition in dynamic environments.
• To demonstrate effectiveness in disaster victim rescue applications.

Main Methods:

• Utilizing 3 ultrasonic distance sensors for obstacle avoidance.
• Implementing a backpropagation neural network for sensor data processing.
• Integrating a camera for face recognition capabilities.
• Employing a 2.4 GHz transmitter for remote video transmission and control.

Main Results:

• The proposed architecture demonstrates effective obstacle avoidance.
• Face recognition functionality was successfully integrated.
• The system's performance was evaluated, showing promising results.
• The robot proved capable of autonomous navigation and image acquisition.

Conclusions:

• The developed intelligent surveillance robot architecture is effective for autonomous operation.
• The integration of ultrasonic sensors and neural networks facilitates robust obstacle avoidance.
• The system shows significant potential for applications such as disaster victim rescue.