Final robotics project focused on autonomous maze solving, robot simulation, and neural-network-based anomaly detection.
The project combines intelligent navigation algorithms, robotics simulation, and AI techniques to develop an autonomous robot capable of navigating and reasoning in maze environments.
- Virginia Samez
- Tommaso Manzana
- Luca Girotti
- Lorenzo Madiai
- Johana Chen
The objective of the project was to design and implement a robot capable of:
- autonomously solving mazes,
- navigating inside a simulated environment,
- detecting anomalies and objects,
- adapting its behavior through intelligent algorithms.
The system was inspired by classical maze-solving robotics approaches and extended with AI-based techniques. :contentReference[oaicite:1]{index=1}
The project focused on three main areas:
Implementation of navigation and decision-making algorithms allowing the robot to:
- explore unknown mazes,
- avoid obstacles,
- identify valid paths,
- reach target destinations autonomously.
A complete simulation environment was developed to:
- test robot behaviors,
- validate navigation algorithms,
- reproduce realistic robotics scenarios.
The robot was adapted to operate inside the simulation while interacting with maze structures and environmental constraints.
The project explored AI extensions based on neural networks:
- training models for object/anomaly detection,
- improving environmental awareness,
- expanding the robot’s perception capabilities.
The goal was to move beyond simple reactive behaviors toward more intelligent autonomous systems.
- Autonomous maze navigation
- Intelligent path-finding
- Robotics simulation
- Obstacle avoidance
- Neural-network integration
- Object and anomaly detection
- Multi-component robotics architecture
- Python
- Robotics Simulation
- Neural Networks
- Autonomous Navigation Algorithms
- AI-Based Perception Systems