Thesis Code: 25009
Research Area: Connected Systems & Cybersecurity
Description:
This thesis investigates socially-aware navigation strategies for autonomous mobile robots when crossing roads in human-populated environments. The objective is to develop and evaluate algorithms that enable a robot to interpret pedestrian behavior, infer intentions, and cross the street safely while adhering to social norms and human expectations. The system will integrate perception modules (e.g., pedestrian detection, trajectory prediction), decision-making models, and motion planning techniques that account for right-of-way rules, courtesy behaviors, implicit communication cues, and safe interaction distances.
The thesis includes designing a navigation pipeline that allows the robot to decide when and how to cross, negotiate with nearby pedestrians through motion cues, and execute a smooth, collision-free trajectory. The proposed approach will be tested in simulation and/or controlled real-world scenarios to assess safety, efficiency, and social acceptability. The outcome will provide insights into human-robot coexistence in dynamic urban spaces and contribute to the development of socially compliant autonomous mobility systems.
Requirements:
- C++ and Python programming languages
- Linux OS knowledge
- Proactive and problem solving mindset
Contact: send a resume with attached the list of exams to francesco.aglieco@linksfoundation.com and enrico.ferrera@linksfoundation.com
