Path Planning, Mapping & Learning for Mobile Robot Navigation

Ph.DDOCTOR OF PHILOSOPH

Learning to Navigate from Scratch using World Models and Curiosity: the Good, the Bad, and the Ugly

Learning to navigate unknown environments from scratch is a challenging problem. This work presents a system that integrates world models with curiosity-driven exploration for autonomous navigation in new environments. We evaluate performance through simulations and real-world experiments of varying scales and complexities. In simulated environments, the approach rapidly and comprehensively explores the surroundings. Real-world scenarios introduce additional challenges. Despite demonstrating promise in a small controlled environment, we acknowledge that larger and dynamic environments can pose challenges for the current system. Our analysis emphasizes the significance of developing adaptable and robust world models that can handle environmental changes to prevent repetitive exploration of the same areas.Comment: IROS 2023 workshop World Models and Predictive Coding in Cognitive Robotics and IROS 2023 workshop Learning Robot Super Autonom

Simultaneous Path Planning and Topological Mapping (SP2ATM) for environment exploration and goal oriented navigation

10.1016/j.robot.2010.12.003Robotics and Autonomous Systems593-4228-242RASO