Perception Model for Mobile Robots Assisting Humans in Decision-Making during Complex Situations

Abstract

Perception, the process of comprehending and deriving meaning from one's surroundings, is fundamental to human decision-making. In this context, we explore the development of a robust perception model designed for mobile robots to facilitate effective human-robot communication and decision-making in dynamic and intricate scenarios.
Achieving localization without GPS in a network of roads using stratified sequential importance sampling where the stratification levels are based on semantic object spaces in the map and on the running time, we articulate and describe the proposed development and experimentation environment, demonstrating the potential of our perception model.