Socially-aware navigation of omnidirectional mobile robot with extended social force model in multi-human environment

We propose a new navigation method for an omnidirectional mobile robot to maneuver in a complex and populated environment. In an indoor populated environment, for example, robot has to react to such circumstances and achieve both physical and psychological navigational safety. From social researches, human motion is a hybrid system composed of holonomic and non-holonomic movements. Based on this concept, we develop a socially-aware navigation method for an omnidirectional mobile robot to achieve natural, humanlike movement. By using laser range finder and camera as sensors, robot detects geometric features and human behavior information. From the geometric features robot can construct an environment model and extract the information about distances and directions of the obstacles. Whereas with the heading and orientation information from camera, robot can model individual humans successfully. While acquiring these models as context, a suitable navigation behavior would be executed. To interact with surroundings, we develop the extended Social Force Model(ESFM) to describe the interactive force, referred to as social force, with human and environment. As a whole, the contributions of our work are twofold one being that we propose a dynamic grouping model based on human behavior using learning-based method and another being that we develop an extended Social Force Model for the system based on which a successful navigation strategy can be realized. ? 2019 IEEE.