Side Tendency Layer: Enhancing Social Robot Navigation for Passenger and Pedestrian Comfort Using Local Sensor Data

Due to the demographic shift towards an aging population, there is growing interest in the development of Person Navigation Systems (PNS), which support people with impairments and their caregivers. However, there is a lack of algorithms for navigating these systems that behave socially and simultaneously take the passenger comfort factors, such as side tendency, into account. In this paper, we propose a novel method that incorporates side tendency into the path planning for PNS or social robots using local sensor data. The sensor data of our intelligent wheelchair is utilized to approximate the walls on the left and right. Based on these detections, a predefined comfort function is applied to assign lateral costs in the costmap according to the estimated width of the corridor, representing the side tendency. Utilizing dynamic sensor data, the proposed approach facilitates adaptation to changes or unknown environments, thus demonstrating its superiority over previous methods based on a static global map.