Jarosław Karwowski’s research while affiliated with Warsaw University of Technology and other places

Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.

Social robots have recently gained popularity, and many human-aware navigation approaches have emerged. This work presents SRPB – a comprehensive benchmark for quantitatively assessing robot navigation methods. As an automated quantitative approach, our benchmark produces invariable indicators of the algorithm’s performance that can assist the system designer in selecting the best method for the specific application. Our benchmark extends state-of-the-art task performance scores and proposes novel social metrics regarding robot motion naturalness and the perceived safety of humans surrounding the robot. Our social metrics take human tracking reliability into account. Using SRPB integrated with the TIAGo robot, we assessed the robot’s behaviour operating with traditional and human-aware trajectory planners in simulated and real-world environments. Our experiments tested whether state-of-the-art human-aware trajectory planners significantly improve human-awareness indicators over traditional approaches yet still maintain reasonable navigation performance. An open-source implementation of our benchmark, compatible with the Robot Operating System, is provided.

Social robots' software is commonly tested in a simulation due to the safety and convenience reasons as well as an environment configuration repeatability assurance. An interaction between a robot and a human requires taking a person presence and his movement abilities into consideration. The purpose of the article is to present the HuBeRo framework, which can be used to simulate human motion behaviour. The framework allows independent control of each individual's activity, which distinguishes the presented approach from state-of-the-art, open-source solutions from the robotics domain. The article presents the framework assumptions, architecture and an example application with respect to presented scenarios.