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.

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' 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.

The technical capabilities of robots and their increased versatility require a single robot to be able to perform a wide array of complex tasks. What more, when performing a task, it may be asked to carry out another one. Switching between different tasks is a known problem in engineering, however, robots, compared to programs operating in a virtual space, are unique as they must follow laws of physics. In this work, we introduce a method for harmonising service robot tasks being managed by finite state machines. The method allows for the handling of safe suspend and resume procedures for a complex task. Additionally, we consider a case when the robot can not switch the current task to another at any time. The method was implemented and verified by conducting multiple interruptions of one task by another one. The tasks during the verification were being performed in simulation by a TIAGo robot.