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.

A review of the known and an indication of the new threats for cyber-physical robotic systems, caused by cybernetic attacks, serves, in this paper, as a basis for the analysis of the known methods relied upon to detect and mitigate consequences of such attacks. A particular emphasis is placed on threats specific for cyber-physical systems, as they are a feature distinguishing these systems from their traditional Information and Communication Technologies (ICT) counterparts. Based on the review of literature and own analyses, unresolved issues regarding the cyber-security of robot systems are presented and discussed.

Purpose – This paper seeks to develop universal software (a programming framework) enabling the implementation of service robot controllers. The software should distinguish the hardware-oriented part of the system from the task-oriented one. Moreover, force, vision as well as other sensors should be taken into account. Multi-effector systems have to be considered. Design/methodology/approach – The robot programming framework MRROC++ has been implemented as a hierarchical structure composed of processes, potentially consisting of threads. All of the software is written in an object-oriented manner using C++ and is supervised by a QNX real-time operating system. The framework has been verified on several systems executing diverse tasks. Here, a Rubik's cube puzzle-solving system, consisting of two arms and utilizing force control and visual servos, is presented. Findings – The presented framework is well suited to tasks requiring two-handed manipulation with force sensing, visual servoing and online construction of plans of actions. The Rubik's cube puzzle is a reasonable initial benchmark for validation of fundamental service robot capabilities. It requires force sensing and sight coupled with two-handed manipulation and logical reasoning, as do the majority of service tasks. Owing to the use of force sensing during manipulation, jamming of the faces has always been avoided; however, visual servoing could only cope with slow handing over of the cube due to the volume of computations associated with vision processing. Research limitations/implications – The proposed software structure does not limit the implementation of service robot controllers. However, some of the specific algorithms used for the solution of the benchmark task (i.e. Rubik's cube puzzle) need to be less time-consuming. Practical implications – The MRROC++ robot programming framework can be applied to the implementation of diverse robot controllers executing complex service tasks. Originality/value – A demanding benchmark task for service robots has been formulated. This task, as well as many others, has been used to validate the MRROC++ robot programming framework which significantly facilitates the implementation of diverse robot systems.

An application of advanced optimization techniques to solve the path planning problem for closed chain robot systems is proposed. The approach to path planning is formulated as a "quasi-dynamic" NonLinear Programming (NLP) problem with equality and inequality constraints in terms of the joint variables. The essence of the method is to find joint paths which satisfy the given constraints and minimize the proposed performance index. For numerical solution of the NLP problem, the IPOPT solver is used, which implements a nonlinear primal-dual interior-point method, one of the leading techniques for large-scale nonlinear optimization.