About this Research Topic
The presence of autonomous robots is continuously increasing in all sectors of the economy. Even though automated systems have incessantly gained importance in the primary and secondary sectors since the industrial revolution, human labour is unlikely to become redundant. Therefore, autonomous robots need to interact seamlessly amongst their human collaborators to boost human comfort and their joint performance. Autonomous systems’ social skills are even more critical in those robots involved in the service industry. One of the most relevant skills of the robots used in health care, museums, airports, and restaurants – to name a few – is precisely to interact and move around humans, who are at the core of the service industry.
More than two decades of research in social robotics has brought many advances in social navigation. Human trajectory prediction and human-aware path planning are among the areas that have gathered more attention, arguably due to their immediate application. Research involving non-verbal cues, interaction and collaborative navigation has recently gained considerable interest too. Experimental studies to model human-robot proxemics and provide human-aware navigation metrics are still needed to evaluate human-aware navigation algorithms systematically.
This Research Topic aims to compile the latest state-of-the-art research in social navigation for autonomous systems addressing the previously mentioned topics and other topics related to human-aware navigation. This Research Topic calls for contributions in the field of human-aware navigation-related, but not limited to, the following themes:
• Human-aware localisation and mapping in human-populated environments
• Human-aware path planning for autonomous systems
• Perception and sensors for social robot navigation
• Human trajectory prediction
• Social cues in human-aware navigation (verbal and non-verbal cues)
• Human-robot interaction applied to social navigation (verbal and non-verbal interaction)
• Human guidance and collaborative navigation
• Datasets for human-aware navigation
• Simulation of social navigation scenarios
• Evaluation of social navigation algorithms
• Machine learning applied to social navigation
More than two decades of research in social robotics has brought many advances in social navigation. Human trajectory prediction and human-aware path planning are among the areas that have gathered more attention, arguably due to their immediate application. Research involving non-verbal cues, interaction and collaborative navigation has recently gained considerable interest too. Experimental studies to model human-robot proxemics and provide human-aware navigation metrics are still needed to evaluate human-aware navigation algorithms systematically.
This Research Topic aims to compile the latest state-of-the-art research in social navigation for autonomous systems addressing the previously mentioned topics and other topics related to human-aware navigation. This Research Topic calls for contributions in the field of human-aware navigation-related, but not limited to, the following themes:
• Human-aware localisation and mapping in human-populated environments
• Human-aware path planning for autonomous systems
• Perception and sensors for social robot navigation
• Human trajectory prediction
• Social cues in human-aware navigation (verbal and non-verbal cues)
• Human-robot interaction applied to social navigation (verbal and non-verbal interaction)
• Human guidance and collaborative navigation
• Datasets for human-aware navigation
• Simulation of social navigation scenarios
• Evaluation of social navigation algorithms
• Machine learning applied to social navigation
Keywords: human-aware navigation, social robotics, autonomous robots, path planning in crowded environments, perception for human-aware navigation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
