A Survey on Human-aware Robot Navigation

Abstract

Intelligent systems are increasingly part of our everyday lives and have been integrated seamlessly to the point where it is difficult to imagine a world without them. Physical manifestations of those systems on the other hand, in the form of embodied agents or robots, have so far been used only for specific applications and are often limited to functional roles (e.g. in the industry, entertainment and military fields). Given the current growth and innovation in the research communities concerned with the topics of robot navigation, human-robot-interaction and human activity recognition, it seems like this might soon change. Robots are increasingly easy to obtain and use and the acceptance of them in general is growing. However, the design of a socially compliant robot that can function as a companion needs to take various areas of research into account. This paper is concerned with the navigation aspect of a socially-compliant robot and provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions.

The Risk of Automation of Occupations at European Level

“…This category of skills is a little different from the others, because they are also an emotional component (Pietrykowski, 2017), which is why it is a little harder to imitate by Artificial Intelligence, but not impossible. Robots can develop care and health care skills by learning (Möller et al, 2021). Managerial skills are an important category needed in the labor market and encourage the creation of an organizational culture (Shamsi, 2017).…”

Modeling Human Behavior Part I -- Learning and Belief Approaches

“…Additionally, systems require the means to observe and adapt to humans [94,107]. An important example is human-aware robot navigation [67,70] and further human-robot interaction scenarios [109]. In the navigation context, the motion, goals, and general behavior are crucial for the robot to successfully navigate the environment.…”

“…This adaptability has been explored in RL agents in robotics to enable generalization from initial demonstrations or exploration [48,99], which enables aspects of learning from demonstrations and an interactive learning process as seen in Imitation Learning, Inverse Reinforcement Learning, and Active Learning. Robot agents can also learn to anticipate the movement of humans or other artificial agents in order to compensate for their movements or rendezvous at a later position [67,70,122]. This concept can also be extended to further topics in human-robot collaboration [109].…”

Companion robot communication with road infrastructure as part of IoRT

“…changes in shape and size, and devotes much attention to reactive algorithms and rigorous mathematical research of proposed navigation solutions, showing the interdependence between mathematics and robotics. It is similar in the publication of Möller et al (2021) [3], where it was recognized that the design of a robot serving society that can act as a companion must take into account different areas of research. In an article by Calderit et al (2021) [4] presented a new framework for robot navigation by introducing the concept of time-dependent social mapping.…”