Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey article, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. In this domain, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Therefore, based on this observation, in the beginning of this article, we propose a categorization to organize the use cases of proximity sensors in human-centered robotics. Then, we devote effort to present the sensing technologies and different measuring principles that have been developed over the years, also providing a summary in form of a table. Following, we review the literature regarding the applications that have been proposed. Finally, we give an overview of the most important trends that will shape the future of this domain.

This article presents ViRa, a real-time open-source millimeter-wave radar simulation framework for perception-driven robotic applications. ViRa provides (i) raw data of radar sensors in real-time, simulation of (ii) multi-antenna configurations for spatial estimation of objects, (iii) wave penetration of non-conductive objects to infer information in occluded situations, (iv) different radar beam patterns, and, (v) configurations of radar sensors as given by real-world radars. By using ViRa, researchers can simulate radar sensors in different robotic scenarios and investigate radars prior to the installation. This allows an acceleration in the development of radar sensors for robotic applications without the need of real hardware. Contrary to simple model abstractions, which only output loose features, ViRa generates raw radar data using computer graphics techniques on graphics processing unit (GPU) level embedded inside a game engine environment. ViRa allows to feed data directly into machine learning frameworks, which enables further improvement in novel research directions, such as safe human-robot interaction or agile drone flights in obstacle-rich environments. The proposed simulation framework is validated with data from different scenarios in robotics such as human tracking for human-robot interaction. The obtained results are compared with a reference simulation framework and show significantly higher correlation when compared to real-world measurement data.

Robust object detection and low computational effort are two key issues, which have to be addressed when Electrical Capacitance Tomography (ECT) is applied in the field of mobile applications. In this paper we present an object detection technique in combination with an artefact reduced fast reconstruction algorithm. The approach achieves a high object detection rate in the vicinity and center of the sensor front end. The proposed light-weight signal processing chain is the key to integrate this sensing technology on a platform limited in terms of space, energy and computational resources.