The Role of Time in a Robotic Swarm: A Joint View on Communications, Localization, and Sensing

Abstract

Autonomous robotic swarms are envisioned for a variety of sensing applications in space exploration, search-and-rescue and disaster management. An important capability of a swarm is sensing spatio-temporal processes such as radio wave propagation or seismic activities. The spatio-temporal properties of these processes dictate the required sensing position and time accuracy, as well as update rate. A dedicated wireless communication system needs to be jointly designed for swarm information exchange, self-localization and sensing. In this article, we emphasize the role of time in a robotic swarm. We introduce the system ingredients and dive into realistic clock models. Clock models and channel access schemes decisively influence the swarm self-localization and synchronization accuracy, and consequently the swarm sensing performance. Finally, we discuss practical implementation aspects, introduce our developed swarm radio system, and give an outlook on a moon-analogue exploration mission. Siwei Zhang received the B.Sc. in electrical engineering from the Zhejiang University, China, in 2009, the M.Sc. in communication engineering from the Technical University of Munich, Germany, in 2011, and the Dr.-Ing. (Ph.D.) in electrical engineering from the University of Kiel, Germany, in 2020. In 2012, he joined the Institute of Communications and Navigation, German Aerospace Center (DLR), Germany, as a Research Staff Member. His research interests include radio navigation, cooperative positioning and swarm navigation. Robert Pöhlmann received B.Sc. and M.Sc. in electrical engineering and information technology from the Technical University of Munich (TUM), Germany, in 2014 and 2016, respectively. In 2013, he joined the Institute of Communications and Navigation, German Aerospace Center (DLR), Germany, as a Student Trainee, where he became a Research Staff Member in 2016. His current research interests are in the area of statistical signal processing for multi-antenna systems and cooperative localization. Armin Dammann received the Dipl.Ing. (M.Sc.) and Dr.Ing. (Ph.D.

Cooperative Communication, Localization, Sensing and Control for Autonomous Robotic Networks

“…Sub-meter position uncertainty can be obtained even for LOFAR boxes that do not directly connected with anchors. This accuracy is at least an order of magnitude smaller than the wavelength of the low frequency signals (15 m), ergo sufficient for LOFAR mission [8]. The LOFAR signals are detected by all LOFAR boxes coherently.…”

“…In this paper, we have a joint look at the communication, localization, sensing and control aspects in an exploration mission, which is essential for design such an autonomous robotic network [7], [8]. Depending on the overall mission objectives, the trade-off between communication and localization determines the favorable swarm formations.…”

“…Figure 5. LOFAR payload boxes are placed by a lightweight rover and precisely synchronized and localized with our swarm navigation system[8]. Low frequency radio signals emitted either from space or by an artificial transmitter are detected by this array.…”

Simultaneous Localization and Calibration for Cooperative Radio Navigation

“…We assume that the processing time between forward and backward transmission at node j is already compensated and very short, such that the impact of the relative frequency offset of the two clocks can be neglected. Otherwise, the clock behavior must be taken into account with an appropriate clock model [35]. The ranging bias is caused by varying group delays in the transmitter and receiver chains, for instance due to temperature variations of the RF components.…”

Real-Time Learning for THZ Radar Mapping and UAV Control

“…Following a similar research direction, [4] showed the optimization of the trajectory of a unmanned aerial vehicle (UAV)-radar for environment mapping and detection using a RL approach where rewards were predicted within a finite temporal horizon. Indeed, time is a key aspect for UAV networks because of their limited energy autonomy [5][6][7] and, thus, it should be properly accounted for when designing the UAV control for time-critical applications (e.g., search-and-rescue). In [5], an information-seeking algorithm is developed for extraterrestrial exploration and return-to-base application, whereas in [8,9] a similar problem is solved using RL for source localization.…”