Optimized Waveform for Energy Efficient Ranging

Mobile communication terminals exploit existing reference signal structures for propagation delay based positioning. However, the used waveforms are not optimized for energy efficiency and improved ranging performance for positioning. Recently, a parametric waveform with adaptable power spectral density has been proposed in the context of 5G, and has shown an improved ranging performance. In this paper, we investigate the energy reduction of a ranging signal for a targeted ranging performance by adjusting the parametric waveform. We focus on the newly opened 28 GHz frequency band offering 850 MHz of contiguous bandwidth in the United States. Based on derived Ziv-Zakai lower bounds and a mmWave path loss model with shadow fading we determine the optimal waveform parameter. Our results show a transmit power reduction of 4.77 dB compared to existing reference signal structures. Furthermore, we show a link budget example in the context of ITS positioning

Effect of Non-Integer Delay on Ranging Accuracy for Ultra-Reliable Systems

Ultra-reliable communication systems are drawing a lot of attention due to the rising demand on new wireless technologies for safety critical applications. Many of these applications require ultra-reliable distance estimation between the communicating nodes. Automatic coupling between train wagons is one of the scenarios where ultra-reliable communication and ranging at short distances is required. The main objective of this paper is to define a theoretical channel model for the aforementioned scenario, to define a proper discrete equivalence of the communication system model, to derive Cram´er Rao Lower Bounds for ranging accuracy. Ranging accuracy simulation results are provided using three systems: ITS-G5, IR-UWB, and a proposed 5G wide band system operating in the mm-Wave frequency band. We show from the results that the proposed mm-Wave system is suitable for ultra-reliable ranging at short distances

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

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 scheme 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

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

Networks composed of a myriad of autonomous robots have attracted increasing attention in recent years due to their enormous capability expansion from single robot systems. In these networks, robots benefit from the collaboration with each other to enhance their situation awareness for autonomous operation. For example, in an extraterrestrial exploration mission, a robotic swarm can collaboratively utilize the inter-robot communication system to propagate information, synchronize and navigate itself to achieve mission objectives like joint environmental sensing. In addition, each robot can decide and control its own trajectory, so that the aforementioned tasks are accomplished in a globally efficient manner. In this paper, we propose formation optimization strategies for autonomous robotic networks, which adapt to the mission demands on cooperative communication, localization and sensing. We also discuss three space exploration examples with different mission demands, which leads to distinct network formations. These three missions will be conceptually demonstrated in a space analog mission on the volcano Mount Etna in June 2022

Swarm Technologies For Future Space Exploration Missions

Modern robotic platforms for in-situ space exploration are single-robots equipped with a number of specialized sensors providing scientists with unique information about a planet's surface. However, there is a number of exploration problems where large spatial apertures of the exploration system are necessary, requiring a completely new perspective on in-situ space exploration and it's required technologies. Large networks of robots, called swarm, pave the way: agents in a swarm span ad-hoc communication networks, localize themselves based on radio signals, share resources, process data and make inference over the network in a decentralized fashion. By cooperation, local information collected by agents becomes globally available. In this work we present our recent results in development of swarm technologies for future in-situ space exploration missions: a wireless system jointly used for communication and localization, and swarm navigation and exploration strategies to sample and reconstruct static spatial fields

Simultaneous Localization and Calibration for Cooperative Radio Navigation

Cooperative radio localization and navigation systems can be used in scenarios where the reception of global navigation satellite system (GNSS) signals is not possible or impaired. While the benefit of cooperation has been highlighted by many papers, calibration is not widely considered, but equally important in practice. Utilizing the signal propagation time requires group delay or ranging bias calibration and estimating the direction-of-arrival (DoA) requires antenna response calibration. Often, calibration parameters are determined only once before operation. However, the calibration parameters are influenced by e.g. changing temperatures of radio frequency (RF) components or changing surroundings of antennas. To cope with that, we derive a cooperative simultaneous localization and calibration (SLAC) algorithm based on Bayesian filtering, which estimates antenna responses and ranging biases simultaneously with positions and orientations. By simulations, we show that the calibration parameters can be estimated during operation without additional sensors. We further proof practical applicability of SLAC by evaluating measurement data from robotic rovers. With SLAC, both ranging and DoA estimation performance is improved, resulting in better position and orientation estimation accuracy. SLAC is thus able to provide reliable calibration and to mitigate model mismatch. Finally, we discuss open research questions and possible extensions of SLAC

Simultaneous Localization and Antenna Calibration

Cooperative localization fills the gap in scenarios where global navigation satellite system (GNSS) reception is denied or impaired. Position and orientation information is then often provided based on signal round-trip time (RTT) and direction-of-arrival (DoA). Obtaining a meaningful RTT requires calibrated transceiver group delays, and accurate DoA estimation requires antenna calibration. Usually, such calibrations are performed once before operation. However, calibration parameters can change over time, e.g. due to varying temperature of RF components or reconfigurable antenna surroundings. To cope with that, we propose to estimate antenna responses and ranging biases simultaneously with positions and orientations by simultaneous localization and calibration (SLAC). We derive a SLAC algorithm based on Bayesian filtering, which is suitable for arbitrary antenna types. The algorithm is evaluated with measurement data from robotic rovers. We show, that ranging and DoA performance is improved considerably, leading to better position and orientation accuracy with SLAC

Autonomous Navigation of a Robotic Swarm in Space Exploration Missions

In recent years the paradigm of navigation has shifted from pinpointing the location of a single agent to continuously estimating the full kinematic state of a team of autonomous agents. In this paper, we propose a kinematic-aware information seeking algorithm for a robotic swarm. The algorithm tightly couples state estimation and autonomous control given ranging and kinematic models. With the help of the Fisher information theory, agents generate information seeking command sequence on their actuators, which leads to smooth trajectories. As an outcome, the swarm continuously optimizes its formation so that the agents’ position and orientation uncertainty is actively minimized. The proposed algorithm is verified by physics simulations and demonstrated in a space-analog mission of autonomous swarm navigation on volcano Mount Etna

Cooperative Pose Estimation in a Robotic Swarm: Framework, Simulation and Experimental Results

Swarm robotics has gained an increasing attention in applications like extraterrestrial exploration and disaster management, due to the ability of simultaneously observing at different locations and avoiding a single point of failure. In order to operate autonomously, robots in a swarm need to know their precise poses, including their positions, velocities and orientations. When external navigation infrastructures like the global navigation satellite systems (GNSS) are not ubiquitously accessible, the swarm of robots need to rely on internal measurements to estimate their poses. In this paper, we propose a cooperative 3D pose estimation framework, based on the insights of sensor characteristics that we gained from outdoor swarm navigation experiments. A decentralized particle filter (DPF) operates on each robot to estimate its pose via fusing radio-based ranging, inertial sensor data, control commands and the pose estimates of its neighbors. This framework is integrated in the swarm navigation ecosystem developed at the German Aerospace Center (DLR), and is unified for both simulations and experiments

Cooperative Radio Navigation for Robotic Exploration: Evaluation of a Space-Analogue Mission

Autonomous robotic systems will play an important role in future planetary exploration missions. To allow autonomous operation of robots, reliable navigation is vital. Such a navigation solution is provided by cooperative radio navigation, where radio signals are exchanged among the robots. Based on the signal round-trip time (RTT) and direction-of-arrival (DoA), the robots' positions and orientations are estimated. Cooperative navigation has been well studied theoretically, but experiments mainly focused on indoor scenarios and other applications. For the first time, we have demonstrated cooperative radio navigation within a space-analogue exploration mission with two robotic rovers. The mission took place on the volcano Mt Etna, Sicily, Italy. During the first part of the mission, simultaneous localization and calibration (SLAC) is performed to improve the accuracy of RTT and DoA estimates by reducing the bias. Then, the rovers travel to a distant area of interest. Ultimately, one rover travels so far that it is connected to the network only via another rover. We find that even in this challenging single-link scenario, robust cooperative navigation is achieved. When the rovers are not further than 60 m away from the lander, their position root-mean-square errors (RMSEs) are 0.3m to 0.9m. Even for the most challenging mission phase, when the rovers are 100 m to 160 m away from the lander with single-link localization, the position RMSEs are 1.7m to 2.6m. The orientation RMSEs of the rovers lie between 2.4° to 6.1°. Thus, with this space-analogue mission, we show that cooperative radio navigation for planetary exploration is robust and accurate