A large number of missions to the Moon is planned in the coming years by both, public and private sectors. A high demand for a Lunar position, navigation and timing (PNT) system exists to aid landing, to support autonomous robotic exploration etc. ESA has proposed a satellite based system for communication and PNT. Furthermore, cooperative radio navigation for users on the Lunar surface, where PNT is provided through radio signals exchanged among robots and other entities, has been suggested. An analysis of the combination of satellite and cooperative navigation for Lunar PNT has not been done yet. In this paper, we provide a first estimation theoretic analysis of joint Lunar satellite and cooperative surface PNT. We derive the Bayesian Cramér-Rao bound (BCRB) and analyze the benefit of cooperation by comparing different cooperation strategies and scenarios, using system parameters from recent studies.

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 networked autonomous agents. In this paper, we propose a kinematics-aware information seeking algorithm for swarm navigation. 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 sequences. As an outcome, the swarm continuously optimizes its trajectory so that the agents’ position and orientation uncertainty is actively minimized. The proposed algorithm is verified by large-scale swarm simulations and demonstrated in a space-analogue mission of autonomous swarm navigation on the volcano Mount Etna.

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 directions-of-arrival (DoAs) 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 DoAs 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.

Autonomous robotic swarms have attracted increasing attention in recent years due to their enormous, principally unlimited, capability expansion from single robot systems. A communication system, as the `glue' in a swarm, plays an essential role that goes beyond conventional data exchange into localization, sensing, control, etc. Each building block of a swarm can be rooted from specific well-addressed research topic such as communications, signal processing, robotics, control, etc. However, an interdisciplinary view on swarm communication system, specially its impacts on other sub-systems, is still missing. In this paper, we share our experience in the assembling of a radio-based swarm system, from module design to system integration, from theoretical investigation to experimental platform development. With that we aim to shed light on some essential issues when designing a communication system particularly suitable for swarms.

A multitude of autonomous robotic platforms collectively organized as a swarm attracts increasing attention for remote sensing and exploration tasks. A navigation system is essential for the swarm to collectively localize itself as well as external sources. In this article, we propose a self-aware swarm navigation system that is conscious of the causality between its position and the localization uncertainty. This knowledge allows the swarm to move in a way to not only account for external mission objectives but also enhance position information. Position information for classical navigation systems has already been studied with the Fisher information (FI) and Bayesian information (BI) theories. We show how to extend these theories to a self-aware swarm navigation system, particularly emphasizing the collective performance. In this respect, fundamental limits and geometric interpretations of localization with generic observation models are discussed. We further propose a general concept of FI and BI based information seeking swarm control. The weighted position Cramér-Rao bound (CRB) and posterior CRB (PCRB) are employed flexibly as either a control cost function or constraints according to different mission criteria. As a result, the swarm actively adapts its position to enrich position information with different emerging collective behaviors. The proposed concept is illustrated by a case study of a swarm mission for gas exploration on Mars.