Self-Aware Swarm Navigation in Autonomous Exploration Missions

Zhang, Siwei; Pöhlmann, Robert; Wiedemann, Thomas; Dammann, Armin; Wymeersch, Henk; Hoeher, Peter Adam

doi:10.1109/jproc.2020.2985950

Cited by 46 publications

(55 citation statements)

References 99 publications

(158 reference statements)

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“…We studied the joint estimation of LOFAR box location and LOFAR signal direction of arrival (DoA) in [38]. Our LOFAR setup targets signal reception at 20 MHz carrier frequency, i. e., a wavelength of 15 m. Accurate beam steering in azimuth and elevation requires an array localization accuracy of at least one order of magnitude higher than the carrier wavelength, which is 1.5 m for our system.…”

Section: Radio-based Localization (Radio-cpt)mentioning

confidence: 99%

The ARCHES Space-Analogue Demonstration Mission: Towards Heterogeneous Teams of Autonomous Robots for Collaborative Scientific Sampling in Planetary Exploration

Schuster

Müller

Brunner

et al. 2020

IEEE Robot. Autom. Lett.

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Teams of mobile robots will play a crucial role in future missions to explore the surfaces of extraterrestrial bodies. Setting up infrastructure and taking scientific samples are expensive tasks when operating in distant, challenging, and unknown environments. In contrast to current single-robot space missions, future heterogeneous robotic teams will increase efficiency via enhanced autonomy and parallelization, improve robustness via functional redundancy, as well as benefit from complementary capabilities of the individual robots. In this article, we present our heterogeneous robotic team, consisting of flying and driving robots that we plan to deploy on scientific sampling demonstration missions at a Moon-analogue site on Mt. Etna, Sicily, Italy in 2021 as part of the ARCHES project. We describe the robots' individual capabilities and their roles in two mission scenarios. We then present components and experiments on important tasks therein: automated task planning, high-level mission control, spectral rock analysis, radio-based localization, collaborative multi-robot 6D SLAM in Moon-analogue and Marslike scenarios, and demonstrations of autonomous sample return.

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Section: Radio-based Localization (Radio-cpt)mentioning

confidence: 99%

The ARCHES Space-Analogue Demonstration Mission: Towards Heterogeneous Teams of Autonomous Robots for Collaborative Scientific Sampling in Planetary Exploration

Schuster

Müller

Brunner

et al. 2020

IEEE Robot. Autom. Lett.

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“…One possibility to solve the minimization problem in (6) is to use a numerical approach. In the case study, we adopted the projection gradient method [39], [53]…”

Section: Navigation Algorithmmentioning

confidence: 99%

“…im , (39) where N MC is the number of Monte Carlo iterations, K is the number of time steps, 1 (x) is the unit step function that is equal to 1 if x ≥ 0 and 0 otherwise, e , and e th is a localization threshold.…”

Section: Case Studymentioning

confidence: 99%

“…Citation information: DOI 10.1109/ACCESS.2020.3001393, IEEE Access In Fig. 5, we provide the success rate evaluated as in (39) by varying the number of UAVs and the sensing capabilities. A localization error lower than 1 m can be achieved in nearly 80% of the cases with N = 4 UAVs with either a reference ranging accuracy of 10 −2 m or a bearing accuracy of 5 • degrees.…”

Section: Case Studymentioning

confidence: 99%

“…In regard to control design, many works in the literature have focused on optimal sensor/anchor placement [26], [38], while others tackle the problem from an optimal control point-of-view [37]. Among other approaches, information-seeking optimal control (e.g., strategies driven by Shannon or Fisher information measures) has been extensively investigated for localization and tracking applications [29]- [31], [33]- [36], [38], [39]. For example, an analysis of the best network geometry was performed in [38], The analysis was based on a Fisher information metric for a team of collaborative UAVs used to track on-ground targets, but, in this solution, the target estimation algorithm and the trajectory design were not optimized together with the swarm formation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

2020

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Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations.

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Cooperative behavior of a heterogeneous robot team for planetary exploration using deep reinforcement learning

Barth,

2024

Acta Astronautica

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Self-Aware Swarm Navigation in Autonomous Exploration Missions

Abstract: This article presents methods for incorporating self-awareness into a swarm navigation system through a case study.

Cited by 46 publications

References 99 publications

The ARCHES Space-Analogue Demonstration Mission: Towards Heterogeneous Teams of Autonomous Robots for Collaborative Scientific Sampling in Planetary Exploration

The ARCHES Space-Analogue Demonstration Mission: Towards Heterogeneous Teams of Autonomous Robots for Collaborative Scientific Sampling in Planetary Exploration

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

Cooperative behavior of a heterogeneous robot team for planetary exploration using deep reinforcement learning

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