Integrated Relative-Measurement-Based Network Localization and Formation Maneuver Control (Extended Version)

This paper studies the problem of integrated distributed network localization and formation maneuver control. We develop an integrated relative-measurement-based scheme, which only uses relative positions, distances, bearings, angles, ratio-of-distances, or their combination to achieve distributed network localization and formation maneuver control in $\mathbb{R}^d (d \ge 2)$. By exploring the localizability and invariance of the target formation, the scale, rotation, and translation of the formation can be controlled simultaneously by only tuning the leaders' positions, i.e., the followers do not need to know parameters of the scale, rotation, and translation of the target formation. The proposed method can globally drive the formation errors to zero in finite time over multi-layer $d\!+\!1$-rooted graphs. A simulation example is given to illustrate the theoretical results.Comment: 12 pages; 7 figures, title corrected, DOI adde

Distributed multi-UAV shield formation based on virtual surface constraints

This paper proposes a method for the deployment of a multi-agent system of unmanned aerial vehicles (UAVs) as a shield with potential applications in the protection of infrastructures. For this purpose, a distributed control law based on the gradient of a potential function is proposed to acquire the desired shield shape, which is modeled as a quadric surface in the 3D space. The graph of the formation is a Delaunay triangulation, which guarantees the formation to be rigid. An algorithm is proposed to design the formation (target distances between agents and interconnections) to distribute the agents over the virtual surface, where the input parameters are just the parametrization of the quadric and the number of agents of the system. Proofs of system stability with the proposed control law are provided, as well as a new method to guarantee that the resulting triangulation over the surface is Delaunay, which can be executed locally. Simulation and experimental results illustrate the effectiveness of the proposed approach

Angle-Displacement Rigidity Theory with Application to Distributed Network Localization

This paper investigates the localization problem of a network in 2-D and 3-D spaces given the positions of anchor nodes in a global frame and inter-node relative measurements in local coordinate frames. It is assumed that the local frames of different nodes have different unknown orientations. First, an angle-displacement rigidity theory is developed, which can be used to localize all the free nodes by the known positions of the anchor nodes and local relative measurements (local relative position, distance, local relative bearing, angle, or ratio-of-distance measurements). Then, necessary and sufficient conditions for network localizability are given. Finally, a distributed network localization protocol is proposed, which can globally estimate the locations of all the free nodes of a network if the network is infinitesimally angle-displacement rigid. The proposed method unifies local-relative-position-based, distance-based, local-relative-bearing-based, angle-based, and ratio-of-distance-based distributed network localization approaches. The novelty of this work is that the proposed method can be applied in both generic and non-generic configurations with an unknown global coordinate frame in both 2-D and 3-D spaces

Localizability and distributed protocols for bearing-based network localization in arbitrary dimensions

This paper addresses the problem of bearing-based network localization, which aims to localize all the nodes in a static network given the locations of a subset of nodes termed anchors and inter-node bearings measured in a common reference frame. The contributions of the paper are twofold. Firstly, we propose necessary and sufficient conditions for network localizability with both algebraic and rigidity theoretic interpretations. Secondly, we propose and analyze a linear distributed protocol for bearing-based network localization. One novelty of our work is that the localizability analysis and localization protocol are applicable to networks in arbitrary dimensional spaces

Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon

Range-Only Node Localization: The Arbitrary Anchor Case In D-Dimensions

This work is situated at the intersection of two large fields of research the Localization problem and applications in Wireless Networks. We are interested in providing good estimations for network node locations in a defined space based on sensor measurements. Many methods have being created for the localization problem, in special we have the classical Triangulation and Trilateration procedures and MultiDimensional Scaling. A more recent method, DILOC, utilizes barycentric coordinates in order to simplify part of the non-linearities inherent to this problem. Except for Triangulation in which we require angle measurements between nodes, the other cited methodologies require, typically only, range measurements. Off course, there exists variants which allow the use of range and angle measurements. We specialize our interest in range only methods utilizing barycentric coordinates by first providing a novel way to compute barycentric coordinates for any possible node-neighbor spatial configuration in any given dimension. Which, we use as basis for our experiments with averaging processes and the development of our centralized and distributed gradient descent algorithms. Our distributed algorithm is able to receive range measurements with noise of uncharacterized distributions as it inputs. Using simulations in Matlab, we provide comparisons of our algorithms with Matlab\u27s MDS function. Lastly, we show our efforts on providing a physical network implementation utilizing existing small form factor computers, wireless communication modules and range sensors

Maneuvering formations of mobile agents using designed mismatched angles

This paper investigates how to maneuver a planar formation of mobile agents using designed mismatched angles. The desired formation shape is specified by a set of interior angle constraints. To realize the maneuver of translation, rotation and scaling of the formation as a whole, we intentionally force the agents to maintain mismatched desired angles by introducing a pair of mismatch parameters for each angle constraint. To allow different information requirements in the design and implementation stages, we consider both measurement-dependent and 10 measurement-independent mismatches. Starting from a triangular formation, we consider generically angle rigid formations that can be constructed from the triangular formation by adding new agents in sequence, each having two angle constraints associated with some existing three agents. The control law for each newly added agent arises naturally from the angle constraints and makes full use of the angle mismatch parameters. We show that the control can effectively stabilize the formations while simultaneously realizing maneuvering. Simulations are conducted to validate the theoretical results