Distributed data fusion algorithms for inertial network systems

New approaches to the development of data fusion algorithms for inertial network systems are described. The aim of this development is to increase the accuracy of estimates of inertial state vectors in all the network nodes, including the navigation states, and also to improve the fault tolerance of inertial network systems. An analysis of distributed inertial sensing models is presented and new distributed data fusion algorithms are developed for inertial network systems. The distributed data fusion algorithm comprises two steps: inertial measurement fusion and state fusion. The inertial measurement fusion allows each node to assimilate all the inertial measurements from an inertial network system, which can improve the performance of inertial sensor failure detection and isolation algorithms by providing more information. The state fusion further increases the accuracy and enhances the integrity of the local inertial states and navigation state estimates. The simulation results show that the two-step fusion procedure overcomes the disadvantages of traditional inertial sensor alignment procedures. The slave inertial nodes can be accurately aligned to the master node

On-barn pig weight estimation based on body measurements by structure-from-motion (SfM)

Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized

A Decentralized Mobile Computing Network for Multi-Robot Systems Operations

Collective animal behaviors are paradigmatic examples of fully decentralized operations involving complex collective computations such as collective turns in flocks of birds or collective harvesting by ants. These systems offer a unique source of inspiration for the development of fault-tolerant and self-healing multi-robot systems capable of operating in dynamic environments. Specifically, swarm robotics emerged and is significantly growing on these premises. However, to date, most swarm robotics systems reported in the literature involve basic computational tasks---averages and other algebraic operations. In this paper, we introduce a novel Collective computing framework based on the swarming paradigm, which exhibits the key innate features of swarms: robustness, scalability and flexibility. Unlike Edge computing, the proposed Collective computing framework is truly decentralized and does not require user intervention or additional servers to sustain its operations. This Collective computing framework is applied to the complex task of collective mapping, in which multiple robots aim at cooperatively map a large area. Our results confirm the effectiveness of the cooperative strategy, its robustness to the loss of multiple units, as well as its scalability. Furthermore, the topology of the interconnecting network is found to greatly influence the performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conferenc

Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks

Distributed signal processing for wireless sensor networks enables that different devices cooperate to solve different signal processing tasks. A crucial first step is to answer the question: who observes what? Recently, several distributed algorithms have been proposed, which frame the signal/object labelling problem in terms of cluster analysis after extracting source-specific features, however, the number of clusters is assumed to be known. We propose a new method called Gravitational Clustering (GC) to adaptively estimate the time-varying number of clusters based on a set of feature vectors. The key idea is to exploit the physical principle of gravitational force between mass units: streaming-in feature vectors are considered as mass units of fixed position in the feature space, around which mobile mass units are injected at each time instant. The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i.e., gravitational clusters. By sharing estimates among neighboring nodes via a diffusion-adaptation scheme, cooperative and distributed cluster enumeration is achieved. Numerical experiments concerning robustness against outliers, convergence and computational complexity are conducted. The application in a distributed cooperative multi-view camera network illustrates the applicability to real-world problems.Comment: 12 pages, 9 figure

Adaptive Synchronization of Robotic Sensor Networks

The main focus of recent time synchronization research is developing power-efficient synchronization methods that meet pre-defined accuracy requirements. However, an aspect that has been often overlooked is the high dynamics of the network topology due to the mobility of the nodes. Employing existing flooding-based and peer-to-peer synchronization methods, are networked robots still be able to adapt themselves and self-adjust their logical clocks under mobile network dynamics? In this paper, we present the application and the evaluation of the existing synchronization methods on robotic sensor networks. We show through simulations that Adaptive Value Tracking synchronization is robust and efficient under mobility. Hence, deducing the time synchronization problem in robotic sensor networks into a dynamic value searching problem is preferable to existing synchronization methods in the literature.Comment: First International Workshop on Robotic Sensor Networks part of Cyber-Physical Systems Week, Berlin, Germany, 14 April 201