Multi-agents adaptive estimation and coverage control using Gaussian regression

We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function. In particular, centroidal Voronoi partitions have to be computed. The difficulty of the task is that the sensory function is unknown and has to be reconstructed on line from noisy measurements. Hence, estimation and coverage needs to be performed at the same time. We cast the problem in a Bayesian regression framework, where the sensory function is seen as a Gaussian random field. Then, we design a set of control inputs which try to well balance coverage and estimation, also discussing convergence properties of the algorithm. Numerical experiments show the effectivness of the new approach

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations

A new Measure for Optimization of Field Sensor Network with Application to LiDAR

This thesis proposes a solution to the problem of modeling and optimizing the field sensor network in terms of the coverage performance. The term field sensor is referred to a class of sensors which can detect the regions in 2D/3D spaces through non-contact measurements. The most widely used field sensors include cameras, LiDAR, ultrasonic sensor, and RADAR, etc. The key challenge in the applications of field sensor networks, such as area coverage, is to develop an effective performance measure, which has to involve both sensor and environment parameters. The nature of space distribution in the case of the field sensor incurs a great deal of difficulties for such development and, hence, poses it as a very interesting research problem. Therefore, to tackle this problem, several attempts have been made in the literature. However, they have failed to address a comprehensive and applicable approach to distinctive types of field sensors (in 3D), as only coverage of a particular sensor is usually addressed at the time. In addition, no coverage model has been proposed yet for some types of field sensors such as LiDAR sensors. In this dissertation, a coverage model is obtained for the field sensors based on the transformation of sensor and task parameters into the sensor geometric model. By providing a mathematical description of the sensor’s sensing region, a performance measure is introduced which characterizes the closeness between a single sensor and target configurations. In this regard, the first contribution is developing an Infinity norm based measure which describes the target distance to the closure of the sensing region expressed by an area-based approach. The second contribution can be geometrically interpreted as mapping the sensor’s sensing region to an n-ball using a homeomorphism map and developing a performance measure. The third contribution is introducing the measurement principle and establishing the coverage model for the class of solid-state (flash) LiDAR sensors. The fourth contribution is point density analysis and developing the coverage model for the class of mechanical (prism rotating mechanism) LiDAR sensors. Finally, the effectiveness of the proposed coverage model is illustrated by simulations, experiments, and comparisons is carried out throughout the dissertation. This coverage model is a powerful tool as it applies to the variety of field sensors

Robotic Searching for Stationary, Unknown and Transient Radio Sources

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments

Placement interactif de capteurs mobiles dans des environnements tridimensionnels non convexes

La présente thèse propose un système complet de placement de capteurs mobiles dans un environnement pleinement tridimensionnel et préalablement inconnu. Les capteurs mobiles sont des capteurs placés sur des unités robotiques autonomes, soit des véhicules possédant une unité de calcul et pouvant se déplacer dans l’environnement. Le placement de capteur est fondé sur une vue désirée par un utilisateur du système nommé vue virtuelle. La vue virtuelle est contrôlée à distance en changeant les paramètres intrinsèques et extrinsèques du capteur virtuel, soit sa position, sa résolution, son champ de vue, etc. Le capteur virtuel n’est alors soumis à aucune contrainte physique, par exemple il peut être placé à n’importe quelle hauteur dans l’environnement et avoir un champ de vue et une résolution arbitrairement grande. Les capteurs mobiles (réels) ont pour tâche de récupérer toute l’information contenue dans le point de vue virtuel. Ce n’est qu’en combinant leur capacité sensorielle que les capteurs mobiles pourront capter l’information demandée par l’utilisateur. Tout d’abord, cette thèse s’attaque au problème de placement de capteurs en définissant une fonction de visibilité servant à évaluer le positionnement d’un groupe de capteurs dans l’environnement. La fonction de visibilité développée est applicable aux environnements tridimensionnels et se base sur le principe de ligne de vue directe entre un capteur et la cible. De plus, la fonction prend en compte la densité d’échantillonnage des capteurs afin de reproduire la densité désirée indiquée par le capteur virtuel. Ensuite, ce travail propose l’utilisation d’un modèle de l’environnement pleinement tridimensionnel et pouvant être construit de manière incrémentale, rendant son utilisation possible dans un environnement tridimensionnel non convexe préalablement inconnu. Puis, un algorithme d’optimisation coopératif est présenté afin de trouver simultanément le nombre de capteurs et leur positionnement respectif afin d’acquérir l’information contenue dans la vue virtuelle. Finalement, la thèse démontre expérimentalement dans diverses conditions que le système proposé est supérieur à l’état de l’art pour le placement de capteurs dans le but d’observer une scène bidimensionnelle. Il est aussi établi expérimentalement en simulation et en réalité que les performances se transposent à l’observation d’environnements tridimensionnels non convexes préalablement inconnus.This Thesis proposes a novel mobile sensor placement system working in initially unknown three dimensional environment. The mobile sensors are fix sensors placed on autonomous robots, which are ground and aerial vehicles equipped with computing units. The sensor placement is based on a user-defined view, named the virtual view. This view is manipulated through a virtual sensor intrinsic and extrinsic parameters, such as its position, orientation, field of view, resolution, etc. The virtual sensor is not subject to any physical constraint, for example it can be place where no sensor could be or it possess an arbitrary large field of view and resolution. The mobile (real) sensors have to acquire the entire information contained in this virtual view. It is only by combining the sensory capacity of an unknown number of sensors that they can acquire the necessary information. First, this Thesis addresses the sensor placement problem by defining a visibility function to qualify a group of sensor configurations in the environment. This function is applicable to three dimensional environments and is based on direct line of sight principle, where we compute the sensor sampling density in its visibility region. Then, this Thesis proposes the use of an incrementally built model of the environment containing all the information needed by the objective function. Next, a cooperative optimization algorithm is put forward to simultaneously find the number of sensors and their respective position required to capture all the information in the virtual view. Finally, the proposed system is experimentally shown to use less sensor to acquire the scene of interest at a higher resolution than state of the art methods in initially known two dimensional environments. It is also shown in simulation and practice that the performance of the system can be transposed to initially unknown non-convex three dimensional environments