Cooperative Localization of Drones by using Interval Methods

International audienc

Cooperative localization of drones by using interval methods

In this article we address the problem of cooperative pose estimation in a group of unmanned aerial vehicles (UAVs) in a bounded-error context. The UAVs are equipped with cameras to track landmarks, and with a communication and ranging system to cooperate with their neighbors. Measurements are represented by intervals, and constraints are expressed on the robots poses (positions and orientations). Pose domains subpavings are obtained by using set inversion via interval analysis. Each robot of the group first computes a pose domain using only its sensors measurements. Then, through position boxes exchanges, the positions are cooperatively refined by constraint propagation in the group. Results with real robot data are presented, and show that the position accuracy is improved thanks to cooperation

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

A Fog Computing Architecture for Disaster Response Networks

In the aftermath of a disaster, the impacted communication infrastructure is unable to provide first responders with a reliable medium of communication. Delay tolerant networks that leverage mobility in the area have been proposed as a scalable solution that can be deployed quickly. Such disaster response networks (DRNs) typically have limited capacity due to frequent disconnections in the network, and under-perform when saturated with data. On the other hand, there is a large amount of data being produced and consumed due to the recent popularity of smartphones and the cloud computing paradigm. Fog Computing brings the cloud computing paradigm to the complex environments that DRNs operate in. The proposed architecture addresses the key challenges of ensuring high situational awareness and energy efficiency when such DRNs are saturated with large amounts of data. Situational awareness is increased by providing data reliably, and at a high temporal and spatial resolution. A waypoint placement algorithm places hardware in the disaster struck area such that the aggregate good-put is maximized. The Raven routing framework allows for risk-averse data delivery by allowing the user to control the variance of the packet delivery delay. The Pareto frontier between performance and energy consumption is discovered, and the DRN is made to operate at these Pareto optimal points. The FuzLoc distributed protocol enables mobile self-localization in indoor environments. The architecture has been evaluated in realistic scenarios involving deployments of multiple vehicles and devices

Mobile robot localization by multiangulation using set inversion

International audienceThis work is about solving the global localization issue for mobile robots operating in large and cooperative environments. It tackles the problem of estimating the pose of a robot in the environment using real-time data either from the robot on-board sensors or/and from the sensors in the environment or/and the real-time data coming from other robots. The paper focuses on the 3-DOF localization of a mobile robot that is to say the estimation of the robot coordinates (xmr, ymr , θmr) in a 2D-environment. The interest of this method lies on the ability to easily integrate a large variety of sensors, from the roughest to the most complex one. The method takes into account the following constraints: a flexible number of measurements, generic goniometric measurements, a statistical knowledge on the measurements limited to the tolerance, and the fact the measurements are acquired both from the robot onboard sensors and the environment sensors. The approach is able to integrate an heterogeneous set of measurements not only generic goniometric measurements but also range, position given by a tactile tile, complex shape, dead reckoning measurements. The way that outliers and environment model inaccuracies can be taken into account is described. The problem of nonlinear bounded-error estimation is viewed as a set inversion. The paper presents the theoretical formulation of the localization method in a bounded-error context and the parameter estimation based on interval analysis. Simulation results as well as real experiments show the interest of the method in a cooperative environment context