Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling

We consider cooperative localization technique for mobile agents with communication and computation capabilities. We start by provide and overview of different decentralization strategies in the literature, with special focus on how these algorithms maintain an account of intrinsic correlations between state estimate of team members. Then, we present a novel decentralized cooperative localization algorithm that is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each agent propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the agent making this measurement as the interim master. By acquiring information from the interim landmark, the agent the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement

Cooperative Localization under Limited Connectivity

We report two decentralized multi-agent cooperative localization algorithms in which, to reduce the communication cost, inter-agent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown inter-agent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown inter-agent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic~experiment.Comment: 9 pages, 5 figure

Task Allocation and Collaborative Localisation in Multi-Robot Systems

To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation. The order that tasks are completed in is often important, and makes task allocation difficult to solve (40 tasks have 1047 different ways of completing them). Algorithms in literature range from fast methods that provide reasonable allocations, to slower methods that can provide optimal allocations. These algorithms work well for systems with identical robots, but do not utilise robot differences for superior allocations when robots are non-identical. They also can not be applied to robots that can use different tools, where they must consider which tools to use for each task. Robot localisation is performed using sensors which are often assumed to always be available. This is not the case in GPS-denied environments such as tunnels, or on long-range missions where replacement sensors are not readily available. A promising method to overcome this is collaborative localisation, where robots observe one another to improve their location estimates. There has been little research on what robot properties make collaborative localisation most effective, or how to tune systems to make it as accurate as possible. Most task allocation algorithms do not consider localisation as part of the allocation process. If task allocation algorithms limited inter-robot distance, collaborative localisation can be performed during task completion. Such an algorithm could equally be used to ensure robots are within communication distance, and to quickly detect when a robot fails. While some algorithms for this exist in literature, they provide a weak guarantee of inter-robot distance, which is undesirable when applied to real robots. The aim of this thesis is to improve upon task allocation algorithms by increasing task allocation speed and efficiency, and supporting robot tool changes. Collaborative localisation parameters are analysed, and a task allocation algorithm that enables collaborative localisation on real robots is developed. This thesis includes a compendium of journal articles written by the author. The four articles forming the main body of the thesis discuss the multi-robot task allocation and localisation research during the author’s candidature. Two appendices are included, representing conference articles written by the author that directly relate to the thesis.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Construction d'une carte coopérative dans les réseaux véhiculaires

International audienceDans les réseaux véhiculaires, l’identification des voisins est le point de départ de nombreuses applications. De même, ladécouverte des voisins à plusieurs sauts est nécessaire pour de nombreuses applications ITS coopératives. Cependant,en raison de la dynamique de ces réseaux, cette tâche n’est pas simple. Généralement, les informations relatives auxnœuds deviennent rapidement obsolètes. En plus, elles peuvent avoir été transmises par des nœuds non fiables. Dans cecontexte, un nœud doit évaluer la confiance qu’il a dans l’information reçue. Nous proposons un algorithme distribuépour la construction coopérative d’une carte fournissant les coordonnées des nœuds et les services disponibles dans levoisinage jusqu’à n sauts. La carte comprend également une estimation de la confiance dans les informations collectéesainsi que la fiabilité des chemins vers les services découverts. Les expériences par émulation de réseau démontrentl’intérêt de notre approche. Elle devrait permettre de sélectionner des vehicules pertinents dans le cadre d’applicationsITS coopératives

Leader-assisted localization approach for a heterogeneous multi-robot system

This thesis presents the design, implementation, and validation of a novel leader assisted localization framework for a heterogeneous multi-robot system (MRS) with sensing and communication range constraints. It is assumed that the given heterogeneous MRS has a more powerful robot (or group of robots) with accurate self localization capabilities (leader robots) while the rest of the team (child robots), i.e. less powerful robots, is localized with the assistance of leader robots and inter-robot observation between teammates. This will eventually pose a condition that the child robots should be operated within the sensing and communication range of leader robots. The bounded navigation space therefore may require added algorithms to avoid inter-robot collisions and limit robots’ maneuverability. To address this limitation, first, the thesis introduces a novel distributed graph search and global pose composition algorithm to virtually enhance the leader robots’ sensing and communication range while avoiding possible double counting of common information. This allows child robots to navigate beyond the sensing and communication range of the leader robot, yet receive localization services from the leader robots. A time-delayed measurement update algorithm and a memory optimization approach are then integrated into the proposed localization framework. This eventually improves the robustness of the algorithm against the unknown processing and communication time-delays associated with the inter-robot data exchange network. Finally, a novel hierarchical sensor fusion architecture is introduced so that the proposed localization scheme can be implemented using inter-robot relative range and bearing measurements. The performance of the proposed localization framework is evaluated through a series of indoor experiments, a publicly available multi-robot localization and mapping data-set and a set of numerical simulations. The results illustrate that the proposed leader-assisted localization framework is capable of establishing accurate and nonoverconfident localization for the child robots even when the child robots operate beyond the sensing and communication boundaries of the leader robots

Data incest in cooperative localisation with the common past-invariant ensemble Kalman filter

In this paper we consider the problem of cooperative vehicle localisation, in which a group of vehicles are driving in an outdoor environment, each estimating their position using a global positioning system (GPS) and odometry. Additionally, the vehicles can improve their estimates by observing positions of other vehicles using a proximity sensor, such as a radar, and a mutual communication, which is especially helpful to those vehicles operating in areas with no GPS coverage. In a distributed fusion system, each vehicle needs to account for the fact that information received from other vehicles might originate in part from the vehicle itself, resulting in a correlation between the state estimate and observation errors. This problem, also known as data incest, is amplified by the dynamic and unstructured nature of the communication topology, inherent to a cooperative localisation scenario. We provide a novel solution to the problem based on the Common Past-Invariant Ensemble Kalman filter (CPI-EnKF) - a generalisation of the Ensemble Kalman filter that can be applied in the presence of common past information shared between the state estimate and the observation, which has been recently proposed by this paper’s authors. As we will demonstrate, the CPI-EnKF is simpler to apply, provides better estimates, can be scaled to an arbitrary number of vehicles and is computationally more efficient than other similar methods