272 research outputs found
Evaluation of the Benefits of Zero Velocity Update in Decentralized EKF-Based Cooperative Localization Algorithms for GNSS-Denied Multi-Robot Systems
This paper proposes the cooperative use of zero velocity update (ZU) in a
decentralized extended Kalman filter (DEKF) based localization algorithm for
multi-robot systems. The filter utilizes inertial measurement unit (IMU),
ultra-wideband (UWB), and odometry velocity measurements to improve the
localization performance of the system in the presence of a GNSS-denied
environment. The contribution of this work is to evaluate the benefits of using
ZU in a DEKF-based localization algorithm. The algorithm is tested with real
hardware in a video motion capture facility and a Robot Operating System (ROS)
based simulation environment for unmanned ground vehicles (UGV). Both
simulation and real-world experiments are performed to show the effectiveness
of using ZU in one robot to reinstate the localization of other robots in a
multi-robot system. Experimental results from GNSS-denied simulation and
real-world environments show that using ZU with simple heuristics in the DEKF
significantly improves the 3D localization accuracy.Comment: 18 pages, preprint version, the manuscript is accepted for
publication in NAVIGATION, the Journal of the Institute of Navigation.
Submitted:10-11-2022, Revised: 21-04-2023, Accepted:23-06-202
On-manifold Decentralized State Estimation using Pseudomeasurements and Preintegration
This paper addresses the problem of decentralized, collaborative state
estimation in robotic teams. In particular, this paper considers problems where
individual robots estimate similar physical quantities, such as each other's
position relative to themselves. The use of \emph{pseudomeasurements} is
introduced as a means of modelling such relationships between robots' state
estimates, and is shown to be a tractable way to approach the decentralized
state estimation problem. Moreover, this formulation easily leads to a
general-purpose observability test that simultaneously accounts for
measurements that robots collect from their own sensors, as well as the
communication structure within the team. Finally, input preintegration is
proposed as a communication-efficient way of sharing odometry information
between robots, and the entire theory is appropriate for both vector-space and
Lie-group state definitions. The proposed framework is evaluated on three
different simulated problems, and one experiment involving three quadcopters.Comment: 15 pages, 13 figures, submitted to IEE
Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape
Motivated by the tremendous progress we witnessed in recent years, this paper
presents a survey of the scientific literature on the topic of Collaborative
Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM.
With fleets of self-driving cars on the horizon and the rise of multi-robot
systems in industrial applications, we believe that Collaborative SLAM will
soon become a cornerstone of future robotic applications. In this survey, we
introduce the basic concepts of C-SLAM and present a thorough literature
review. We also outline the major challenges and limitations of C-SLAM in terms
of robustness, communication, and resource management. We conclude by exploring
the area's current trends and promising research avenues.Comment: 44 pages, 3 figure
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
A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions
Several interesting problems in multi-robot systems can be cast in the
framework of distributed optimization. Examples include multi-robot task
allocation, vehicle routing, target protection and surveillance. While the
theoretical analysis of distributed optimization algorithms has received
significant attention, its application to cooperative robotics has not been
investigated in detail. In this paper, we show how notable scenarios in
cooperative robotics can be addressed by suitable distributed optimization
setups. Specifically, after a brief introduction on the widely investigated
consensus optimization (most suited for data analytics) and on the
partition-based setup (matching the graph structure in the optimization), we
focus on two distributed settings modeling several scenarios in cooperative
robotics, i.e., the so-called constraint-coupled and aggregative optimization
frameworks. For each one, we consider use-case applications, and we discuss
tailored distributed algorithms with their convergence properties. Then, we
revise state-of-the-art toolboxes allowing for the implementation of
distributed schemes on real networks of robots without central coordinators.
For each use case, we discuss their implementation in these toolboxes and
provide simulations and real experiments on networks of heterogeneous robots
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