25,822 research outputs found
IR sensors array for robots localization using K means clustering algorithm
The position of multi-robot system in an indoor localization system is successfully estimated using a new algorithm. The localization problem is resolved by using an array of IR receiver sensors distributed uniformly in the environment. The necessary information about the localization development is collected by scanning the IR sensor array in the environment. The scheme of scanning process is done column by column to recognize and mention the position of IR receiver’s sensors, which received signals from the IR transmitter that is fixed on the robot. This principle of scanning helps to minimize the required time for robot localization. The k-means clustering algorithm is used to estimate the multi-robot locations by isolating the labeled IR receivers into clusters. Basically the multi-robot position is estimated to be the middle of each cluster. Simulation results demonstrate the advances algorithm in estimation the multi-robot positions for various dimensional IR receiver’s array
DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems
An optimization problem is at the heart of many robotics estimating,
planning, and optimum control problems. Several attempts have been made at
model-based multi-robot localization, and few have formulated the multi-robot
collaborative localization problem as a factor graph problem to solve through
graph optimization. Here, the optimization objective is to minimize the errors
of estimating the relative location estimates in a distributed manner. Our
novel graph-theoretic approach to solving this problem consists of three major
components; (connectivity) graph formation, expansion through transition model,
and optimization of relative poses. First, we estimate the relative
pose-connectivity graph using the received signal strength between the
connected robots, indicating relative ranges between them. Then, we apply a
motion model to formulate graph expansion and optimize them using go graph
optimization as a distributed solver over dynamic networks. Finally, we
theoretically analyze the algorithm and numerically validate its optimality and
performance through extensive simulations. The results demonstrate the
practicality of the proposed solution compared to a state-of-the-art algorithm
for collaborative localization in multi-robot systems.Comment: Preprint of the Paper Accepted to DARS 202
GPRL: Gaussian Processes-Based Relative Localization for Multi-Robot Systems
Relative localization is crucial for multi-robot systems to perform
cooperative tasks, especially in GPS-denied environments. Current techniques
for multi-robot relative localization rely on expensive or short-range sensors
such as cameras and LIDARs. As a result, these algorithms face challenges such
as high computational complexity, dependencies on well-structured environments,
etc. To overcome these limitations, we propose a new distributed approach to
perform relative localization using a Gaussian Processes map of the Radio
Signal Strength Indicator (RSSI) values from a single wireless Access Point
(AP) to which the robots are connected. Our approach, Gaussian Processes-based
Relative Localization (GPRL), combines two pillars. First, the robots locate
the AP w.r.t. their local reference frames using novel hierarchical inferencing
that significantly reduces computational complexity. Secondly, the robots
obtain relative positions of neighbor robots with an AP-oriented vector
transformation. The approach readily applies to resource-constrained devices
and relies only on the ubiquitously-available RSSI measurement. We extensively
validate the performance of the two pillars of the proposed GRPL in Robotarium
simulations. We also demonstrate the applicability of GPRL through a
multi-robot rendezvous task with a team of three real-world robots. The results
demonstrate that GPRL outperformed state-of-the-art approaches regarding
accuracy, computation, and real-time performance
SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems
The availability of accurate localization is critical for multi-robot
exploration strategies; noisy or inconsistent localization causes failure in
meeting exploration objectives. We aim to achieve high localization accuracy
with contemporary exploration map belief and vice versa without needing global
localization information. This paper proposes a novel simultaneous exploration
and localization (SEAL) approach, which uses Gaussian Processes (GP)-based
information fusion for maximum exploration while performing communication graph
optimization for relative localization. Both these cross-dependent objectives
were integrated through the Rao-Blackwellization technique. Distributed
linearized convex hull optimization is used to select the next-best unexplored
region for distributed exploration. SEAL outperformed cutting-edge methods on
exploration and localization performance in extensive ROS-Gazebo simulations,
illustrating the practicality of the approach in real-world applications.Comment: Accepted to IROS 202
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
This paper presents Kimera-Multi, the first multi-robot system that (i) is
robust and capable of identifying and rejecting incorrect inter and intra-robot
loop closures resulting from perceptual aliasing, (ii) is fully distributed and
only relies on local (peer-to-peer) communication to achieve distributed
localization and mapping, and (iii) builds a globally consistent
metric-semantic 3D mesh model of the environment in real-time, where faces of
the mesh are annotated with semantic labels. Kimera-Multi is implemented by a
team of robots equipped with visual-inertial sensors. Each robot builds a local
trajectory estimate and a local mesh using Kimera. When communication is
available, robots initiate a distributed place recognition and robust pose
graph optimization protocol based on a novel distributed graduated
non-convexity algorithm. The proposed protocol allows the robots to improve
their local trajectory estimates by leveraging inter-robot loop closures while
being robust to outliers. Finally, each robot uses its improved trajectory
estimate to correct the local mesh using mesh deformation techniques.
We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking
datasets, and challenging outdoor datasets collected using ground robots. Both
real and simulated experiments involve long trajectories (e.g., up to 800
meters per robot). The experiments show that Kimera-Multi (i) outperforms the
state of the art in terms of robustness and accuracy, (ii) achieves estimation
errors comparable to a centralized SLAM system while being fully distributed,
(iii) is parsimonious in terms of communication bandwidth, (iv) produces
accurate metric-semantic 3D meshes, and (v) is modular and can be also used for
standard 3D reconstruction (i.e., without semantic labels) or for trajectory
estimation (i.e., without reconstructing a 3D mesh).Comment: Accepted by IEEE Transactions on Robotics (18 pages, 15 figures
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Data-Efficient Decentralized Visual SLAM
Decentralized visual simultaneous localization and mapping (SLAM) is a
powerful tool for multi-robot applications in environments where absolute
positioning systems are not available. Being visual, it relies on cameras,
cheap, lightweight and versatile sensors, and being decentralized, it does not
rely on communication to a central ground station. In this work, we integrate
state-of-the-art decentralized SLAM components into a new, complete
decentralized visual SLAM system. To allow for data association and
co-optimization, existing decentralized visual SLAM systems regularly exchange
the full map data between all robots, incurring large data transfers at a
complexity that scales quadratically with the robot count. In contrast, our
method performs efficient data association in two stages: in the first stage a
compact full-image descriptor is deterministically sent to only one robot. In
the second stage, which is only executed if the first stage succeeded, the data
required for relative pose estimation is sent, again to only one robot. Thus,
data association scales linearly with the robot count and uses highly compact
place representations. For optimization, a state-of-the-art decentralized
pose-graph optimization method is used. It exchanges a minimum amount of data
which is linear with trajectory overlap. We characterize the resulting system
and identify bottlenecks in its components. The system is evaluated on publicly
available data and we provide open access to the code.Comment: 8 pages, submitted to ICRA 201
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
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