221 research outputs found
Acoustic emission localization on ship hull structures using a deep learning approach
In this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid
LiDAR is currently one of the most utilized sensors to effectively monitor
the status of power lines and facilitate the inspection of remote power
distribution networks and related infrastructures. To ensure the safe operation
of the smart grid, various remote data acquisition strategies, such as Airborne
Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser
Scanning (TSL) have been leveraged to allow continuous monitoring of regional
power networks, which are typically surrounded by dense vegetation. In this
article, an unsupervised Machine Learning (ML) framework is proposed, to
detect, extract and analyze the characteristics of power lines of both high and
low voltage, as well as the surrounding vegetation in a Power Line Corridor
(PLC) solely from LiDAR data. Initially, the proposed approach eliminates the
ground points from higher elevation points based on statistical analysis that
applies density criteria and histogram thresholding. After denoising and
transforming of the remaining candidate points by applying Principle Component
Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a
two-stage DBSCAN clustering to identify each power line individually. Finally,
all high elevation points in the PLC are identified based on their distance to
the newly segmented power lines. Conducted experiments illustrate that the
proposed framework is an agnostic method that can efficiently detect the power
lines and perform PLC-based hazard analysis.Comment: Accepted in the 22nd World Congress of the International Federation
of Automatic Control [IFAC WC 2023
Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems
Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean
(Sub-T) environments in the presence of aerosol particles have recently become
the main focus in the field of robotics. Aerosol particles such as smoke and
dust directly affect the performance of any mobile robotic platform due to
their reliance on their onboard perception systems for autonomous navigation
and localization in Global Navigation Satellite System (GNSS)-denied
environments. Although obstacle avoidance and object detection algorithms are
robust to the presence of noise to some degree, their performance directly
relies on the quality of captured data by onboard sensors such as Light
Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel
modular agnostic filtration pipeline based on intensity and spatial information
such as local point density for removal of detected smoke particles from Point
Cloud (PCL) prior to its utilization for collision detection. Furthermore, the
efficacy of the proposed framework in the presence of smoke during multiple
frontier exploration missions is investigated while the experimental results
are presented to facilitate comparison with other methodologies and their
computational impact. This provides valuable insight to the research community
for better utilization of filtration schemes based on available computation
resources while considering the safe autonomous navigation of mobile robots.Comment: Accepted in the 49th Annual Conference of the IEEE Industrial
Electronics Society [IECON2023
Reactive Task Allocation for Balanced Servicing of Multiple Task Queues
In this article, we propose a reactive task allocation architecture for a
multi-agent system for scenarios where the tasks arrive at random times and are
grouped into multiple queues. Two stage tasks are considered where every task
has a beginning, an intermediate and a final part, typical in pick-and-drop and
inspect-and-report scenarios. A centralized auction-based task allocation
system is proposed, where an auction system takes into consideration bids
submitted by the agents for individual tasks, current length of the queues and
the waiting times of the tasks in the queues to decide on a task allocation
strategy. The costs associated with these considerations, along with the
constraints of having unique mappings between tasks and agents and constraints
on the maximum number of agents that can be assigned to a queue, results in a
Linear Integer Program (LIP) that is solved using the SCIP solver. For the
scenario where the queue lengths are penalized but not the waiting times, we
demonstrate that the auction system allocates tasks in a manner that all the
queue lengths become constant, which is termed balancing. For the scenarios
where both the costs are considered, we qualitatively analyse the effect of the
choice of the relative weights on the resulting task allocation and provide
guidelines for the choice of the weights. We present simulation results that
illustrate the balanced allocation of tasks and validate the analysis for the
trade-off between the costs related to queue lengths and task waiting times.Comment: Submitted to The 22nd World Congress of the International Federation
of Automatic Control (IFAC 2023
Irregular Change Detection in Sparse Bi-Temporal Point Clouds using Learned Place Recognition Descriptors and Point-to-Voxel Comparison
Change detection and irregular object extraction in 3D point clouds is a
challenging task that is of high importance not only for autonomous navigation
but also for updating existing digital twin models of various industrial
environments. This article proposes an innovative approach for change detection
in 3D point clouds using deep learned place recognition descriptors and
irregular object extraction based on voxel-to-point comparison. The proposed
method first aligns the bi-temporal point clouds using a map-merging algorithm
in order to establish a common coordinate frame. Then, it utilizes deep
learning techniques to extract robust and discriminative features from the 3D
point cloud scans, which are used to detect changes between consecutive point
cloud frames and therefore find the changed areas. Finally, the altered areas
are sampled and compared between the two time instances to extract any
obstructions that caused the area to change. The proposed method was
successfully evaluated in real-world field experiments, where it was able to
detect different types of changes in 3D point clouds, such as object or
muck-pile addition and displacement, showcasing the effectiveness of the
approach. The results of this study demonstrate important implications for
various applications, including safety and security monitoring in construction
sites, mapping and exploration and suggests potential future research
directions in this field
Adaptive Compression of Slowly Varying Images Transmitted over Wireless Sensor Networks
In this article a scheme for image transmission over Wireless Sensor Networks (WSN) with an adaptive compression factor is introduced. The proposed control architecture affects the quality of the transmitted images according to: (a) the traffic load within the network and (b) the level of details contained in an image frame. Given an approximate transmission period, the adaptive compression mechanism applies Quad Tree Decomposition (QTD) with a varying decomposition compression factor based on a gradient adaptive approach. For the initialization of the proposed control scheme, the desired a priori maximum bound for the transmission time delay is being set, while a tradeoff among the quality of the decomposed image frame and the time needed for completing the transmission of the frame should be taken under consideration. Based on the proposed control mechanism, the quality of the slowly varying transmitted image frames is adaptively deviated based on the measured time delay in the transmission. The efficacy of the adaptive compression control scheme is validated through extended experimental results
3DEG: Data-Driven Descriptor Extraction for Global re-localization in subterranean environments
Current global re-localization algorithms are built on top of localization
and mapping methods andheavily rely on scan matching and direct point cloud
feature extraction and therefore are vulnerable infeatureless demanding
environments like caves and tunnels. In this article, we propose a novel
globalre-localization framework that: a) does not require an initial guess,
like most methods do, while b)it has the capability to offer the
top-kcandidates to choose from and last but not least provides anevent-based
re-localization trigger module for enabling, and c) supporting completely
autonomousrobotic missions. With the focus on subterranean environments with
low features, we opt to usedescriptors based on range images from 3D LiDAR
scans in order to maintain the depth informationof the environment. In our
novel approach, we make use of a state-of-the-art data-driven
descriptorextraction framework for place recognition and orientation regression
and enhance it with the additionof a junction detection module that also
utilizes the descriptors for classification purposes
Comparison between Docker and Kubernetes based Edge Architectures for Enabling Remote Model Predictive Control for Aerial Robots
Edge computing is becoming more and more popular among researchers who seek
to take advantage of the edge resources and the minimal time delays, in order
to run their robotic applications more efficiently. Recently, many edge
architectures have been proposed, each of them having their advantages and
disadvantages, depending on each application. In this work, we present two
different edge architectures for controlling the trajectory of an Unmanned
Aerial Vehicle (UAV). The first architecture is based on docker containers and
the second one is based on kubernetes, while the main framework for operating
the robot is the Robotic Operating System (ROS). The efficiency of the overall
proposed scheme is being evaluated through extended simulations for comparing
the two architectures and the overall results obtained.Comment: 6 pages, 15 figures, conference article, IECON 202
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