460 research outputs found
A Robust Localization System for Inspection Robots in Sewer Networks †
Sewers represent a very important infrastructure of cities whose state should be monitored
periodically. However, the length of such infrastructure prevents sensor networks from being
applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network.
It is capable of sensing gas concentrations and detecting failures in the network such as cracks and
holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely
geo-localized to allow the operators performing the required correcting measures. To this end, this
paper presents a robust localization system for global pose estimation on sewers. It makes use of prior
information of the sewer network, including its topology, the different cross sections traversed and
the position of some elements such as manholes. The system is based on a Monte Carlo Localization
system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into
account the sewer network topology for discarding wrong hypotheses. Additionally, the localization
is further refined with novel updating steps proposed in this paper which are activated whenever
a discrete element in the sewer network is detected or the relative orientation of the robot over the
sewer gallery could be estimated. Each part of the system has been validated with real data obtained
from the sewers of Barcelona. The whole system is able to obtain median localization errors in the
order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art
Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the
approach.Unión Europea ECHORD ++ 601116Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C2
MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems
This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV)
platform called the Multi-robot Systems (MRS) Drone that can be used in a large
range of indoor and outdoor applications. The MRS Drone features unique
modularity with respect to changes in actuators, frames, and sensory
configuration. As the name suggests, the platform is specially tailored for
deployment within a MRS group. The MRS Drone contributes to the
state-of-the-art of UAV platforms by allowing smooth real-world deployment of
multiple aerial robots, as well as by outperforming other platforms with its
modularity. For real-world multi-robot deployment in various applications, the
platform is easy to both assemble and modify. Moreover, it is accompanied by a
realistic simulator to enable safe pre-flight testing and a smooth transition
to complex real-world experiments. In this manuscript, we present mechanical
and electrical designs, software architecture, and technical specifications to
build a fully autonomous multi UAV system. Finally, we demonstrate the full
capabilities and the unique modularity of the MRS Drone in various real-world
applications that required a diverse range of platform configurations.Comment: 49 pages, 39 figures, accepted for publication to the Journal of
Intelligent & Robotic System
Nuclear Environments Inspection with Micro Aerial Vehicles: Algorithms and Experiments
In this work, we address the estimation, planning, control and mapping
problems to allow a small quadrotor to autonomously inspect the interior of
hazardous damaged nuclear sites. These algorithms run onboard on a
computationally limited CPU. We investigate the effect of varying illumination
on the system performance. To the best of our knowledge, this is the first
fully autonomous system of this size and scale applied to inspect the interior
of a full scale mock-up of a Primary Containment Vessel (PCV). The proposed
solution opens up new ways to inspect nuclear reactors and to support nuclear
decommissioning, which is well known to be a dangerous, long and tedious
process. Experimental results with varying illumination conditions show the
ability to navigate a full scale mock-up PCV pedestal and create a map of the
environment, while concurrently avoiding obstacles.Comment: 10 pages, ISER 201
A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives
Efficient localization plays a vital role in many modern applications of
Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would
contribute to improved control, safety, power economy, etc. The ubiquitous 5G
NR (New Radio) cellular network will provide new opportunities for enhancing
localization of UAVs and UGVs. In this paper, we review the radio frequency
(RF) based approaches for localization. We review the RF features that can be
utilized for localization and investigate the current methods suitable for
Unmanned vehicles under two general categories: range-based and fingerprinting.
The existing state-of-the-art literature on RF-based localization for both UAVs
and UGVs is examined, and the envisioned 5G NR for localization enhancement,
and the future research direction are explored
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured
industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined
well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure
a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and
a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system
is that all the algorithms relied on the multi-resolution of the octomap for the world representation.
We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements
of the capability of the open-source system to run online and on-board the UAV in real-time. Our
approach is compared to different reference heuristics under this simulation environment showing
better performance in regards to the amount of explored space. With the proposed approach, the UAV
is able to explore 93% of the search space under 30 min, generating a path without repetition that
adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411
The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles
We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation
system for supporting replicable research through realistic simulations and
real-world experiments. We propose a unique multi-frame localization paradigm
for estimating the states of a UAV in various frames of reference using
multiple sensors simultaneously. The system enables complex missions in GNSS
and GNSS-denied environments, including outdoor-indoor transitions and the
execution of redundant estimators for backing up unreliable localization
sources. Two feedback control designs are presented: one for precise and
aggressive maneuvers, and the other for stable and smooth flight with a noisy
state estimate. The proposed control and estimation pipeline are constructed
without using the Euler/Tait-Bryan angle representation of orientation in 3D.
Instead, we rely on rotation matrices and a novel heading-based convention to
represent the one free rotational degree-of-freedom in 3D of a standard
multirotor helicopter. We provide an actively maintained and well-documented
open-source implementation, including realistic simulation of UAV, sensors, and
localization systems. The proposed system is the product of years of applied
research on multi-robot systems, aerial swarms, aerial manipulation, motion
planning, and remote sensing. All our results have been supported by real-world
system deployment that shaped the system into the form presented here. In
addition, the system was utilized during the participation of our team from the
CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions,
and also in the DARPA SubT challenge. Each time, our team was able to secure
top places among the best competitors from all over the world. On each
occasion, the challenges has motivated the team to improve the system and to
gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic
Systems (JINT), for the provided open-source software see
http://github.com/ctu-mr
Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments
Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature.
Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map.
Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes).
Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening
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