2,429 research outputs found
RAFCON: a Graphical Tool for Task Programming and Mission Control
There are many application fields for robotic systems including service
robotics, search and rescue missions, industry and space robotics. As the
scenarios in these areas grow more and more complex, there is a high demand for
powerful tools to efficiently program heterogeneous robotic systems. Therefore,
we created RAFCON, a graphical tool to develop robotic tasks and to be used for
mission control by remotely monitoring the execution of the tasks. To define
the tasks, we use state machines which support hierarchies and concurrency.
Together with a library concept, even complex scenarios can be handled
gracefully. RAFCON supports sophisticated debugging functionality and tightly
integrates error handling and recovery mechanisms. A GUI with a powerful state
machine editor makes intuitive, visual programming and fast prototyping
possible. We demonstrated the capabilities of our tool in the SpaceBotCamp
national robotic competition, in which our mobile robot solved all exploration
and assembly challenges fully autonomously. It is therefore also a promising
tool for various RoboCup leagues.Comment: 8 pages, 5 figure
Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms
We present a decentralized and scalable approach for deployment of a robot
swarm. Our approach tackles scenarios in which the swarm must reach multiple
spatially distributed targets, and enforce the constraint that the robot
network cannot be split. The basic idea behind our work is to construct a
logical tree topology over the physical network formed by the robots. The
logical tree acts as a backbone used by robots to enforce connectivity
constraints. We study and compare two algorithms to form the logical tree:
outwards and inwards. These algorithms differ in the order in which the robots
join the tree: the outwards algorithm starts at the tree root and grows towards
the targets, while the inwards algorithm proceeds in the opposite manner. Both
algorithms perform periodic reconfiguration, to prevent suboptimal topologies
from halting the growth of the tree. Our contributions are (i) The formulation
of the two algorithms; (ii) A comparison of the algorithms in extensive
physics-based simulations; (iii) A validation of our findings through
real-robot experiments.Comment: 8 pages, 8 figures, submitted to IROS 201
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
Genetic Algorithms used for Search and Rescue of Vulnerable People in an Urban Setting
The main goal of this research is to design and develop a genetic algorithm (GA) for path planning of an Unmanned Aerial Vehicle (UAV) outfitted with a camera to efficiently search for a lost person in an area of interest. The research focuses on scenarios where the lost person is from a vulnerable population, such as someone suffering from Alzheimer or a small child who has wondered off. To solve this problem, a GA for path planning was designed and implemented in Matlab. The area of interest is considered to be a circle that encompasses the distance the person could have walked in the time they have been missing. The area might also have some subareas that could not be excluded from the search for various reasons, such as a river they could not cross, or a fenced area. A grid is imposed on the area of interest, based on the field of view of the camera that the UAV is carrying and the height the UAV is flying. A chromosome is the encoding of the path the UAV will fly and the fitness function of the GA is designed to ensure that the UAV is covering all areas of the grid with the least amount of backtracking. The results show that the GA can find a path that efficiently covers the area. These results can be generalized to use more than one UAV
Evolutionary strategies in swarm robotics controllers
Nowadays, Unmanned Vehicles (UV) are widespread around the world. Most of these
vehicles require a great level of human control, and mission success is reliant on this
dependency. Therefore, it is important to use machine learning techniques that will train the
robotic controllers to automate the control, making the process more efficient.
Evolutionary strategies may be the key to having robust and adaptive learning in robotic
systems. Many studies involving UV systems and evolutionary strategies have been
conducted in the last years, however, there are still research gaps that need to be addressed,
such as the reality gap. The reality gap occurs when controllers trained in simulated
environments fail to be transferred to real robots.
This work proposes an approach for solving robotic tasks using realistic simulation and
using evolutionary strategies to train controllers. The chosen setup is easily scalable for multirobot
systems or swarm robots.
In this thesis, the simulation architecture and setup are presented, including the drone
simulation model and software. The drone model chosen for the simulations is available in the
real world and widely used, such as the software and flight control unit. This relevant factor
makes the transition to reality smoother and easier. Controllers using behavior trees were
evolved using a developed evolutionary algorithm, and several experiments were conducted.
Results demonstrated that it is possible to evolve a robotic controller in realistic
simulation environments, using a simulated drone model that exists in the real world, and also
the same flight control unit and operating system that is generally used in real world
experiments.Atualmente os Veículos Não Tripulados (VNT) encontram-se difundidos por todo o Mundo.
A maioria destes veículos requerem um elevado controlo humano, e o sucesso das missões
está diretamente dependente deste fator. Assim, é importante utilizar técnicas de
aprendizagem automática que irão treinar os controladores dos VNT, de modo a automatizar o
controlo, tornando o processo mais eficiente.
As estratégias evolutivas podem ser a chave para uma aprendizagem robusta e adaptativa
em sistemas robóticos. Vários estudos têm sido realizados nos últimos anos, contudo, existem
lacunas que precisam de ser abordadas, tais como o reality gap. Este facto ocorre quando os
controladores treinados em ambientes simulados falham ao serem transferidos para VNT
reais.
Este trabalho propõe uma abordagem para a resolução de missões com VNT, utilizando
um simulador realista e estratégias evolutivas para treinar controladores. A arquitetura
escolhida é facilmente escalável para sistemas com múltiplos VNT.
Nesta tese, é apresentada a arquitetura e configuração do ambiente de simulação,
incluindo o modelo e software de simulação do VNT. O modelo de VNT escolhido para as
simulações é um modelo real e amplamente utilizado, assim como o software e a unidade de
controlo de voo. Este fator é relevante e torna a transição para a realidade mais suave. É
desenvolvido um algoritmo evolucionário para treinar um controlador, que utiliza behavior
trees, e realizados diversos testes.
Os resultados demonstram que é possível evoluir um controlador em ambientes de
simulação realistas, utilizando um VNT simulado mas real, assim como utilizando as mesmas
unidades de controlo de voo e software que são amplamente utilizados em ambiente real
New Method for Localization and Human Being Detection using UWB Technology: Helpful Solution for Rescue Robots
International audienceTwo challenges for rescue robots are to detect human beings and to have an accurate positioning system. In indoor positioning, GPS receivers cannot be used due to the reflections or attenuation caused by obstacles. To detect human beings, sensors such as thermal camera, ultrasonic and microphone can be embedded on the rescue robot. The drawback of these sensors is the detection range. These sensors have to be in close proximity to the victim in order to detect it. UWB technology is then very helpful to ensure precise localization of the rescue robot inside the disaster site and detect human beings. We propose a new method to both detect human beings and locate the rescue robot at the same time. To achieve these goals we optimize the design of UWB pulses based on B-splines. The spectral effectiveness is optimized so the symbols are easier to detect and the mitigation with noise is reduced. Our positioning system performs to locate the rescue robot with an accuracy about 2 centimeters. During some tests we discover that UWB signal characteristics abruptly change after passing through a human body. Our system uses this particular signature to detect human body
UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether
This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle
(UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a
flying sensor but also as a tether attachment device. Two robots are connected
with a tether, allowing the UAV to anchor the tether to a structure located at
the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the
poor traversability of the UGV by not only providing a wider range of scanning
and mapping from the air, but also by allowing the UGV to climb steep terrains
with the winding of the tether. In addition, we present an autonomous framework
for the collaborative navigation and tether attachment in an unknown
environment. The UAV employs visual inertial navigation with 3D voxel mapping
and obstacle avoidance planning. The UGV makes use of the voxel map and
generates an elevation map to execute path planning based on a traversability
analysis. Furthermore, we compared the pros and cons of possible methods for
the tether anchoring from multiple points of view. To increase the probability
of successful anchoring, we evaluated the anchoring strategy with an
experiment. Finally, the feasibility and capability of our proposed system were
demonstrated by an autonomous mission experiment in the field with an obstacle
and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on
Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1
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