327 research outputs found
Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
In this paper, new method is proposed for a more robust Data Assimilation (DA) design of the
river flow and stage estimation. By using the new sets of data that are derived from the incorporated Multi
Imputation Particle Filter (MIPF) in the DA structure, the proposed method is found to have overcome the
issue of missing observation data and contributed to a better estimation process. The convergence analysis
of the MIPF is discussed and shows that the number of the particles and imputation influence the ability of
this method to perform estimation. The simulation results of the MIPF demonstrated the superiority of the
proposed approach when being compared to the Extended Kalman Filter (EKF) and Particle Filter (PF)
A systematic literature review of decision-making and control systems for autonomous and social robots
In the last years, considerable research has been carried out to develop robots that can improve our quality of life during tedious and challenging tasks. In these contexts, robots operating without human supervision open many possibilities to assist people in their daily activities. When autonomous robots collaborate with humans, social skills are necessary for adequate communication and cooperation. Considering these facts, endowing autonomous and social robots with decision-making and control models is critical for appropriately fulfiling their initial goals. This manuscript presents a systematic review of the evolution of decision-making systems and control architectures for autonomous and social robots in the last three decades. These architectures have been incorporating new methods based on biologically inspired models and Machine Learning to enhance these systems’ possibilities to developed societies. The review explores the most novel advances in each application area, comparing their most essential features. Additionally, we describe the current challenges of software architecture devoted to action selection, an analysis not provided in similar reviews of behavioural models for autonomous and social robots. Finally, we present the future directions that these systems can take in the future.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR
A biologically inspired architecture for an autonomous and social robot
Lately, lots of effort has been put into the construction of robots able to live among humans. This fact has favored the development of personal or social robots, which are expected to behave in a natural way. This implies that these robots could meet certain requirements, for example, to be able to decide their own actions (autonomy), to be able to make deliberative plans (reasoning), or to be able to have an emotional behavior in order to facilitate human-robot interaction. In this paper, the authors present a bioinspired control architecture for an autonomous and social robot, which tries to accomplish some of these features. In order to develop this new architecture, authors have used as a base a prior hybrid control architecture (AD) that is also biologically inspired. Nevertheless, in the later, the task to be accomplished at each moment is determined by a fix sequence processed by the Main Sequencer. Therefore, the main sequencer of the architecture coordinates the previously programmed sequence of skills that must be executed. In the new architecture, the main sequencer is substituted by a decision making system based on drives, motivations, emotions, and self-learning, which decides the proper action at every moment according to robot's state. Consequently, the robot improves its autonomy since the added decision making system will determine the goal and consequently the skills to be executed. A basic version of this new architecture has been implemented on a real robotic platform. Some experiments are shown at the end of the paper.This work has been supported by the Spanish Government through the project called “Peer to Peer Robot-Human Interaction” (R2H), of MEC (Ministry of Science and Education), the project “A new approach to social robotics” (AROS), of MICINN (Ministry of Science and Innovation), the CAM Project S2009/DPI-1559/ROBOCITY2030 II, developed by the research team RoboticsLab at the University Carlos III of Madrid
Kick control: using the attracting states arising within the sensorimotor loop of self-organized robots as motor primitives
Self-organized robots may develop attracting states within the sensorimotor
loop, that is within the phase space of neural activity, body, and
environmental variables. Fixpoints, limit cycles, and chaotic attractors
correspond in this setting to a non-moving robot, to directed, and to irregular
locomotion respectively. Short higher-order control commands may hence be used
to kick the system from one self-organized attractor robustly into the basin of
attraction of a different attractor, a concept termed here as kick control. The
individual sensorimotor states serve in this context as highly compliant motor
primitives.
We study different implementations of kick control for the case of simulated
and real-world wheeled robots, for which the dynamics of the distinct wheels is
generated independently by local feedback loops. The feedback loops are
mediated by rate-encoding neurons disposing exclusively of propriosensoric
inputs in terms of projections of the actual rotational angle of the wheel. The
changes of the neural activity are then transmitted into a rotational motion by
a simulated transmission rod akin to the transmission rods used for steam
locomotives.
We find that the self-organized attractor landscape may be morphed both by
higher-level control signals, in the spirit of kick control, and by interacting
with the environment. Bumping against a wall destroys the limit cycle
corresponding to forward motion, with the consequence that the dynamical
variables are then attracted in phase space by the limit cycle corresponding to
backward moving. The robot, which does not dispose of any distance or contact
sensors, hence reverses direction autonomously.Comment: 17 pages, 9 figure
Higher coordination with less control - A result of information maximization in the sensorimotor loop
This work presents a novel learning method in the context of embodied
artificial intelligence and self-organization, which has as few assumptions and
restrictions as possible about the world and the underlying model. The learning
rule is derived from the principle of maximizing the predictive information in
the sensorimotor loop. It is evaluated on robot chains of varying length with
individually controlled, non-communicating segments. The comparison of the
results shows that maximizing the predictive information per wheel leads to a
higher coordinated behavior of the physically connected robots compared to a
maximization per robot. Another focus of this paper is the analysis of the
effect of the robot chain length on the overall behavior of the robots. It will
be shown that longer chains with less capable controllers outperform those of
shorter length and more complex controllers. The reason is found and discussed
in the information-geometric interpretation of the learning process
Pemahaman pelajar tingkatan lima katering terhadap bab kaedah memasak dalam mata pelajaran teknologi katering
Bab Kaedah Memasak merupakan salah satu bab yang penting dalam mata
pelajaran Teknologi Katering. Faktor terpenting adalah memastikan pelajar menguasai
serta memahami konsepnya adalah melalui proses pengajaran dan pembelajaran yang
betul. Tinjauan awal di Sekolah Menengah Teknik yang menawarkan Kursus Katering,
menunjukkan bahawa kebanyakan pelajar sukar untuk menguasai dan memahami bab
tersebut. Berdasarkan hasil tinjauan , pengkaji ingin mengenalpasti pemasalahan dalam
memahami bab tersebut. Di samping itu juga, pengkaji ingin mengenalpasti adakah
pencapaian pelajar dalam PMR, minat, motivasi dan gaya pembelajaran mempengaruhi
pemahaman pelajar, Kajian rintis telah dilakukan terhadap 10 orang responden dengan
nilai alpha 0.91. Ini menunjukkan kebolehpercayaan terhadap kajian di jalankan adalah
tinggi. Responden adalah terdiri daripada 30 orang pelajar Tingkatan Lima (ERT)
Sekolah Menengah Teknik Muar, Johor. Keputusan skor min keseluruhan menunjukkan
pelajar berminat dan mempunyai motivasi ynag baik dalam bidang ini. Namun begitu,
gaya pembelajaran yang diamalkan tidak sesuai dan antara pemyebab wujudnya
pemasalahan dalam memahami bab Kaedah Memasak. Ujian kolerasi menunjukkan
bahawa tidak terdapat sebarang hubungan signifikan antara pencapaian PMR pelajar
dengan pemahaman bab tersebut. Sementara minat, motivasi dan gaya pembelajaran
membuktikan ada hubungan signifikan dengan pemahaman pelajar dalam bab Kaedah
Memasak
Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons
The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with 18 degrees of freedom, and obstacle-avoidance of a wheel-driven robot. © 2014 Toutounji and Pasemann
Unified Behavior Framework in an Embedded Robot Controller
Robots of varying autonomy have been used to take the place of humans in dangerous tasks. While robots are considered more expendable than human beings, they are complex to develop and expensive to replace if lost. Recent technological advances produce small, inexpensive hardware platforms that are powerful enough to match robots from just a few years ago. There are many types of autonomous control architecture that can be used to control these hardware platforms. One in particular, the Unified Behavior Framework, is a flexible, responsive control architecture that is designed to simplify the control system’s design process through behavior module reuse, and provides a means to speed software development. However, it has not been applied on embedded systems in robots. This thesis presents a development of the Unified Behavior Framework on the Mini-WHEGS™, a biologically inspired, embedded robotic platform. The Mini-WHEGS™ is a small robot that utilize wheel- legs to emulate cockroach walking patterns. Wheel-legs combine wheels and legs for high mobility without the complex control system required for legs. A color camera and a rotary encoder completes the robot, enabling the Mini-WHEGS™ to identify color objects and track its position. A hardware abstraction layer designed for the Mini-WHEGS™ in this configuration decouples the control system from the hardware and provide the interface between the software and the hardware. The result is a highly mobile embedded robot system capable of exchanging behavior modules with much larger robots while requiring little or no change to the modules
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