This paper deals with the application of Echo State Network (ESN) model to robust control of the Twin Rotor Aero-Dynamical System (TRAS) through estimation and cancellation of disturbances. The work describes the mode...
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In the article we propose a fast and cheap strategy of service robot kinematic parameters calibration. Our method is based on sensors (cameras in particular) that are already mounted on the robot and inexpensive marke...
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ISBN:
(纸本)9781479987023
In the article we propose a fast and cheap strategy of service robot kinematic parameters calibration. Our method is based on sensors (cameras in particular) that are already mounted on the robot and inexpensive markers, that are easy to fix on the robot arms. We developed the method to compute the impact of manipulator velocity on markers localization error in camera image. Thanks to our method, the calibration data acquisition can be significantly shortened, because a certain, acceptable marker detection error threshold can be introduced that allows to acquire the data with non-zero velocity of the manipulator. Additionally, we propose a method of automatic component based data acquisition system generation, based on the embodied agent theory and tree like kinematic model representation of the robot. Our model is suitable for the most of service robots. The same model forms the base for an automatic generation of measure functions for cost functions used in the optimization process to find the optimal set of model parameters. The whole approach has been verified experimentally using Velma service robot.
Cloud robotics is becoming a trend in the modern robotics field, as it became evident that true artificial intelligence can be achieved only by sharing collective knowledge. In the ICT area, the most common way to for...
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The influence of the wet and warm atmosphere on CVD graphene was investigated. The CVD graphene grown on Cu foil and then transferred onto the BK7 glass substrate was applied in the experiments. The environmental cond...
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The paper considers nonlinear 2D continuous-time systems described by a statespace model of the Roesser form and nonlinear differential repetitive process. The property of exponential dissipativity is introduced and t...
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High performance computing is required in a number of data-intensive domains. CPU and GPU clusters are one of the most progressive branches in a field of parallel computing and data processing nowadays. Cloud computin...
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We present a solution to a specific version of one of the most fundamental computer science problem - the nearest neighbour problem (NN). The new, proposed variant of the NN problem is the multispace, dynamic, fixed-r...
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We present a solution to a specific version of one of the most fundamental computer science problem - the nearest neighbour problem (NN). The new, proposed variant of the NN problem is the multispace, dynamic, fixed-radius, all nearest neighbours problem, where the NN data structure handles queries that concern different subsets of input dimensions. In other words, solutions to this problem allow searching for closest points in terms of different features. This is an important issue in the context of practical applications of incremental state abstraction techniques for high dimensional Markov Decision Processes (MDP). The proposed solution is a set of simple, one-dimensional structures, that can handle range queries for arbitrary subset of input dimensions for the Chebyshev distance. We also provide version for other metrics, and a simplified version of the algorithm that yields approximate results but runs faster. The proposed approximation is deterministic in a way that ensures that the most important (in the context of the considered state abstraction task) parts of the result are returned with no accuracy loss. The presented experimental study demonstrates improvement in comparison to some state-of-the-art algorithms on uniformly random and MDP-generated data.
State abstraction [1] is one of solutions to the curse of dimensionality [2] problem, and possibly allows real-life application of AI algorithms. We present a new state abstraction algorithm inspired by stimulus discr...
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State abstraction [1] is one of solutions to the curse of dimensionality [2] problem, and possibly allows real-life application of AI algorithms. We present a new state abstraction algorithm inspired by stimulus discrimination theory from behavioral psychology [3], [4] and by current work on bisimulation theory as applied to reinforcement learning [5], [6], [7]. The new way of comparing state abstractions with the proposed notion of the ambiguity coefficient is evaluated on a well known Coffee Task domain. It is also a foundation for applying bisimulation approach to continuous domains.
This paper introduces the Reactive ASR-FA algorithm which is a novel ant routing algorithm that utilizes statistical models of packet delay to detect changes in the network conditions. The algorithm is able to quickly...
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Iterative learning control can be applied to systems that repeat the same task over a finite duration with resetting to the starting location once each one is complete. The novel feature is the use of information from...
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