This paper addresses the problem of keeping an autonomous marine vehicle in a moving triangular formation by regulating its position with respect to two leader vehicles. The follower vehicle has no prior knowledge of ...
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This paper addresses the problem of keeping an autonomous marine vehicle in a moving triangular formation by regulating its position with respect to two leader vehicles. The follower vehicle has no prior knowledge of the path described by the leaders but has access to their heading angle and is able to measure inter-vehicle ranges. It is assumed that the distance between the leaders is constant and known. A control strategy is adopted that generates speed and heading commands so as to drive suitably defined along track and cross track errors to zero. The commands are used as input to local inner loops for yaw and speed control. The paper describes the algorithms derived for range-based control and assesses their performance in simulations using realistic models of the vehicles involved. Tests with three autonomous marine vehicles equipped with acoustic modems and ranging devices allow for the evaluation of the performance of the algorithms in a real-world situation.
The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be ...
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The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals aspart of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebra fish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal -- robot societies.
The Robotic Darwinian Particle Swarm Optimization (RDPSO) recently introduced in the literature has the ability to dynamically partition the whole population of robots based on simple 'punish-reward' rules. Al...
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Introducing compliant actuation to robotic joints is an approach to ensure safety in closer human-machine interaction. Further, the possibility to adjust stiffness can be benificial considering energy storage and the ...
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Range-only simultaneous localization and mapping is addressed in this paper, through the design, analysis, and experimental validation of a globally asymptotically stable (GAS) filter. A nonlinear sensor-based system ...
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This paper describes the use of ant colony models for photographic rendering, that is, for the depiction or interpretation of photographic images. A description of a previously proposed ant-based edge detection method...
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This paper describes the use of ant colony models for photographic rendering, that is, for the depiction or interpretation of photographic images. A description of a previously proposed ant-based edge detection method called pherographia is given in order to contextualize this paper's proposal. Then, the application of the KANTS algorithm to photo-rendering is introduced. KANTS is ant-based clustering algorithm described by a set of equations that model the local behavior of simple units (ants). These units represent data samples. When moving on a heterogeneous 2-dimensional lattice of vectors, global behavior emerges from the local interaction between the ants, which self-organize into clusters. In this paper, a data set consists of the RGB values of a digital (or digitalized) photograph. Two different approaches for the initialization of the environment are tested, each one resulting in radically different output images: when the environment is initialized with the RGB values of the same image that is used to generate the data set, the final result is very similar to the original photo; a random initialization of the environment leads to an abstract interpretation of the photo. The results are contextualized within previous swarm artworks by the authors.
This paper addresses the problem of state estimation in formations of autonomous vehicles. The approach considered here consists in the implementation of a local state observer in each vehicle relying only on locally ...
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ISBN:
(纸本)9781479909964
This paper addresses the problem of state estimation in formations of autonomous vehicles. The approach considered here consists in the implementation of a local state observer in each vehicle relying only on locally available measurements and data communicated by neighboring agents, resulting in a decentralized state observer which features lower computational and communication loads than comparable centralized solutions. A method for computing observer gains which yield globally asymptotically stable error dynamics is presented for fixed topology formations, as well as an iterative algorithm for improving the decentralized estimator's performance when the measurements are corrupted by noise. The proposed framework is particularized to the practical case of a formation of Autonomous Underwater Vehicles (AUVs), and a continuous-discrete formulation is achieved for the local state observers, to take into account the difference in sampling rates between on-board instrumentation and positioning systems. To assess the performance of the solution, simulation results are presented and discussed for different formation topologies.
作者：
Popov, VladimirUral Federal University
Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg
Different planning problems for mobile robots are of considerable interest for many years. Frequently, such problems require to solve different hard computational problems. Multi-robot forest coverage is one of such c...
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Different planning problems for mobile robots are of considerable interest for many years. Frequently, such problems require to solve different hard computational problems. Multi-robot forest coverage is one of such computational problems. In this paper, we consider an efficient approach to solve the problem of multi-robot forest coverage for unweighted terrain. In particular, we consider an explicit reduction from the decision version of the problem of multi-robot forest coverage for unweighted terrain to the satisfiability problem. For different satisfiability algorithms, the results of computational experiments are presented. [ABSTRACT FROM AUTHOR]
作者：
Gorbenko, AnnaUral Federal University
Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg
Many robotic tasks require advanced systems of visual sensing. Robotic systems of visual sensing must be able to solve a number of different complex problems of visual data analysis. Object categorization is one of su...
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Many robotic tasks require advanced systems of visual sensing. Robotic systems of visual sensing must be able to solve a number of different complex problems of visual data analysis. Object categorization is one of such problems. In this paper, we propose an approach to automatic generation of computationally effective modules of object categorization for autonomous mobile robots. This approach is based on the consideration of the stack cover problem. In particular, it is assumed that the robot is able to perform an initial inspection of the environment. After such inspection, the robot needs to solve the stack cover problem by using a supercomputer. A solution of the stack cover problem allows the robot to obtain a template for computationally effective scheduling of object categorization. Also, we consider an efficient approach to solve the stack cover problem. In particular, we consider an explicit reduction from the decision version of the stack cover problem to the satisfiability problem. For different satisfiability algorithms, the results of computational experiments are presented. [ABSTRACT FROM AUTHOR]
The classification of skin lesions in dermoscopy images depends on three critical steps: i) lesion segmentation, ii) feature extraction and iii) feature classification. Lesion segmentation plays an important role sinc...
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The classification of skin lesions in dermoscopy images depends on three critical steps: i) lesion segmentation, ii) feature extraction and iii) feature classification. Lesion segmentation plays an important role since segmentation errors may jeopardize the other two steps, leading to erroneous decisions. This paper studies the robustness of a skin lesion classifier based on a Bag-of-features approach in the presence of segmentation errors. We compare the performance achieved by the system using an automatic segmentation algorithm with the performance obtained using manual segmentation provided by a specialist. We observe a degradation of the system accuracy by 8% when automatic segmentation is used. We also show that these results can be improved if manually segmented images are used in training phase, keeping a fully automatic solution during the testing phase.
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