1,749 research outputs found

    Visual Perception of Garments for their Robotic Manipulation

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    TĂ©matem pƙedloĆŸenĂ© prĂĄce je strojovĂ© vnĂ­mĂĄnĂ­ textiliĂ­ zaloĆŸenĂ© na obrazovĂ© informaci a vyuĆŸitĂ© pro jejich robotickou manipulaci. PrĂĄce studuje několik reprezentativnĂ­ch textiliĂ­ v bÄ›ĆŸnĂœch kognitivně-manipulačnĂ­ch ĂșlohĂĄch, jako je napƙíklad tƙíděnĂ­ neznĂĄmĂœch oděvĆŻ podle typu nebo jejich sklĂĄdĂĄnĂ­. NěkterĂ© z těchto činnostĂ­ by v budoucnu mohly bĂœt vykonĂĄvĂĄny domĂĄcĂ­mi robotickĂœmi pomocnĂ­ky. StrojovĂĄ manipulace s textiliemi je poptĂĄvanĂĄ takĂ© v prĆŻmyslu. HlavnĂ­ vĂœzvou ƙeĆĄenĂ©ho problĂ©mu je měkkost a s tĂ­m souvisejĂ­cĂ­ vysokĂĄ deformovatelnost textiliĂ­, kterĂ© se tak mohou nachĂĄzet v bezpočtu vizuĂĄlně velmi odliĆĄnĂœch stavĆŻ.The presented work addresses the visual perception of garments applied for their robotic manipulation. Various types of garments are considered in the typical perception and manipulation tasks, including their classification, folding or unfolding. Our work is motivated by the possibility of having humanoid household robots performing these tasks for us in the future, as well as by the industrial applications. The main challenge is the high deformability of garments, which can be posed in infinitely many configurations with a significantly varying appearance

    Editorial

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    Monocular navigation for long-term autonomy

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    We present a reliable and robust monocular navigation system for an autonomous vehicle. The proposed method is computationally efficient, needs off-the-shelf equipment only and does not require any additional infrastructure like radio beacons or GPS. Contrary to traditional localization algorithms, which use advanced mathematical methods to determine vehicle position, our method uses a more practical approach. In our case, an image-feature-based monocular vision technique determines only the heading of the vehicle while the vehicle's odometry is used to estimate the distance traveled. We present a mathematical proof and experimental evidence indicating that the localization error of a robot guided by this principle is bound. The experiments demonstrate that the method can cope with variable illumination, lighting deficiency and both short- and long-term environment changes. This makes the method especially suitable for deployment in scenarios which require long-term autonomous operation

    Conceptual spatial representations for indoor mobile robots

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    We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ïŹndings in cognitive psychology, our model is composed of layers representing maps at diïŹ€erent levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system

    MULTI-ROBOT COVERAGE WITH DYNAMIC COVERAGE INFORMATION COMPRESSION

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    This work considers the problem of coverage of an initially unknown environment by a set of autonomous robots. A crucial aspect in multi-robot coverage involves robots sharing information about the regions they have already covered at certain intervals, so that multiple robots can avoid repeated coverage of the same area. However, sharing the coverage information between robots imposes considerable communication and computation overhead on each robot, which increases the robots’ battery usage and overall coverage time. To address this problem, we explore a novel coverage technique where robots use an information compression algorithm before sharing their coverage maps with each other. Specifically, we use a polygonal approximation algorithm to represent any arbitrary region covered by a robot as a polygon with a fixed, small number of vertices. At certain intervals, each robot then sends this small set of vertices to other robots in its communication range as its covered area, and each receiving robot records this information in a local map of covered regions so that it can avoid repeat coverage. The coverage information in the map is then utilized by a technique called spanning tree coverage (STC) by each robot to perform area coverage. We have verified the performance of our algorithm on simulated Coroware Corobot robots within the Webots robot simulator with different sizes of environments and different types of obstacles in the environments, while modelling sensor noise from the robots’ sensors. Our results show that using the polygonal compression technique is an effective way to considerably reduce data transfer between robots in a multi-robot team without sacrificing the performance and efficiency gains that communication provides to such a system

    A Dynamical System Approach for Resource-Constrained Mobile Robotics

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    The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system
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