waashk/dblp huggingface dataset:
from images to rooms. in this paper we start from a set of images obtained by the robot that is moving around in an environment. we present a method to automatically group the images into groups that correspond to convex subspaces in the environment which are related to the human concept of rooms. pairwise similarities between the images are computed using local features extracted from the images and geometric constraints. the images with the proposed similarity measure can be seen as a graph or in a way as a base level dense topological map. from this low level representation the images are grouped using a graph-clustering technique which effectively finds convex spaces in the environment. the method is tested and evaluated on challenging data sets acquired in real home environments. the resulting higher level maps are compared with the maps humans made based on the same data.
automatic surface roughing with 3d machine vision and cooperative robot control. this paper presents an innovative and practical strategy for automated leather surface roughing, using structured light 3d machine vision for object profile perception, and nurbs interpolation for accurate and smooth trajectory generation. as high pressure grit blasting is used for roughing, considering the spacial constraints in the blasting chamber, an additional degree of freedom is introduced using a rotary table, which supports the workpiece. cooperative control is implemented between a 6-dof robot and the rotary table to minimize robot movements, while satisfying the requirements of variable velocity control, accurate trajectory tracking and orientation control. experimental results of consistent roughing performance have shown the efficiency of the proposed method.
robot team coordination for target tracking using fuzzy logic controller in game theoretic framework. this paper involves a collision free target tracking problem of multi-agent robot system. target tracking requires team coordination to maintain a desired formation and to keep team-mates and target together. every team-mate makes decisions on their moving direction that may spoil the tactical position of others and makes the global coordination task nontrivial. the contribution of the paper is twofold. first, the convergence of target tracking is improved by a new game theoretic concept using a semi-cooperative stackelberg equilibrium point and a new formation component in the individual cost functions. to enhance the robustness, a pd like fuzzy controller tunes the cost function weights directly for the game theoretic solution and helps to achieve a prescribed value of cost function components. simulation result for target tracking by a three-member robot team is presented.
uncalibrated visual servoing using the fundamental matrix. this paper describes a new algorithm for visual control of an uncalibrated 3 dof joint system, using two weakly calibrated fixed cameras. the algorithm estimates on-line the image jacobian, a matrix which linearly relates joint velocity and image feature velocity. in our experiments we prove that by using the fundamental matrix, robustness of the estimation in the presence of noise is significantly increased with respect to already existing algorithms in specialized literature.
adaptive visual gesture recognition for human-robot interaction using a knowledge-based software platform. in human-human communication we can adapt or learn new gestures or new users using intelligence and contextual information. achieving natural gesture-based interaction between humans and robots, the system should be adaptable to new users, gestures and robot behaviors. this paper presents an adaptive visual gesture recognition method for human-robot interaction using a knowledge-based software platform. the system is capable of recognizing users, static gestures comprised of the face and hand poses, and dynamic gestures of face in motion. the system learns new users, poses using multi-cluster approach, and combines computer vision and knowledge-based approaches in order to adapt to new users, gestures and robot behaviors. in the proposed method, a frame-based knowledge model is defined for the person-centric gesture interpretation and human-robot interaction. it is implemented using the frame-based software platform for agent and knowledge management (spak). the effectiveness of this method has been demonstrated by an experimental human-robot interaction system using a humanoid robot 'robovie'.
formation optimization for a fleet of wheeled mobile robots - a geometric approach. tight formation-based operations are critical in several emerging applications for robot collectives - ranging from cooperative payload transport to synchronized distributed data-collection. in this paper, we investigate the optimal relative layout for members of a team of differentially-driven wheeled mobile robots (dd-wmrs) moving in formation for ultimate deployment in cooperative payload transport tasks. our particular focus is on modeling such formations, developing the motion plans and determining the ''best formation'' in a differential-geometric setting. specifically, a preferred team-fixed frame serves as a virtual leader inducing motion plans for the individual dd-wmrs which form the vertices of a virtual structure. the resulting motion plans for the dd-wmrs as well as overall team-performance depend both on the specifiedteam-frame motions as well as their relative-layout within the formation. emphasis is placed on developing suitable invariant (yet quantitative) measures of formation quality and a systematic optimization-based selection of the formation-layout. the use of relative formation-parameterization with respect to a team-frame serves to decouple the team-level optimal layout selection process. the optimal location of each dd-wmr can now be found with respect to the team-frame individually and the feasibility of distributed implementation facilitates scaling to larger-sized formations. analytical and numerical results, from case studies of formation optimization of three dd-wmrs maneuvering along certain desired planar paths, are presented to highlight the salient features and benefits.
velocity planning for a mobile robot to track a moving target - a potential field approach. potential field method has been widely used for mobile robot path planning, but mostly in a static environment where the target and the obstacles are stationary. the path planning result is normally the direction of the robot motion. in this paper, the potential field method is applied for both path and speed planning, or the velocity planning, for a mobile robot in a dynamic environment where the target and the obstacles are moving. the robot's planned velocity is determined by relative velocities as well as relative positions among robot, obstacles and targets. the implementation factors such as maximum linear and angular speed of the robot are also considered. the proposed approach guarantees that the robot tracks the moving target while avoiding moving obstacles. simulation studies are provided to verify the effectiveness of the proposed approach.
from omnidirectional images to hierarchical localization. we propose a new vision-based method for global robot localization using an omnidirectional camera. topological and metric localization information are combined in an efficient, hierarchical process, with each step being more complex and accurate than the previous one but evaluating fewer images. this allows us to work with large reference image sets in a reasonable amount of time. simultaneously, thanks to the use of 1d three-view geometry, accurate metric localization can be achieved based on just a small number of nearby reference images. owing to the wide baseline features used, the method deals well with illumination changes and occlusions, while keeping the computational load small. the simplicity of the radial line features used speeds up the process without affecting the accuracy too much. we show experiments with two omnidirectional image data sets to evaluate the performance of the method and compare the results using the proposed radial lines with results from state-of-the-art wide-baseline matching techniques.
development of intelligent multisensor surveillance systems with agents. intelligent multisensor surveillance systems consist of several types of sensors, which are installed on fixed and mobile devices. these components provide a huge quantity of information that has to be contrasted, correlated and integrated in order to recognize and react on special situations. these systems work in highly dynamic environments, with severe security and robustness requirements. all these characteristics imply the need for distributed solutions. in these solutions, scattered components can decide and act with some degree of autonomy (for instance, if they become isolated), or cooperate and coordinate for a complete tracking of special situations. in order to cope with these requirements and to better structure the solution, we have decided to design surveillance system control as a multiagent system. this is done by applying an agent-orientated methodology, which is assessed with concrete scenarios.
a multi-agent architecture with cooperative fuzzy control for a mobile robot. the challenges of robotics have led the researchers to develop control architectures composed of distributed, independent and asynchronous behaviors. one way to approach decentralization is through cooperative control, since it allows the development of complex behavior based on several controllers combined to achieve the desired result. robots, however, require high-level cognitive capacities, and multi-agent architectures provide the appropriate level of abstraction to define them. this article describes a multi-agent architecture combined with cooperative control developed within the agent. the experiments were carried out on an activmedia pioneer 2dx mobile robot.
the centre of area method as a basic mechanism for representation and navigation. potential methods and all their gradient-based derivations are extensively used in autonomous robotics, primarily in association with reactive navigational strategies. in this article we introduce the fundamentals, formalisation and application of a brand-new method based on first-order moments called the ''centre of area method''. we also comment on its validity, at an individual level and in combination with other methods, in order to build a situated representation of the environment.
symbol grounding through robotic manipulation in cognitive systems. though proposals have been put forth to solve the classical symbol grounding problem through robotic sensorimotor interactions, only little progress has been made in this direction with actual working systems, and symbol grounding through physical interaction has been rarely dealt with. we address this problem in the context of robotic manipulation for cognitive systems, and claim that there are symbols which do not refer simply to physical objects but rather to the embodied interactions between the robot and the objects in its environment. through the description of two manipulation experiments we offer a proposal on which to build a theory of symbolic representations for physical interactions. some important neuroscience studies that support our view are also briefly described.
localization of legged robots combining a fuzzy-markov method and a population of extended kalman filters. this paper presents a new approach to robot vision-based self-localization in dynamic and noisy environments for legged robots when efficiency is a strong requirement. the major contribution of this paper is the improvement of a markovian method based on a fuzzy occupancy grid (fmk). our proposal combines fmk with a population of extended kalman filters, making the complete algorithm both robust and accurate while keeping its computational cost bounded. two different strategies have been designed to combine both the methods. they have been tested in the robocup environment and quantitatively compared with other approaches in several experiments with the real robot.
from bio-inspired vs. psycho-inspired to etho-inspired robots. artificial intelligence has been roughly divided into two schools of thought since its beginnings, the symbolic (also known as gofai, neat, classical, etc.) and the subsymbolic one (connectionist, scruffy, etc.). these two approaches have also had strong influence on the robotics field. in this paper we want to present a less known one, based on ethology, and its application to the generation of autonomous behavior in mobile robots. in this way, we present the foundations of jde (jerarquia dinamica de esquemas), an etho-inspired architecture where behavior is organized as a dynamic hierarchy of independent schemas, as well as some examples of its applications, and a discussion about its properties.
globally consistent 3d mapping with scan matching. a globally consistent solution to the simultaneous localization and mapping (slam) problem in 2d with three degrees of freedom (dof) poses was presented by lu and milios [f. lu, e. milios, globally consistent range scan alignment for environment mapping, autonomous robots 4 (april) (1997) 333-349]. to create maps suitable for natural environments it is however necessary to consider the 6dof pose case, namely the three cartesian coordinates and the roll, pitch and yaw angles. this article describes the extension of the proposed algorithm to deal with these additional dofs and the resulting non-linearities. simplifications using taylor expansion and cholesky decomposition yield a fast application that handles the massive amount of 3d data and the computational requirements due to the 6dof. our experiments demonstrate the functionality of estimating the exact poses and their covariances in all 6dof, leading to a globally consistent map. the correspondences between scans are found automatically by use of a simple distance heuristic.
researching and developing a real-time infrastructure for intelligent systems - evolution of an integrated approach. in this paper, we describe the principles and the methodologies that we have researched for the creation of a software infrastructure for bridging the gap from brain-like systems design to standard software technology. looking at the brain, we constantly take inspiration and choose the relevant principles that our computer-base model should/could be based on. this ranges from the evolution of the brain (phylogenetically and ontogenetically), the inherent autonomy of the currently identified areas, the intrinsic synchronization through the most basic control mechanisms that regulates interaction, communication, and modulation. with these principles in mind, we started to make a subdivision of our system into instance, functional and computing architecture, modeling each sub-system with processes and tools in order to create a basic infrastructure that supports the research and creation of intelligent systems. the basic elements of our infrastructure are the bbcm (brain bytes component model) and bbdm (brain bytes data model), created to enable the modularization and reuse of our systems. based on those, we have developed dtbos (design tool for brain operating system), the design environment for supporting graphical design, rtbos (real-time brain operating system), the middleware that supports real-time execution of our modular systems, and cmbos (control-monitor brain operating system) to enable the monitoring of running modules. we will show the feasibility of the established environment by shortly describing some of the experimental systems in the area of cognitive robotics that we have created. this will serve to give a more concrete understanding of the dimensions and the type of systems that we have been able to create.
appearance-based localization for mobile robots using digital zoom and visual compass. this paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. the approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by markov localization. this enables the system to deal with perceptual aliasing or absence of reliable sensor data. it has been implemented on a robot operating in an office scenario and the robustness of the approach is demonstrated experimentally.
toward humanoid manipulation in human-centred environments. in order for humanoid robots to enter human-centred environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary for real-time interaction with the environment and humans. the goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on the other hand the realization of service tasks in a household scenario. in this paper, we present a new humanoid robot currently being developed for applications in human-centred environments. in addition, we present an integrated grasping and manipulation system consisting of a motion planner for the generation of collision-free paths and a vision system for the recognition and localization of a subset of household objects as well as a grasp analysis component which provides the most feasible grasp configurations for each object.
biped robot design powered by antagonistic pneumatic actuators for multi-modal locomotion. an antagonistic muscle mechanism that regulates joint compliance contributes enormously to human dynamic locomotion. antagonism is considered to be the key for realizing more than one locomotion mode. in this paper, we demonstrate how antagonistic pneumatic actuators can be utilized to achieve three dynamic locomotion modes (walking, jumping, and running) in a biped robot. firstly, we discuss the contribution of joint compliance to dynamic locomotion, which highlights the importance of tunable compliance. secondly, we introduce the design of a biped robot powered by antagonistic pneumatic actuators. lastly, we apply simple feedforward controllers for realizing walking, jumping, and running and confirm the contribution of joint compliance to such multimodal dynamic locomotion. based on the results, we can conclude that the antagonistic pneumatic actuators are superior candidates for constructing a human-like dynamic locomotor.
towards long-lived robot genes. robot projects are often evolutionary dead ends, with the software and hardware they produce disappearing without trace afterwards. in humanoid robotics, a small field with an avid appetite for novel devices, we experience a great deal of ''churn'' of this nature. in this paper, we explore how best to make our projects stable and long-lasting, without compromising our ability to constantly change our sensors, actuators, processors and networks. we also look at how to encourage the propagation and evolution of hardware designs, so that we can start to build up a ''gene-pool'' of material to draw upon for new projects. we advance on two fronts, software and hardware. for some time, we have been developing and using the yarp robot software architecture [giorgio metta, paul fitzpatrick, lorenzo natale, yarp: yet another robot platform, international journal on advanced robotics systems 3 (2006) 43-48], which helps organize communication between sensors, processors, and actuators so that loose coupling is encouraged, making gradual system evolution much easier. yarp includes a model of communication that is transport-neutral, so that data flow is decoupled from the details of the underlying networks and protocols in use. importantly for the long term, yarp is designed to play well with other architectures. device drivers written for yarp can be ripped out and used without any ''middleware''. on the network, basic interoperation is possible with a few lines of code in any language with a socket library, and maximally efficient interoperation can be achieved by following documented protocols. these features are not normally the first things that end-users look for when starting a project, but they are crucial for longevity. we emphasize the strategic utility of the free software social contract [b. perens, the open source definition, in: chris dibona, sam ockman, mark stone (eds.), open sources: voices from the open source revolution, o'reilly and associates, cambridge, ma, 1999] to software development for small communities with idiosyncratic requirements. we also work to expand our community by releasing the design of our icub humanoid [n.g. tsagarakis, g. metta, g. sandini, d. vernon, r. beira, f. becchi, l. righetti, j. santos-victor, a.j. ijspeert, m.c. carrozza, d.g. caldwell, icub - the design and realization of an open humanoid platform for cognitive and neuroscience research, journal of advanced robotics 21 (10) (2007) 1151-1175] under a free and open licence, and funding development using this platform.
from schemas to neural networks: a multi-level modelling approach to biologically-inspired autonomous robotic systems. biology has been an important source of inspiration in building adaptive autonomous robotic systems. due to the inherent complexity of these models, most biologically-inspired robotic systems tend to be ethological without linkage to underlying neural circuitry. yet, neural mechanisms are crucial in modelling adaptation and learning. the work presented in this paper describes a schema and neural network multi-level modelling approach to biologically inspired autonomous robotic systems. a prey acquisition model with detour behaviour in frogs is presented to exemplify the modelling approach. the model is tested with simulated and physical robots using the asl/nsl and miro robotic system.
sensor fusion-based visual target tracking for autonomous vehicles with the out-of-sequence measurements solution. in this paper, a novel algorithm is proposed for the visual target tracking by autonomous guide vehicles (agv). this paper proposes a sensor data fusion system to estimate the dynamics of the target. optical flow vectors, colour features, stereo pair disparities are used as the visual features while the vehicle's inertial measurements are used to estimate the stereo cameras' motion. the algorithm estimates the velocity and position of the target which is then used by the vehicle to track the target. in this sensor data fusion-based tracking system, the measurements from the same target can arrive out of sequence. this is called the ''out-of-sequence'' measurements (oosm) problem. thus the resulting problem - how to update the current state estimate with an ''older'' measurement - needs to be solved. in this paper the 1-step-lag oosm solution from bar-shalom is applied for the extended kalman filter-based target-state estimation. the performance of the proposed tracking algorithm with the oosm solution is demonstrated through extensive experimental results.
recognition of in-hand manipulation using contact state transition for multifingered robot hand control. this paper proposes a method for recognizing in-hand manipulation of the operator by observing a contact state transition between an object and the human hand. an instrumented object with a pressure distribution sensor and a position/orientation sensor has been developed. by processing information from the sensors, the contact regions on the operator's palm surface are detected. a contact state transition diagram is created by taking practical contact states into account. a recognition algorithm based on dynamic programming (dp) is proposed to recognize the type of in-hand manipulation by comparing the similarity of the contact state transition between an input sequence and template manipulation primitives. the validity of the proposed method is confirmed by experiments.