waashk/dblp huggingface dataset:
a sonar approach to obstacle detection for a vision-based autonomous wheelchair. an advanced prototype computer controlled power wheelchair navigation system or ccpwns has been developed to provide autonomy for highly disabled users, whose mix of disabilities makes it difficult or impossible to control their own power chairs in their homes. the working paradigm is ''teach and repeat'' a mode of control for typical industrial holonomic robots. ultrasound sensors, which during subsequent autonomous tracking will be used to detect obstacles, also are active during teaching. based upon post-processed data collected during this teaching event, elaborate trajectories-which may involve multiple direction changes, pivoting and so on, depending upon the requirements of the typically restricted spaces within which the chair must operate-will later be called upon by the disabled rider. an off-line postprocessor assigns an ultrasound profile to the sequence of poses of any taught trajectory. use of this profile during tracking obviates most of the inherent problems of using ultrasound to avoid obstacles while retaining the ability to near solid objects, such as when passing through a narrow doorway, where required by the environment and trajectory objectives. the work in this article describes a procedure to obtain consistent maps of sonar boundaries during the teaching process, and a preliminary approach to use this information during the tracking phase. the approach is illustrated by results obtained by using the ccpwns prototype.
prediction of geometric errors of robot manipulators with particle swarm optimisation method. this paper reports on the prediction of the expected positioning errors of robot manipulators due to the errors in their geometric parameters. a swarm intelligence (si) based algorithm, which is known as particle swarm optimization (pso), has been used to generate error estimation functions. the experimental system used is a motoman sk120 manipulator. the error estimation functions are based on the robot position data provided by a high precision laser measurement system. the functions have been verified for three test trajectories, which contain various configurations of the manipulator. the experimental results demonstrate that the positioning errors of robot manipulators can be effectively predicted using some constant coefficient polynomials whose coefficients are determined by employing the pso algorithm. it must be emphasized that once the estimation functions are obtained, there may be no need of any further experimental data in order to determine the expected positioning errors for a subsequent use in the error correction process.
cognitive maps for mobile robots - an object based approach. robots are rapidly evolving from factory work-horses to robot-companions. the future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is comprehensible to humans. the work presented here is oriented in this direction. it suggests a hierarchical probabilistic representation of space that is based on objects. a global topological representation of places with object graphs serving as local maps is proposed. the work also details the first efforts towards conceptualizing space on the basis of the human compatible representation so formed. such a representation and the resulting conceptualization would be useful for enabling robots to be cognizant of their surroundings. experiments on place classification and place recognition are reported in order to demonstrate the applicability of such a representation towards understanding space and thereby performing spatial cognition. further, relevant results from user studies validating the proposed representation are also reported. thus, the theme of the work is - representation for spatial cognition.
biomimetic whiskers for shape recognition. rodents demonstrate an outstanding capability of tactile perception with their whiskers. mechanoreceptors surrounding the whisker shaft in their follicle structure measure deflection of the whisker. we designed biomimetic whiskers following the basic design of the follicle. in experiments with the artificial whiskers, we have explored tactile perception based on active whisking where the deflection angle or velocity provides the localization information which is the basis of shape recognition. measuring contact distances at varying protraction angles allows discrimination of round objects with a varying curvature, or objects with different lateral shapes, such as square and round objects. we show the capabilities and limitations of a single whisker for shape recognition as well as the usefulness of multiple whiskers. in addition, measuring both vertical and horizontal deflection of a single whisker allows detection of the vertical shape for objects with a smooth surface. two or more whiskers stacked vertically can recognize the vertical shape by observing the difference of their deflection amplitudes or the time shift of deflection velocity peak. the results provide a clue on how autonomous robots could improve their sensory capabilities with mechanical probes.
self-calibration of environmental camera for mobile robot navigation. an environmental camera is a camera embedded in a working environment to provide vision guidance to a mobile robot. in the setup of such robot systems, the relative position and orientation between the mobile robot and the environmental camera are parameters that must unavoidably be calibrated. traditionally, because the configuration of the robot system is task-driven, these kinds of external parameters of the camera are measured separately and should be measured each time a task is to be performed. in this paper, a method is proposed for the robot system in which calibration of the environmental camera is rendered by the robot system itself on the spot after a system is set up. specific kinds of motion patterns of the mobile robot, which are called test motions, have been explored for calibration. the calibration approach is based upon executing certain selected test motions on the mobile robot and then using the camera to observe the robot. according to a comparison of odometry and sensing data, the external parameters of the camera can be calibrated. furthermore, an evaluation index (virtual sensing error) has been developed for the selection and optimization of test motions to obtain good calibration performance. all the test motion patterns are computed offline in advance and saved in a database, which greatly shorten the calibration time. simulations and experiments verified the effectiveness of the proposed method.
fast and accurate slam with rao-blackwellized particle filters. rao-blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. this technique applies a particle filter in which each particle carries an individual map of the environment. accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. this paper presents an approximative but highly efficient approach to mapping with rao-blackwellized particle filters. moreover, it provides a compact map model. a key advantage is that the individual particles can share large parts of the model of the environment. furthermore, they are able to reuse an already computed proposal distribution. both techniques substantially speed up the overall filtering process and reduce the memory requirements. experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using rao-blackwellized particle filters.
using covariance intersection for slam. one of the greatest obstacles to the use of simultaneous localization and mapping (slam) in a real-world environment is the need to maintain the full correlation structure between the vehicle and all of the landmark estimates. this structure is computationally expensive to maintain and is not robust to linearization errors. in this tutorial we describe slam algorithms that attempt to circumvent these difficulties through the use of covariance intersection (ci). ci is the optimal algorithm for fusing estimates when the correlations among them are unknown. a feature of ci relative to techniques which exploit full correlation information is that it provides provable consistency with much less computational overhead. in practice, however, a tradeoff typically needs to be made between estimation accuracy and computational cost. we describe a number of techniques that span the range of tradeoffs from maximum computational efficiency with straight ci to maximum estimation efficiency with the maintenance of all correlation information. we present a set of examples illustrating benefits of ci-based slam.
active estimation of distance in a robotic system that replicates human eye movement. in a moving agent, the different apparent motion of objects located at various distances provides an important source of depth information. while motion parallax is evident for large translations of the agent, a small parallax also occurs in most head/eye systems during rotations of the cameras. a similar parallax is also present in the human eye, so that a redirection of gaze shifts the projection of an object on the retina by an amount that depends not only on the amplitude of the rotation, but also on the distance of the object with respect to the observer. this study examines the accuracy of distance estimation on the basis of the parallax produced by camera rotations. sequences of human eye movements were used to control the motion of a pan/tilt system specifically designed to reproduce the oculomotor parallax present in the human eye. we show that the oculomotor strategies by which humans scan visual scenes produce parallaxes that provide accurate estimation of distance. this information simplifies challenging visual tasks such as image segmentation and figure/ground segregation.
occupancy grids building by sonar and mobile robot. in this paper, a modified method for occupancy grid map building by a moving mobile robot and a scanning ultrasonic range-finder is proposed. the map building process consists of two phases: (1) gleaning of information from environment, and (2) sonar data processing. for sonar data processing the proposed modified method combines: (1) statistical approach for probability sonar model building; and (2) application of fuzzy logic theory for sonar data fusion. it is experimentally shown that, in some applications, the proposed modified method has advantages over other well-known methods.
bilateral teleoperation through the internet. this paper proposes a stable control structure for the bilateral teleoperation of robots through internet. the problem is motivated by the increasing use of the internet as a communication channel. internet has a time-varying delay which depends on factors such as congestion, bandwidth and distance. in this work, we propose a control structure for the teleoperation of a manipulator robot with force feedback. such a control structure includes state controllers (placed on the local and remote sites) and a time-delay compensation, which modifies the delayed position command generated by the human operator using the force that he feels in such a delayed moment and the current force between the slave and the remote environment. in addition, the proposed control scheme is designed considering a model of the communication channel. finally, experiments of bilateral teleoperation of robots through intranet and internet are shown to test the performance and stability of the designed teleoperation system.
recursive scan-matching slam. this paper presents scan-slam, a new generalization of simultaneous localization and mapping (slam). slam implementations based on extended kalman filter (ekf) data fusion have traditionally relied on simple geometric models for defining landmarks. this limits ekf-slam to environments suited to such models and tends to discard much potentially useful data. the approach presented in this paper is a marriage of ekf-slam and scan correlation. landmarks are no longer defined by analytical models; instead they are defined by templates composed of raw sensed data. these templates can be augmented as more data become available so that the landmark definition improves with time. a new generic observation model is derived that is generated by scan correlation, and this permits stochastic location estimation for landmarks with arbitrary shape within the kalman filter framework. the statistical advantages of an ekf representation are augmented with the general applicability of scan matching. scan matching also serves to enhance data association reliability by providing a shape metric for landmark disambiguation. experimental results in an outdoor environment are presented which validate the algorithm.
robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering. mobile robots in real-life settings would benefit from being able to localize and track sound sources. such a capability can help localizing a person or an interesting event in the environment, and also provides enhanced processing for other capabilities such as speech recognition. to give this capability to a robot, the challenge is not only to localize simultaneous sound sources, but to track them over time. in this paper we propose a robust sound source localization and tracking method using an array of eight microphones. the method is based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm. results show that a mobile robot can localize and track in real-time multiple moving sources of different types over a range of 7 m. these new capabilities allow a mobile robot to interact using more natural means with people in real-life settings.
multi-robot mobility enhanced hop-count based localization in ad hoc networks. the localization problem is important in mobile robots and wireless sensor network and has been studied for many years. among many localization methods, the hop-count based approach is simple and scalable; however, the localization accuracy is not satisfactory if the node density is low. to solve this problem, in this paper a multi-robot approach is proposed to utilize the cooperation and mobility of the robots to improve the node distribution (density), thus enhancing the hop-count based localization. by an auction-based task allocation scheme, the robots can negotiate with the static sensor nodes and then select the most suitable robots to move to the area of sparse node density, thus increasing the localization accuracy for the static sensor nodes. on the other hand, the robots also can localize themselves with the help of the static sensor nodes. the efficacy of this approach is shown by simulation.
design and dock analysis for the interactive module of a lattice-based self-reconfigurable robot. in this paper, a novel, lattice-based self-reconfigurable modular robot is presented. each module is composed of a cubic part and six rotary sides. there are two holes and two extension pegs on each side. rotary motion is generated by a motor with a reducer by using cone-shaped gears, clutches and so on. its quick disconnect/connect mechanism is analyzed. a face-face incidence matrix (ffim) is proposed to describe the relationship between modules in detail. the states of docking and constraint between modules are analyzed with the geometric method and the contact force of docking is described. lastly, a self-reconfigurable robot consisting of five similar modules designed to pass the groove in simulation with the proposed motion rules and its ffim is presented. the results verify that the above analysis is effective.
on point-to-point motion planning for underactuated space manipulator systems. in free-floating mode, space manipulator systems have their actuators turned off, and exhibit nonholonomic behavior due to angular momentum conservation. the system is underactuated and a challenging problem is to control both the location of the end effector and the attitude of the base, using manipulator actuators only. here a path planning methodology satisfying this requirement is developed. the method uses high order polynomials, as arguments in cosine functions, to specify the desired path directly in joint-space. in this way, the accessibility of final configurations is extended drastically, and the free parameters are determined by optimization techniques. it was found that this approach leads always to a path, provided that the desired change in configuration lies between physically permissible limits. physical limitations, imposed by system's dynamic parameters, are examined. lower and upper bounds for base rotation, due to manipulator motions, are estimated and shown in the implementation section. the presented method avoids the need for many small cyclical motions, and uses smooth functions in the planning scheme, leading to smooth configuration changes in finite and prescribed time.
evolution of fuzzy behaviors for multi-robotic system. in a multi-robotic system, robots interact with each other in a dynamically changing environment. the robots need to be intelligent both at the individual and group levels. in this paper, the evolution of a fuzzy behavior-based architecture is discussed. the behavior-based architecture decomposes the complicated interactions of multiple robots into modular behaviors at different complexity levels. the fuzzy logic approach brings in human-like reasoning to the behavior construction, selection and coordination. various behaviors in the fuzzy behavior-based architecture are evolved by genetic algorithm (ga). at the lowest level of the architecture hierarchy, the evolved fuzzy controllers enhanced the smoothness and accuracy of the primitive robot actions. at a higher level, the individual robot behaviors have become more skillful after the evolution. at the topmost level, the evolved group behaviors have resulted in aggressive competition strategy. the simulation and real-world experimentation on a robot-soccer system justify the effectiveness of the approach.
on the use of bayesian networks to develop behaviours for mobile robots. bayesian networks are models which capture uncertainties in terms of probabilities that can be used to perform reasoning under uncertainty. this paper presents an attempt to use bayesian networks as a learning technique to manage task execution in mobile robotics. to learn the bayesian network structure from data, the k2 structural learning algorithm is used, combined with three different net evaluation metrics. the experiment led to a new hybrid multiclassifying system resulting from the combination of 1-nn with the bayesian network, that allows one to use the power of the bayesian network while avoiding the computational burden of the reasoning mechanism - the so-called evidence propagation process. as an application example we present an approach of the presented paradigm to implement a door-crossing behaviour in a mobile robot using only sonar readings, in an environment with smooth walls and doors. both the performance of the learning mechanism and the experiments run in the real robot-environment system show that bayesian networks are valuable learning mechanisms, able to deal with the uncertainty and variability inherent to such systems.
a relative map approach to slam based on shift and rotation invariants. this paper presents a solution to the simultaneous localization and mapping (slam) problem in the stochastic map framework based on the concept of the relative map. the idea consists in introducing a map state, which only contains relative quantities among the features invariant under shift and rotation. the estimation of this relative state is carried out through an extended kalman filter. the shift and rotation invariance of the state allows us to significantly reduce the computational burden. in particular, the computational requirement is independent of the number of features. furthermore, since the estimation process is local, it is not affected by the linearization introduced by the ekf. the cases of point features and corner features are considered. furthermore, in the case of corners, it is considered a realistic case of an indoor environment containing structures consisting of several corners. finally, since a relative map contains dependent elements, the information coming from all the constraints which express the elements dependency, is exploited. for this, an approximated solution with low computational requirement is proposed. its limitation arises at the loop closure since it cannot exploit the information in this case. this is discussed in depth for the case of point features. experimental results carried out on a real platform in our laboratory and by using the victoria park dataset show the performance of the approach.
real-time motion planning of an autonomous mobile manipulator using a fuzzy adaptive kalman filter. this paper presents the real-time motion planning i.e., map building and path planning of an autonomous mobile manipulator capable of scanning natural terrain using a detector e.g., a landmine detector. map building generates a terrain map using the measurements of laser and ultrasonic rangefinders, and path planning uses the map to define an obstacle-free path for the detector. map building involves sensor fusion to tackle the uncertainties associated with range measurement. fusion takes place in a hierarchical filtering process that updates the map in real time and also optimizes the scanning process based on the terrain type. the filtering process includes a proposed fuzzy adaptive kalman filter in which the gain of the filter is adapted using a fuzzy model that characterizes the terrain. the efficiency of the proposed map building and path planning methods has been verified by experiments on a prototype mine detector robot.
including probabilistic target detection attributes into map representations. range measuring sensors can play an extremely important role in robot navigation. all range measuring devices rely on a 'detection criterion' made in the presence of noise, to determine when the transmitted signal is considered detected and hence a range reading is obtained. in commonly used sensors, such as laser range finders and polaroid sonars, the criterion under which successful detection is assumed, is kept hidden from the user. however, 'detection decisions' on the presence of noise still take place within the sensor. this paper integrates signal detection probabilities into the map building process which provides the most accurate interpretation of such sensor data. to facilitate range detection analysis, map building with a frequency modulated continuous wave millimetre wave radar (fmcw mmwr), which is able to provide complete received power-range spectra for multiple targets down range is considered. this allows user intervention in the detection process and although not directly applicable to the commonly used 'black-box' type range sensors, provides insight as to how not only range values, but received signal strength values should be incorporated into the map building process. this paper presents two separate methods of map building with sensors which return both range and received signal power information. the first is an algorithm which uses received signal-to-noise power to make an estimates of the range to multiple targets down range, without any signal distribution assumptions. we refer to this as feature detection based on target presence probability (tpp). in contrast to the first method, the second method does use assumptions on the statistics of the signal in target presence and absence scenarios to formulate a probabilistic likelihood detector. this allows for an increased rate of convergence to ground truth. evidence theory is then introduced to model and update successive observations in a recursive fashion. both methods are then compared using real mmwr data sets from indoor and outdoor experiments.