waashk/dblp huggingface dataset:
mobility evaluation of wheeled all-terrain robots. standardized performance evaluation is uncommon in mobile robotics. therefore, it is difficult to compare published results and estimate their value. this work aims at providing a basis for the evaluation and comparison of the mobility performance of wheeled, all-terrain robots with respect to terrainability. precisely defined existing and novel metrics are proposed for this purpose. the utility of these metrics is shown by applying them in a comparison to a selection of rover suspension systems known from the literature. appropriate models for evaluation of the performance are described as well as the validation of the metrics by means of experimental testing. the simulations reveal significant differences in performance between the rovers which is confirmed by the test results. the correlation of the performance in simulation and reality is highly satisfying and supports the applicability of the proposed metrics.
comparison of sensor fusion methods for an sma-based hexapod biomimetic robot. this paper compares the performances of different sensor fusion algorithms in a shape memory alloy (sma)-based hexapod biomimetic robot, smabot iv. the algorithms considered include a kalman filter that minimizes the estimation error variance, an h"~ filter that minimizes the worst-case estimation error, and a robust mixed kalman/h"~ filter that allows for uncertainties in both the system and measurement matrices. the sensors installed on the robot include an inertial measurement unit and an electric compass sensor for inertial guidance. in addition, a stride-length-estimation algorithm for an sma-based legged robot was proposed to establish the legged odometry of the robot. allan variance analysis is employed to identify the noise sources of inertial sensors, and the calculated variance values are used to design the parameters of the kalman filter algorithm. finally, experimental results of two-dimensional navigation are presented, and the performances of three sensor fusion algorithms are compared. the results indicate that after identifying the noise characteristics of inertial sensors, the kalman filter provides the best performance.
towards a unified visual framework in a binocular active robot vision system. this paper presents the results of an investigation and pilot study into an active binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. the prototype developed is capable of identifying, targeting, verging on and recognising objects in a cluttered scene without the need for calibration or other knowledge of the camera geometry. this is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. the system structure is based on the 'searchlight metaphor' of biological systems. we present results of an investigation that yield a maximum vergence error of ~6.5 pixels, while ~85% of known objects were recognised in five different cluttered scenes. finally a 'stepping-stone' visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the field of view resulting from any individual saccade.
formation path following control of unicycle-type mobile robots. this paper presents a control strategy for the coordination of multiple mobile robots. a combination of the virtual structure and path following approaches is used to derive the formation architecture. a formation controller is proposed for the kinematic model of two-degree-of-freedom unicycle-type mobile robots. the approach is then extended to consider the formation controller by taking into account the physical dimensions and dynamics of the robots. the controller is designed in such a way that the path derivative is left as a free input to synchronize the robot's motion. simulation results with three robots are included to show the performance of our control system. finally, the theoretical results are experimentally validated on a multi-robot platform.
an information-based exploration strategy for environment mapping with mobile robots. the availability of efficient mapping systems to produce accurate representations of initially unknown environments is recognized as one of the main requirements for autonomous mobile robots. in this paper, we present an efficient mapping system that has been implemented on a mobile robot equipped with a laser range scanner. the system builds geometrical point-based maps of environments employing an information-based exploration strategy that determines the best observation positions by taking into account both the distance travelled and the information gathered. our exploration strategy, being based on solid mathematical foundations, differs from many ad hoc exploration strategies proposed in literature. we present: (a) the theoretical aspects of the criterion for determining the best observation positions for a robot building a map, (b) the implementation of a mapping system that uses the proposed criterion, and (c) the experimental validation of our approach.
maximum-likelihood sample-based maps for mobile robots. the problem of representing the environment of a mobile robot has been studied intensively in the past. the predominant approaches for geometric representations are grid-based or line-based maps. in this paper, we consider sample-based maps which use the data points obtained by range scanners to represent the environment. the main advantage of this representation over the other techniques is that it is able to represent arbitrary structures and at the same time provide an arbitrary accuracy. however, range measurements come in large amounts and not every measurement necessarily contributes to the representation in the same way. we present a novel approach for calculating maximum-likelihood subsets of the data points by sub-sampling laser range data. in particular, our method applies a variant of the fuzzy k-means algorithm to find a map that maximizes the likelihood of the original data. experimental results with real data show that the resulting maps are better suited for robot localization than maps obtained with other sub-sampling techniques.
interactive teaching of task-oriented robot grasps. this paper focuses on the problem of grasp stability and grasp quality analysis. an elegant way to evaluate the stability of a grasp is to model its wrench space. however, classical grasp quality measures suffer from several disadvantages, the main drawback being that they are not task related. indeed, constructive approaches for approximating the wrench space including also task information have been rarely considered. this work presents an effective method for task-oriented grasp quality evaluation based on a novel grasp quality measure. we address the general case of multifingered grasps with point contacts with friction. the proposed approach is based on the concept of programming by demonstration and interactive teaching, wherein an expert user provides in a teaching phase a set of exemplar grasps appropriate for the task. following this phase, a representation of task-related grasps is built. during task planning and execution, a grasp could be either submitted interactively for evaluation by a non-expert user or synthesized by an automatic planning system. grasp quality is then assessed based on the proposed measure, which takes into account grasp stability along with its suitability for the task. to enable real-time evaluation of grasps, a fast algorithm for computing an approximation of the quality measure is also proposed. finally, a local grasp optimization technique is described which can amend uncertainties arising in supplied grasps by non-expert users or assist in planning more valuable grasps in the neighborhood of candidate ones. the paper reports experiments performed in virtual reality with both an anthropomorphic virtual hand and a three-fingered robot hand. these experiments suggest the effectiveness and task relevance of the proposed grasp quality measure.
a local obstacle avoidance method for mobile robots in partially known environment. local obstacle avoidance is a principle capability for mobile robots in unknown or partially known environment. a series of velocity space methods including the curvature velocity method (cvm), the lane curvature method (lcm) and the beam curvature method (bcm) formulate the local obstacle avoidance problem as one of constrained optimization in the velocity space by taking the physical constraints of the environment and the dynamics of the vehicle into account. we present a new local obstacle avoidance approach that combines the prediction model of collision with the improved bcm. not only does this method inherit the quickness of bcm and the safety of lcm, but also the proposed prediction based bcm (pbcm) can be used to avoid moving obstacles in dynamic environments.
entropy based robust estimator and its application to line-based mapping. this paper presents a robust mapping algorithm for an application in autonomous robots. the method is inspired by the notion of entropy from information theory. a kernel density estimator is adopted to estimate the appearance probability of samples directly from the data. an entropy based robust (ebr) estimator is then designed that selects the most reliable inliers of the line segments. the inliers maintained by the entropy filter are those samples that carry more information. hence, the parameters extracted from ebr estimator are accurate and robust to the outliers. the performance of the ebr estimator is illustrated by comparing the results with the performance of three other estimators via simulated and real data.
a novel low-cost, limited-resource approach to autonomous multi-robot exploration and mapping. mobile robots are becoming more heavily used in environments where human involvement is limited, impossible, or dangerous. these robots perform some of the more laborious human tasks on earth and throughout the solar system, simultaneously saving resources and offering automation. higher levels of autonomy are also being sought in these applications, such as distributed exploration and mapping of unknown areas. smaller, less expensive mobile robots are becoming more prevalent, which introduces unique challenges in terms of limited sensing accuracy and onboard computing resources. this paper presents a novel low-cost, limited-resource approach to autonomous multi-robot mapping and exploration in unstructured environments. design and implementation details are presented, along with results from two planetary style environments. results demonstrate that low-cost ($ 1250) mobile robots capable of simultaneous localization and mapping can be successfully constructed. the multi-robot system presented in this paper participated in the 2008 international conference on robotics and automation (icra) space robotics challenge, receiving two awards for successfully completing the 'onto the surface' and 'map the environment' events in a simulated planetary environment. this work demonstrates not only that such systems are possible, but also that this direction of research is important and needs attention.
motion in ambiguity: coordinated active global localization for multiple robots. the task of the robot in localization is to find out where it is, through sensing and motion. in environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in 'multiple hypotheses' locations of a robot. this is inevitable with global localization algorithms, as the local environment seen by a robot repeats at other parts of the map. thus, for effective localization, the robot has to be actively guided to those locations where there is a maximum chance of eliminating most of the ambiguous states - which is often referred to as 'active localization'. when extended to multi-robotic scenarios where all robots possess more than one hypothesis of their position, there is an opportunity to do better by using robots, apart from obstacles, as 'hypotheses resolving agents'. the paper presents a unified framework which accounts for the map structure as well as measurement amongst robots, while guiding a set of robots to locations where they can singularize to a unique state. the strategy shepherds the robots to places where the probability of obtaining a unique hypothesis for a set of multiple robots is a maximum. another aspect of framework demonstrates the idea of dispatching localized robots to locations where they can assist a maximum of the remaining unlocalized robots to overcome their ambiguity, named as 'coordinated localization'. the appropriateness of our approach is demonstrated empirically in both simulation & real-time (on amigo-bots) and its efficacy verified. extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense. it also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither. theoretical backing stems from the proven completeness of the method for a large category of diverse environments.
robot contact tasks in the presence of control target distortions. this work refers to the problem of controlling robot motion and force in frictional contacts under environmental errors and particularly orientation errors that distort the desired control targets and control subspaces. the proposed method uses online estimates of the surface normal (tangent) direction to dynamically modify the control target and control space decomposition. it is proved that these estimates converge to the actual value even though the elasticity and friction parameters are unknown. the proposed control solution is demonstrated through simulation examples in three-dimensional robot motion tasks contacting both planar and curved surfaces.
a strategy for grasping unknown objects based on co-planarity and colour information. in this work, we describe and evaluate a grasping mechanism that does not make use of any specific object prior knowledge. the mechanism makes use of second-order relations between visually extracted multi-modal 3d features provided by an early cognitive vision system. more specifically, the algorithm is based on two relations covering geometric information in terms of a co-planarity constraint as well as appearance based information in terms of co-occurrence of colour properties. we show that our algorithm, although making use of such rather simple constraints, is able to grasp objects with a reasonable success rate in rather complex environments (i.e., cluttered scenes with multiple objects). moreover, we have embedded the algorithm within a cognitive system that allows for autonomous exploration and learning in different contexts. first, the system is able to perform long action sequences which, although the grasping attempts not being always successful, can recover from mistakes and more importantly, is able to evaluate the success of the grasps autonomously by haptic feedback (i.e., by a force torque sensor at the wrist and proprioceptive information about the distance of the gripper after a gasping attempt). such labelled data is then used for improving the initially hard-wired algorithm by learning. moreover, the grasping behaviour has been used in a cognitive system to trigger higher level processes such as object learning and learning of object specific grasping.
visual control through the trifocal tensor for nonholonomic robots. we present a new vision-based control approach which drives autonomously a nonholonomic vehicle to a target location. the vision system is a camera fixed on the vehicle and the target location is defined by an image taken previously in that location. the control scheme is based on the trifocal tensor model, which is computed from feature correspondences in calibrated retina across three views: initial, current and target images. the contribution is a trifocal-based control law defined by an exact input-output linearization of the trifocal tensor model. the desired evolution of the system towards the target is directly defined in terms of the trifocal tensor elements by means of sinusoidal functions without needing metric or additional information from the environment. the trifocal tensor presents important advantages for visual control purposes, because it is more robust than two-view geometry as it includes the information of a third view and, contrary to the epipolar geometry, short baseline is not a problem. simulations show the performance of the approach, which has been tested with image noise and calibration errors.
estimation of pointing poses for visually instructing mobile robots under real world conditions. in this paper, we present an approach for directing a mobile robot under real-world conditions into a target position by means of pointing poses only. because one important objective of our work is the development of a low-cost platform, only monocular vision at web-cam level should be employed. our previous approach presented in gross et al. (2006) [1], richarz et al. (2007) [2] has been improved by several additional processing steps. finally, a background subtraction technique and a histogram equalization have been integrated in the preprocessing stage to be able to work in environments with structured backgrounds and under variable lighting conditions. furthermore, a discriminant analysis was used to find the most relevant input features for the pointing pose estimator. the contribution of this paper is, however, not only the presentation of an approach to estimating pointing poses in a demanding real-world scenario on a mobile robot, but also the detailed and evaluative comparison between different image-preprocessing techniques, alternative feature extraction methods, and several function approximators with the same set of test- and training data. reasonable combinations of the different methods are tested, and for each component on the processing chain the effect on the accuracy of the target estimation is quantized. the approach presented in this paper has been implemented on the mobile interaction robot horos to determine the performance and estimation accuracy under real-world conditions. furthermore, we compared the accuracy of our approach with that of humans performing the same estimation task, and achieved very comparable results for the best estimator.
dimensional synthesis of kinematically redundant serial manipulators for cluttered environments. a systematic procedure for synthesizing kinematically redundant serial manipulators is proposed in this paper. for a given cluttered workcell, the task space locations (tsl's) for the desired manipulator are prescribed. the synthesis is performed with the objective of reachability of the manipulator at specified tsl's, while avoiding obstacles. the problem is formulated as a constrained optimization problem, minimizing the positional error and simultaneously avoiding any collision of the manipulator with either the obstacles or within its links. the technique used to solve the resulting constrained optimization problem is the classical augmented lagrangian method. the paper presents a discussion on the past works in this field. it is observed that the presented literature is confined to special cases only while the proposed method involves full generality of the synthesis problem. the availability of such an algorithm working for full generality is important, particularly for highly constrained environments. the efficiency of the proposed approach to synthesize the desired redundant manipulators is exhibited through diverse cases. the resulting synthesized manipulators are further checked for the possibility of feasible paths between tsl's. an outline of the development of a redundant manipulator, synthesized through the presented approach, is also included in this paper.
a developmental algorithm for ocular-motor coordination. this paper presents a model of ocular-motor development, inspired by ideas and data from developmental psychology. the learning problem concerns the growth of the transform between image space and motor space necessary for the control of visual saccades. an implementation is used to produce experimental results and these are presented and discussed. the algorithm is simple, extremely fast, self-calibrating, adaptive to change, and exhibits emergent stages of behaviour as learning progresses.
a review of log-polar imaging for visual perception in robotics. log-polar imaging consists of a type of methods that represent visual information with a space-variant resolution inspired by the visual system of mammals. it has been studied for about three decades and has surpassed conventional approaches in robotics applications, mainly the ones where real-time constraints make it necessary to utilize resource-economic image representations and processing methodologies. this paper surveys the application of log-polar imaging in robotic vision, particularly in visual attention, target tracking, egomotion estimation, and 3d perception. the concise yet comprehensive review offered in this paper is intended to provide novel and experienced roboticists with a quick and gentle overview of log-polar vision and to motivate vision researchers to investigate the many open problems that still need solving. to help readers identify promising research directions, a possible research agenda is outlined. finally, since log-polar vision is not restricted to robotics, a couple of other areas of application are discussed.
what is analytic infrastructure and why should you care? we define analytic infrastructure to be the services, applications, utilities and systems that are used for either preparing data for modeling, estimating models, validating models, scoring data, or related activities. for example, analytic infrastructure includes databases and data warehouses, statistical and data mining systems, scoring engines, grids and clouds. note that, with this definition, analytic infrastructure does not need to be used exclusively for modeling but simply useful as part of the modeling process. in this article, we discuss the importance of analytic infrastructure and some of the standards that can be used to support analytic infrastructure. we also discuss some specialized analytic infrastructure applications and services, including applications that can manage very large datasets and build models over them and cloud based analytic services.
efficient deployment of predictive analytics through open standards and cloud computing. over the past decade, we have seen tremendous interest in the application of data mining and statistical algorithms, first in research and science and, more recently, across various industries. this has translated into the development of a myriad of solutions by the data mining community that today impact scientific and business applications alike. however, even in this scenario, interoperability and open standards still lack broader adoption among data miners and modelers. in this article we highlight the use of the predictive model markup language (pmml) standard, which allows for models to be easily exchanged between analytic applications. with a focus on interoperability and pmml, we also discuss here emerging trends in cloud computing and software as a service, which have already started to play a critical role in promoting a more effective implementation and widespread application of predictive models. as an illustration of how the benefits of open standards and cloud computing can be combined, we describe a predictive analytics scoring engine platform that leverages these elements to deliver an efficient deployment process for statistical models.