CRAS - Indexed Articles in Journals

Permanent URI for this collection

http://localhost:4000/handle/123456789/349

Browse

Browsing CRAS - Indexed Articles in Journals by Author "António Paulo Moreira"
  • Item
    Assessment of Robotic Picking Operations Using a 6 Axis Force/Torque Sensor
    ( 2016) Moreira,E ; Luís Freitas Rocha ; Andry Maykol Pinto ; António Paulo Moreira ; Germano Veiga
    This letter presents a novel architecture for evaluating the success of picking operations that are executed by industrial robots. It is formed by a cascade of machine learning algorithms (kNN and SVM) and uses information obtained by a 6 axis force/torque sensor and, if available, information from the built-in sensors of the robotic gripper. Beyond measuring the success or failure of the entire operation, this architecture makes it possible to detect in real-time when an object is slipping during the picking. Therefore, force and torque signatures are collected during the picking movement of the robot, which is decomposed into five different stages that allows to characterize distinct levels of success over time. Several trials were performed using an industrial robot with two different grippers for picking a long and flexible object. The experiments demonstrate the reliability of the proposed approach under different picking scenarios since, it obtained a testing performance (in terms of accuracy) up to 99.5% of successful identification of the result of the picking operations, considering an universe of 400 attempts. © 2016 IEEE.
  • Item
    Coordination of Marine Robots Under Tracking Errors and Communication Constraints
    ( 2016) Bruno Miguel Ferreira ; Aníbal Matos ; Nuno Cruz ; António Paulo Moreira
    This paper presents the development and the experimental validation of a centralized coordination control scheme that is robust to communication constraints and individual tracking errors for a team of possibly heterogeneous marine vehicles. By assuming the existence of a lower level target tracking control layer, a centralized potential-field-based coordination scheme is proposed to drive a team of robots along a path that does not necessarily need to be defined a priori. Furthermore, the formation is allowed to hold its position (the vehicles hold their positions with regard to a static virtual leader), which is particularly appreciated in several marine applications. As it is important to guarantee stability and mission completion in adverse environments with limited communications, the centralized control scheme for coordination is constructed in a way that makes it robust to tracking errors and intermittent communication links. The study and developments presented in this paper are complemented with field experiments in which vehicles have coordinated their operation to keep in formation over a dynamic path and static points. This work considers two types of communication technologies. Firstly, standard high rate radio communications are used to drive the formation and, secondly, acoustic communications are employed to assess the performance and the robustness of the proposed approach to degraded and highly variable conditions. Index Terms-Communication
  • Item
    Enhancing dynamic videos for surveillance and robotic applications: The robust bilateral and temporal filter
    ( 2014) Andry Maykol Pinto ; Paulo José Costa ; Miguel Velhote Correia ; António Paulo Moreira
    Over the last few decades, surveillance applications have been an extremely useful tool to prevent dangerous situations and to identify abnormal activities. Although, the majority of surveillance videos are often subjected to different noises that corrupt structured patterns and fine edges. This makes the image processing methods even more difficult, for instance, object detection, motion segmentation, tracking, identification and recognition of humans. This paper proposes a novel filtering technique named robust bilateral and temporal (RBLT), which resorts to a spatial and temporal evolution of sequences to conduct the filtering process while preserving relevant image information. A pixel value is estimated using a robust combination of spatial characteristics of the pixel's neighborhood and its own temporal evolution. Thus, robust statics concepts and temporal correlation between consecutive images are incorporated together which results in a reliable and configurable filter formulation that makes it possible to reconstruct highly dynamic and degraded image sequences. The filtering is evaluated using qualitative judgments and several assessment metrics, for different Gaussian and Salt Pepper noise conditions. Extensive experiments considering videos obtained by stationary and non-stationary cameras prove that the proposed technique achieves a good perceptual quality of filtering sequences corrupted with a strong noise component.
  • Item
    A Flow-based Motion Perception Technique for an Autonomous Robot System
    ( 2014) Andry Maykol Pinto ; António Paulo Moreira ; Miguel Velhote Correia ; Paulo José Costa
    Visual motion perception from a moving observer is the most often encountered case in real life situations. It is a complex and challenging problem, although, it can promote the arising of new applications. This article presents an innovative and autonomous robotic system designed for active surveillance and a dense optical flow technique. Several optical flow techniques have been proposed for motion perception however, most of them are too computationally demanding for autonomous mobile systems. The proposed HybridTree method is able to identify the intrinsic nature of the motion by performing two consecutive operations: expectation and sensing. Descriptive properties of the image are retrieved using a tree-based scheme and during the expectation phase. In the sensing operation, the properties of image regions are used by a hybrid and hierarchical optical flow structure to estimate the flow field. The experiments prove that the proposed method extracts reliable visual motion information in a short period of time and is more suitable for applications that do not have specialized computer devices. Therefore, the HybridTree differs from other techniques since it introduces a new perspective for the motion perception computation: high level information about the image sequence is integrated into the estimation of the optical flow. In addition, it meets most of the robotic or surveillance demands and the resulting flow field is less computationally demanding comparatively to other state-of-the-art methods.
  • Item
    Formation control driven by cooperative object tracking
    ( 2015) Lima,PU ; Ahmad,A ; André Dias ; Conceicao,AGS ; António Paulo Moreira ; Eduardo Silva ; Almeida,L ; Oliveira,L ; Nascimento,TP
    In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles.
  • Item
    Homing a robot with range-only measurements under unknown drifts
    ( 2015) Bruno Miguel Ferreira ; Aníbal Matos ; Nuno Cruz ; António Paulo Moreira
    The problem of homing a mobile robot to a given reference location under unknown relative and absolute positions is addressed in this paper. This problem is easy to solve when all the positions and kinematic variables are known or are observable, but remains a challenge when only range is measured. Its complexity further increases when variable and unknown drifts are added to the motion, which is typical for marine vehicles. Based on the range measurements, it is possible to drive the robot arbitrarily close to the reference. This paper presents a complete solution and demonstrates the validity of the approach based on the Lyapunov theory. The use of models, which are often affected by uncertainties and/or unmodeled terms, is intended to be minimal and only some constraints are imposed on the speed of the robot. We derive a control law that makes the robot converge asymptotically to the reference and prove its stability theoretically. Nevertheless, as it is well known, practical limitations on the actuation can weaken some properties of convergence, namely when the system dynamics require increasing actuation along the approach trajectory. We will demonstrate that the robot reaches a positively invariant set around the reference whose upper bound is determined. Finally, we conclude our work by presenting simulation and experimental data and by demonstrating the validity and the robustness of the method.
  • Item
    A Localization Method Based on Map-Matching and Particle Swarm Optimization
    ( 2015) Andry Maykol Pinto ; António Paulo Moreira ; Paulo José Costa
    This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory, a new robotic competition which is started in Lisbon in 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that performs well. The sensor information is continuously updated in time and space according to the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, the Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high, meaning that the map-matching is unreliable and the robot gets lost. The experiments presented in this paper prove the ability and accuracy of the presented technique to locate small mobile robots for this competition. Extensive results show that the proposed method presents an interesting localization capability for robots equipped with a limited amount of sensors, but also less reliable sensors.
  • Item
    Unsupervised flow-based motion analysis for an autonomous moving system
    ( 2014) Andry Maykol Pinto ; Miguel Velhote Correia ; António Paulo Moreira ; Paulo José Costa
    This article discusses the motion analysis based on dense optical flow fields and for a new generation of robotic moving systems with real-time constraints. It focuses on a surveillance scenario where an especially designed autonomous mobile robot uses a monocular camera for perceiving motion in the environment. The computational resources and the processing-time are two of the most critical aspects in robotics and therefore, two non-parametric techniques are proposed, namely, the Hybrid Hierarchical Optical Flow Segmentation and the Hybrid Density-Based Optical Flow Segmentation. Both methods are able to extract the moving objects by performing two consecutive operations: refining and collecting. During the refining phase, the flow field is decomposed in a set of clusters and based on descriptive motion properties. These properties are used in the collecting stage by a hierarchical or density-based scheme to merge the set of clusters that represent different motion models. In addition, a model selection method is introduced. This novel method analyzes the flow field and estimates the number of distinct moving objects using a Bayesian formulation. The research evaluates the performance achieved by the methods in a realistic surveillance situation. The experiments conducted proved that the proposed methods extract reliable motion information in real-time and without using specialized computers. Moreover, the resulting segmentation is less computationally demanding compared to other recent methods and therefore, they are suitable for most of the robotic or surveillance applications.
  • Item
    Visual motion perception for mobile robots through dense optical flow fields
    ( 2017) Andry Maykol Pinto ; Paulo José Costa ; Miguel Velhote Correia ; Aníbal Matos ; António Paulo Moreira
    Recent advances in visual motion detection and interpretation have made possible the rising of new robotic systems for autonomous and active surveillance. In this line of research, the current work discusses motion perception by proposing a novel technique that analyzes dense flow fields and distinguishes several regions with distinct motion models. The method is called Wise Optical Flow Clustering (WOFC) and extracts the moving objects by performing two consecutive operations: evaluating and resetting. Motion properties of the flow field are retrieved and described in the evaluation phase, which provides high level information about the spatial segmentation of the flow field. During the resetting operation, these properties are combined and used to feed a guided segmentation approach. The WOFC requires information about the number of motion models and, therefore, this paper introduces a model selection method based on a Bayesian approach that balances the model's fitness and complexity. It combines the correlation of a histogram-based analysis with the decay ratio of the normalized entropy criterion. This approach interprets the flow field and gives an estimative about the number of moving objects. The experiments conducted in a realistic environment have proved that the WOFC presents several advantages that meet the requirements of common robotic and surveillance applications: is computationally efficient and provides a pixel-wise segmentation, comparatively to other state-of-the-art methods.
  • Item
    WirelessSyncroVision: Wireless synchronization for industrial stereoscopic systems
    ( 2016) Andry Maykol Pinto ; António Paulo Moreira ; Paulo José Costa
    The research proposes a novel technological solution for marker-based human motion capture called WirelessSyncroVision (WSV). The WSV is formed by two main modules: the visual node (WSV-V) which is based on a stereoscopic vision system and the marker node (WSV-M) that is constituted by a 6-DOF active marker. The solution synchronizes the acquisition of images in remote muti-cameras with the ON period of the active marker. This increases the robustness of the stereoscopic system to illumination changes, which is extremely relevant for programming industrial robotic-arms using a human demonstrator programming by demonstration (PbD). In addition, the research presents a robust method named Adaptive and Robust Synchronization (ARS), that is designed for temporal alignment of remote devices using a wireless network. The algorithm models the phase difference as a function of time, measuring the parameters that must be known to predict the synchronization instant between the active marker and the remote cameras. Results demonstrate that the ARS creates a balance between the real-time capability and the performance estimation of the phase difference. Therefore, this research proposes an elegant solution to synchronize image acquisition systems in real-time that is easy to implement with low operational costs; however, the major advantage of the WSV is related to its high level of flexibility since it can be extended toward to other devices besides the PbD, for instance, motion capture, motion analysis, and remote sensoring systems.