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Marnoto de Oliveira Campos, Francisco Mateus
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- Global localization with non-quantized local image featuresPublication . Campos, Francisco M.; Correia, Luís; Calado, João Manuel FerreiraIn the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.
- Supervision System 4.0 for a Road Tanker Washing Robot ManipulatorPublication . Vicente, L.; Carreira, Fernando; Campos, Francisco M.; Gonçalves Cavaco Mendes, Mário José; Calado, João Manuel Ferreira; Carvalho, GamboaThe washing of road tankers is currently still a manual process that requires an operator to place the washing head into the tanks. To increase productivity and operator safety, it is essential to implement automated systems with Fault Detection and Isolation (FDI) capabilities. On the other hand, the industry 4.0 paradigm promotes the use of collaborative systems that integrate with the other organization’s processes. Realizing this new vision requires Supervision, Control and Data Acquisition (SCADA) systems with FDI modules that integrate with collaborative systems and promote the digitalization of companies. This paper proposes a SCADA system for a new road tanker washing robot, aimed at integrating all systems and communication networks of the organization and future FDI modules. To this end, this paper proposes a communication architecture based on open protocols and a common database to connect SCADA to the lower and higher levels of automation. Furthermore, this paper describes the various aspects of SCADA system development, from synoptic design to validation. To support the development of the SCADA system, a Digital Twin (DT) of the road tanker washing robot was used, allowing to test and validate its functionality through this virtual prototype. The results show that the proposed SCADA system and underlying information architecture are suitable for supervision of a robotized wash operation and that the use of a Digital Twin facilitates SCADA system design and validation.
- Finding a short path for mobile robot arm coverage of a point setPublication . Campos, Francisco M.; Carreira, Fernando; Calado, João Manuel FerreiraThis paper introduces the problema of Mobile Robot Arm Covering (MRAC) along with a three-step procedure to solve it. In Mobile Robot Arm Covering one seeks the shortest path of a mobile robot equipped with a manipulator such that the manipulator workspace covers a given set of geometric entities. In this paper we consider the problema of covering a set of points. This is solved by a three-step procedure: the search space is first discretized into a finite set of robot poses; then the resulting combinatorial problem is solved by a memetic algorithm and, finally, the given solution is improved in the continuous space. Two popular discretization schemes developed for the related Close Enough Traveling Salesman Problem (CETSP) are evaluated in the MRAC context. Futhermore, a new memetic algorithm to solve MRAC and CETSP instances is developed. This algorithm overcomes the limitations of the approaches based on General Traveling Salesman Problem (GTSP) solvers, namely, the difficulty in handling large problems and the large computational times required to solve them.
- Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approachesPublication . Campos, Francisco M.; Correia, Luís; Calado, João Manuel FerreiraIn the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.
- An Evaluation of Local Feature Combiners for Robot Visual LocalizationPublication . Campos, Francisco M.; Correia, Luis; Calado, João Manuel FerreiraIn the last decade, local image features have been widely used in robot visual localization. To assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image to those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, we compare several candidate combiners with respect to their performance in the visual localization task. A deeper insight into the potential of the sum and product combiners is provided by testing two extensions of these algebraic rules: threshold and weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance. The voting method, whilst competitive to the algebraic rules in their standard form, is shown to be outperformed by both their modified versions.