Gerardo G. Acosta

Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation

This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera^^^(R) micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on users prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robots navigation in an unknown environment is used as a test bed for the proposed scaling strategies.

Genetic algorithms and fuzzy control: a practical synergism for industrial applications

A way to automatically generate fuzzy controllers (FCs) that are optimized according to a merit figure is presented in this article. To achieve this task, a procedure based on hierarchical genetic algorithms (HGA) was developed. This procedure and the manner in which fuzzy controllers are codified into chromosomes is described. Resorting to this tool, several fuzzy controllers were constructed. The best three solutions obtained during simulation were selected for testing using an experimental prototype, which consists of an induction motor of variable load. These preliminary results are also included in the report. Based on these results, it is concluded that hierarchical genetic algorithms, though not the only, is a suitable artificial intelligence technique to face the problem of setting a fuzzy controller in a control loop without previous experience in controlling the plant. This is of help in many situations at industrial environments.

Basic tasks for knowledge-based supervision in process control

Abstract A new tasks taxonomy for knowledge-based global supervision (GS) of continuous industrial processes is introduced in this work. Possible required tasks are specified together with the analysis of their dimensions, which should be useful in the selection of the final capabilities of supervision. Moreover, these dimensions would help end-users and designers when comparing different systems. Several methodologies based on concepts such as generic task, generic operation or heuristic classification have been proposed to transform knowledge-based system (KBS) development in a systematic knowledge engineering activity. These approaches have been quite successful in domains such as medicine or mineral prospecting, identifying a large number of tasks that experts in the domain articulate to solve the problem. However, this was not the case in the process control area. The selection of tasks and their capabilities is the first step to be taken, even before choosing a KBS analysis and design methodology. Authors found a lack of facilities to do this selection in the aforementioned approaches when they tried to develop a global supervision tool in a beet sugar factory in Spain. Hence, this article describes an attempt to fill this gap. Moreover, it shows how this taxonomy supported the analysis and design stages of a supervision tool in the mentioned industrial application.

Knowledge based process control supervision and diagnosis: the AEROLID approach

Abstract The artificial intelligence incidence in process control, although an active area in the researchers community and even with some implementations at industrial environment, is not sufficiently evaluated in numerical terms for the long term. The present article shows such an evaluation of a knowledge based system, developing supervisory control tasks in the sugar production from sugar-beet, and paying particular attention to fault detection and diagnosis. A way of conceiving supervision for continuous processes is presented and supported with this industrial application. The expert system carrying out supervisory tasks operates in a VAX ® workstation, directly over the distributed control system. The expert system development tool is G2 ® which has real-time facilities. Although the core system was developed in G2, it also consists of some external modules because it combines both analytical and artificial intelligence problem resolution techniques. The global architecture, as well as the implementation details of the modules necessary for fault identification, are presented altogether with the experimental results obtained from the factory field.

From network-to-antibody robustness in a bio-inspired immune system

Behavioural robustness at antibody and immune network level is discussed. The robustness of the immune response that drives an autonomous mobile robot is examined with two computational experiments in the autonomous mobile robots trajectory generation context in unknown environments. The immune response is met based on the immune network metaphor for different low-level behaviours coordination. These behaviours are activated when a robot sense the appropriate conditions in the environment in relation to the network current state. Results are obtained over a case study in computer simulation as well as in laboratory experiments with a Khepera II microrobot. In this work, we develop a set of tests where such an immune response is externally perturbed at network or low-level behavioural modules to analyse the robust capacity of the system to unexpected perturbations. Emergence of robust behaviour and high-level immune response relates to the coupling between behavioural modules that are selectively engaged with the environment based on immune response. Experimental evidence leads discussions on a dynamical systems perspective of behavioural robustness in artificial immune systems that goes beyond the isolated immune network response.

Experimental results on smooth path tracking with application to pipe surveying on inexpensive AUV

This paper details the development aspects of a low cost AUV autonomous, designed for autonomous pipline inspections, describing: hardware, software and control aspects. The article details three of the mains stages of the project, that have been already achieved: (a) the simulation results of Lyapunov based path planning of torpedo shaped AUV on pipe searching; (b) the construction details of dual torpedo AUV for pipeline inspection and (c) the experimental results when using the prototype in path following using the line of sight (LOS) algorithm.

Trajectory Tracking of the Cormoran AUV Based on a PI-MIMO Approach

This paper describes the control strategy developed for the Cormoran, a low cost ocean observing platform, hybrid between AUVs and ASVs. This robot moves slightly underneath the sea surface following a previously planned route and regularly dives to make vertical profiles of the water column. Obtained data is transmitted in real time to the laboratory and assimilated into a numerical coastal model. The control structure proposed in this article comprises a Navigation Block and a MIMO Control Block. The former is based on a following Lyapunov-based algorithm and computes on-line the reference signals required by the latter to reach and robustly track the desired trajectory. The MIMO Control Block architecture is based on a PI-MIMO control strategy that commands the rudder and the propeller. Computer simulations utilizing a dynamic model of the Cormoran and realistic disturbances are presented.

Accumulated CA–CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data

This paper describes a novel approach to object detection from sidescan sonar (SSS) acoustical images. The current techniques of acoustical images processing consume a great deal of time and computational resources with many parameters to tune in order to obtain good quality images. This is due to the handling of the large data volume generated by these kinds of devices. The technique proposed in this work does not make any a priori assumption about the nature of the SSS image to be processed. However, it is able to make a segmentation of the image into two types of regions: acoustical highlight and seafloor reverberation areas, and based on this, it makes detection. The developed algorithm to achieve this consists of a migration and adaptation of a technique widely used in radar technology for detecting moving objects. This radar technique is known as the cell average-constant false alarm rate (CA-CFAR). This paper presents a drastic improvement of such approach by making an extension into 2-D analysis of the SSS image, in a way similar to integral image used in CA-CFAR detection for pulse Doppler radar. In this form, optimization of the computational effort is achieved. This new technique was called the accumulated cell average-constant false alarm rate in 2-D (ACA-CFAR 2-D). It was applied to pipeline detection and tracking with a very interesting degree of success. In addition, this technique provides similar results to image segmentation with respect to other frequently used approaches, but with much less computational resources and parameters to set. Its simplicity is a strong support of its robustness and accuracy. This feature makes it particularly attractive for using it in real-time applications, such as underwater robotics perception systems. This proposal was tested experimentally with acoustical data from SSS and the results detecting pipelines, and other shapes like sunken vessels or airplanes, are presented in this paper. Likewise, an experimental comparison with the results obtained with inverse undecimated discrete wavelet transform (UDWT) and active contours techniques is also presented.

How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields

Researchers in diverse fields, such as in neuroscience, systems biology and autonomous robotics, have been intrigued by the origin and mechanisms for biological robustness. Darwinian evolution, in general, has suggested that adaptive mechanisms as a way of reaching robustness, could evolve by natural selection acting successively on numerous heritable variations. However, is this understanding enough for realizing how biological systems remain robust during their interactions with the surroundings? Here, we describe selected studies of bio-inspired systems that show behavioral robustness. From neurorobotics, cognitive, self-organizing and artificial immune system perspectives, our discussions focus mainly on how robust behaviors evolve or emerge in these systems, having the capacity of interacting with their surroundings. These descriptions are twofold. Initially, we introduce examples from autonomous robotics to illustrate how the process of designing robust control can be idealized in complex environments for autonomous navigation in terrain and underwater vehicles. We also include descriptions of bio-inspired self-organizing systems. Then, we introduce other studies that contextualize experimental evolution with simulated organisms and physical robots to exemplify how the process of natural selection can lead to the evolution of robustness by means of adaptive behaviors.

Fault Detection and Diagnosis Techniques in Induction Electrical Machines

A GREAT AND VARIED DEAL OF APPROACHES FOR FAULT DETECTION AND DIAGNOSIS IN INDUCTION MACHINES HAS BEEN PROPOSED AND IMPLEMENTED IN THE LAST YEARS. THESE NEW TECHNIQUES ARE ATTRACTIVE DUE TO THEY ARE CHARACTERIZED BY THEIR ON-LINE AND NON-INVASIVE FEATURES. THAT IS, THE ABILITY OF DETECTING FAULTS WHILE THE MACHINE IS UNDER NORMAL RUNNING AND WITHOUT NEEDING TO MOUNT SENSORS WITHIN THE MOTOR. THESE FEATURES, INHERENT OF THE NEW TECHNIQUES, DISTINGUISH THEM FROM THE TRADITIONAL ONES, WHICH MAINLY REQUIRE THAT THE MACHINE UNDER STUDY IS OUT OF SERVICE IN ORDER TO YIELD A DIAGNOSIS. THE PURPOSE OF THIS ARTICLE CONSISTS THEN IN REVIEWING THE PRINCIPAL ALTERNATIVES IN THE FIELD OF FAULT DIAGNOSIS FOR INDUCTION MACHINES AND TO COMPARE THEIR PERFORMANCE TAKING INTO ACCOUNT THE REQUIRED DIAGNOSTIC INFORMATION, THE NUMBER AND IMPORTANCE OF THE FAULTS THEY CAN DETECT, THEIR SPEED TO ANTICIPATE A FAULT AND THE CERTAINTY DEGREE IN THE FINAL DIAGNOSIS.