Behavior-based Control Architecture Research Articles

Evolving Software Architecture Design in Telemedicine: A PRISMA-based Systematic Review.

This article presents a systematic review of recent advancements in telemedicine architectures for continuous monitoring, providing a comprehensive overview of the evolving software engineering practices underpinning these systems. The review aims to illuminate the critical role of telemedicine in delivering healthcare services, especially during global health crises, and to emphasize the importance of effectiveness, security, interoperability, and scalability in these systems. A systematic review methodology was employed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. As the primary research method, the PubMed, IEEE Xplore, and Scopus databases were searched to identify articles relevant to telemedicine architectures for continuous monitoring. Seventeen articles were selected for analysis, and a methodical approach was employed to investigate and synthesize the findings. The review identified a notable trend towards the integration of emerging technologies into telemedicine architectures. Key areas of focus include interoperability, security, and scalability. Innovations such as cognitive radio technology, behavior-based control architectures, Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, cloud computing, decentralized systems, and blockchain technology are addressing challenges in remote healthcare delivery and continuous monitoring. This review highlights major advancements in telemedicine architectures, emphasizing the integration of advanced technologies to improve interoperability, security, and scalability. The findings underscore the successful application of cognitive radio technology, behavior-based control, HL7 FHIR standards, cloud computing, decentralized systems, and blockchain in advancing remote healthcare delivery.

Behavior-Based Control Architecture for Legged-and-Climber Robots

In this paper, we present a fully original control architecture for legged-and-climber robots that is level-based, hierarchical, and centralized. The architecture gives the robots the ability to perform self-reconfiguration after unforeseen leg failures, because it can control this kind of robot with different numbers of legs. The results show the capability of performing movements in any direction and inclination planes. The components and functionalities of the developed control architecture for these robots are described, and, the architecture’s performance is tested on the ROMHEX robot.

Sistema robótico autónomo para la exploración y construcción de mapas en entornos estructurados


 Objetivo: Construir un sistema robótico para la exploración y el mapeo de entornos interiores de trabajo con una arquitectura y una metodología de diseño propia. Metodología: Se definió una arquitectura y un diseño en la parte de hardware y en la parte lógica. En la parte lógica, se utilizó una representación del entorno basada en celdas de ocupación y una arquitectura de control fundada en comportamientos que fue implementada a través de un control fuzzy. Resultados: Se probó la plataforma robótica en escenarios estructurados como pasillos y salones. Su efectividad se comprobó mediante mapas construidos en un aplicativo desarrollado en Matlab. Conclusiones: Se demostró la capacidad de la plataforma robótica para construir mapas de su entorno de forma explícita, que, aun cuando pueden resultar más demandantes en lo computacional, permite una representación precisa y comprensible.

Design of Telemedicine Robot using Behavior-based Control Architecture with Two-Step Fuzzy Logic Optimization

Designing a telemedicine robot is a challenging task. Complexity, incomplete prior knowledge of the environment and unexpected situations set strict requirements for both the hardware and software components of the robot. Several robotics architectures have been proposed that try to cope with the above problems. The behavior-based approach has been established as the main alternative to new robot control. The paper presents the design and prototype of a telemedicine robot using behavior-based control architecture. Several behaviors will design to assist the robot, such as: Remotely control, seeing the situation and detecting obstacles when the robot maneuvers. A fuzzy behavior-based used in one of the behaviors. A Two-Step Fuzzy Optimization is used to optimize the fuzzy parameters and generate a robust and smooth braking system. Based on several experiments, the robot can maneuver properly as instructed. In general, it can be said that the design of telemedicine robots works with excellent performance.

Design of Telerobotic system using Bluetooth with Behaviour-based Control Architecture

Telerobotic system is needed in several environments to perform a process in a remote system using a robot. Some peripherals are connected and used in a telerobotic system. Unfortunately, to interconnected the peripherals, control remotely and perform a task entirely in limited time is not easy. It needs a robust control architecture. Several classic and modern control has been attempted. This paper proposed a behavior-based control architecture to control a telerobotic system using Bluetooth. Several experiments have been performed in a real robot. Based on some results, the proposed system able to run a telerobotic system to perform several tasks well.

WALL-FOLLOWING BEHAVIOR-BASED MOBILE ROBOT USING PARTICLE SWARM FUZZY CONTROLLER

Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.

Goal-seeking Behavior-based Mobile Robot Using Particle Swarm Fuzzy Controller

Behavior-based control architecture has successfully demonstrated their competence in mobile robot development. Fuzzy logic system characteristics are suitable to address the behavior design problems. However, there are difficulties encountered when setting fuzzy parameters manually. Therefore, most of the works in the field generate certain interest for the study of fuzzy systems with added learning capabilities. This paper presents the development of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A goal-seeking behaviors based on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations and experiments with MagellanPro mobile robot have been performed to analyze the performance of the algorithm. The promising results have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.

Command Fusion for Navigation of Mobile Robots in Dynamic Environments with Objects

In this paper, we propose a fuzzy inference model for a navigation algorithm for a mobile robot that intelligently searches goal location in unknown dynamic environments. Our model uses sensor fusion based on situational commands using an ultrasonic sensor. Instead of using the “physical sensor fusion” method, which generates the trajectory of a robot based upon the environment model and sensory data, a “command fusion” method is used to govern the robot motions. The navigation strategy is based on a combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance based on a hierarchical behavior-based control architecture. To identify the environments, a command fusion technique is introduced where the sensory data of the ultrasonic sensors and a vision sensor are fused into the identification process. The result of experiment has shown that highlights interesting aspects of the goal seeking, obstacle avoiding, decision making process that arise from navigation interaction.

A behaviour-based control architecture for heterogeneous modular, multi-configurable, chained micro-robots

This article presents a new control architecture designed for heterogeneous modular, multi-configurable, chained micro-robots. This architecture attempts to fill the gap that exists in heterogeneous modular robotics research, in which little work has been conducted compared to that in homogeneous modular robotics studies. The architecture proposes a three-layer structure with a behaviour-based, low-level embedded layer, a half-deliberative half-behaviour-based high layer for the central control, and a heterogeneous middle layer acting as a bridge between these two layers. This middle layer is very important because it allows the central control to treat all modules in the same manner, facilitating the control of the robot. A communication protocol and a module description language were also developed for the control architecture to facilitate communication and information flow between the heterogeneous modules and the central control. Owing to the heterogeneous behaviour of the architecture, the system can automatically reconfigure its actions to adapt to unpredicted events (such as actuator failure). Several behaviours (at low and high levels) are also presented here.

Coordination control of wheeled mobile robots â a hybrid approach

In this paper, a novel modelling and a hybrid formation control approach for the autonomous navigation for group of mobile robots in a specified formation is proposed. The hybrid approach consists of layered behaviour based control architecture with the behaviours classified into two levels. The supervisor level in the architecture handles the high-level missions such as formation and interobot communication, and the lower level deals with the dynamic control of the robots when navigating in the environment. The switching between the formation and the navigation is done based on the local information and the assigned role of the robots in the group. A modelling framework is also presented in this paper, where the individual behaviours of the hybrid formation control approach are modelled as temporal states and the performance of the approach is analysed through simulations. Experimental investigations have also been performed by using two commercially available robot research platforms. The results obtained are presented and discussed.

Combining Dense Structure from Motion and Visual SLAM in a Behavior-Based Robot Control Architecture

In this paper, we present a control architecture for an intelligent outdoor mobile robot. This enables the robot to navigate in a complex, natural outdoor environment, relying on only a single on-board camera as sensory input. This is achieved through a twofold analysis of the visual data stream: a dense structure from motion algorithm calculates a depth map of the environment and a visual simultaneous localization and mapping algorithm builds a map of the surroundings using image features. This information enables a behavior-based robot motion and path planner to navigate the robot through the environment. In this paper, we show the theoretical aspects of setting up this architecture.

Flocking for multi-robot systems via the Null-Space-based Behavioral control

Flocking is the way in which populations of animals like birds, fishes, and insects move together. In such cases, the global behavior of the team emerges as a consequence of local interactions among the neighboring members. This paper approaches the problem of letting a group of robots flock by resorting to a behavior-based control architecture, namely Null-Space-based Behavioral (NSB) control. Following such a control architecture, very simple behaviors for each robot are defined and properly arranged in priority in order to achieve the assigned mission. In particular, flocking is performed in a decentralized manner, that is, the behaviors of each robot only depend on local information concerning the robot’s neighbors. In this paper, the flocking behavior is analyzed in a variety of conditions: with or without a moving rendez-vous point, in a two- or three-dimensional space and in presence of obstacles. Extensive simulations and experiments performed with a team of differential-drive mobile robots show the effectiveness of the proposed algorithm.

Development of complex robotic systems using the behavior-based control architecture iB2C

This paper presents a development methodology for complex robotic systems using the behavior-based control architecture iB2C ( integrated Behavior- Based Control). It is shown how architectural principles support several behavior-based mechanisms, e.g. coordination mechanisms, behavior interaction, and hierarchical abstraction. Furthermore, design guidelines and structural patterns are presented which support the design and implementation process. The provided analysis tools and visualization techniques help to manage the complexity of large behavior-based networks. Finally, application examples are presented and a step by step description of constructing a behavior-based control structure for the outdoor robot Ravon is given.

이동 로봇을 위한 행위 기반 제어 및 학습 구조의 설계와 구현

A behavior-based control and learning architecture is proposed, where reinforcement learning is applied to learn proper associations between stimulus and response by using two types of memory called as short Term Memory and Long Term Memory. In particular, to solve delayed-reward problem, a knowledge-propagation (KP) method is proposed, where well-designed or well-trained S-R(stimulus-response) associations for low-level sensors are utilized to learn new S-R associations for high-level sensors, in case that those S-R associations require the same objective such as obstacle avoidance. To show the validity of our proposed KP method, comparative experiments are performed for the cases that (ⅰ) only a delayed reward is used, (ⅱ) some of S-R pairs are preprogrammed, (ⅲ) immediate reward is possible, and (ⅳ) the proposed KP method is applied.

A Behaviour-Based Control Architecture for Visually Guiding an Underwater Cable Tracker

Nowadays, the surveillance and inspection of underwater installations, such as power andtelecommunication cables and pipelines, is carried out by trained operators who, from the surface, control a Remotely Operated Vehicle (ROV) with cameras mounted over it. This is a tedious, time-consuming and expensive task, prone to errors mainly because of loss of attention or fatigue of the operator and also due to the typical low quality of seabed images. In this study, the development of a control architecture for visually guiding an Autonomous Underwater Vehicle (AUV) to detect and track an underwater power cable laid on the seabed has been the main concern. The vision subsystem was tested using sequences from a video tape obtained by means of a ROV during several tracking sessions of various real cables, while the performance of the whole system has been assessed using a 3D simulator capable of incorporating the hydrodynamic model of any real underwater vehicle.

Intelligent control of nonholonomic mobile robots with fuzzy perception

This paper is focused on the intelligent control of vehicles with nonholonomic constraints. A new method to compute a fuzzy perception of the environment is presented, dealing with the uncertainties and imprecisions from the sensorial system and taking into account nonholonomic constraints of the vehicle. This fuzzy perception is used, both in the design of each reactive behavior and solving the problem of behavior combination, to implement a fuzzy behavior-based control architecture. Furthermore, teleoperation and planned behaviors, together with their combination with reactive ones, are considered; improving the capabilities of the intelligent control system and its practical application. Experimental results, of an application to control the ROMEO-3R autonomous vehicle, demonstrate the robustness of the proposed method.

Parallel and diagonal parking in nonholonomic autonomous vehicles

This paper considers the problem of parallel and diagonal parking in wheeled vehicles. A method to plan in real-time a set of collision-free manoeuvres is presented. Artificial intelligent techniques, namely fuzzy logic, play an important role in the practical application of the method. Thus, a fuzzy system is used to select the most suitable manoeuvre from the solution set according with the environment, dealing with optimality, path tracking performance and collision avoidance trade-off. This technique has been implemented in a fuzzy behaviour-based control architecture combining planning and reactivity. The efficiency of the proposed method is demonstrated using the nonholonomic mobile robot ROMEO-3R designed and built at the University of Seville.

A behavior-based mobile robot architecture for Learning from Demonstration

Autonomous mobile robots (AMRs), to be truly flexible, should be equipped with learning capabilities, which allow them to adapt effectively to a dynamic and changing environment. This paper proposes a modular, behavior-based control architecture, which is particularly suited for “Learning from Demonstration” experiments in the spatial domain. The robot learns sensory-motor behaviors online by observing the actions of a person, another robot or another behavior. Offline learning phases are not necessary but might be used to trim the attained representation. First results applying RBF-approximation, growing neural cell structures and probabilistic models for progress estimation, are presented.

A behavior-based intelligent control architecture with application to coordination of multiple underwater vehicles

Presents a behavior-based intelligent control architecture for designing controllers which, based on their observation of sensor signals, compute the discrete control actions. These control actions then serve as the "set-points" for the lower level controllers. The behavior-based approach yields an intelligent controller which is a cascade of a perceptor and a response controller. The perceptor extracts the relevant symbolic information from the incoming continuous sensor signals, which enables the execution of one of the behaviors. The response controller is a discrete event system that computes the discrete control actions by executing one of the enabled behaviors. The behavioral approach additionally yields a hierarchical two layered response controller, which provides better complexity management. The inputs from the perceptor are used to first compute the higher level activities, called behaviors, and next to compute the corresponding lower level activities, called actions. The paper focuses on the discrete event subsystem, namely, the response controller. We illustrate the intelligent control architecture by discussing its application to the design of intelligent controllers for autonomous underwater vehicles used for ocean sampling missions. A complete set of discrete event models of the response controller of the underwater vehicles for the above application has been given, and their formal verification discussed.

Evolving complex robot behaviors

Building robots is a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to such difficulties in designing robots is to adopt learning methods. The evolution-based approach is a special method of machine learning and it has been advocated to automate the design of robots. Yet, the tasks achieved so far are fairly simple. In this work, we first analyze the difficulties of applying evolutionary approaches to synthesize robot controllers for complicated tasks, and then suggest an approach to resolve them. Instead of directly evolving a monolithic control system, we propose to decompose the overall task to fit in the behavior-based control architecture, and then to evolve the separate behavior modules and arbitrators using an evolutionary approach. Consequently, the job of defining fitness functions becomes more straightforward and the tasks easier to achieve. To assess the performance of the developed approach, we evolve a control system to achieve an application task of box-pushing as an example. Experimental results show the promise and efficiency of the presented approach.