A Multi-Agent Architecture for the Design of Hierarchical Interval Type-2 Beta Fuzzy System

This paper presents a new methodology for building and evolving hierarchical fuzzy systems. For the system design, a tree-based encoding method is adopted to hierarchically link low dimensional fuzzy systems. Such tree structural representation has by nature a flexible design offering more adjustable and modifiable structures. The proposed hierarchical structure employs a type-2 beta fuzzy system to cope with the faced uncertainties, and the resulting system is called the Hierarchical Interval Type-2 Beta Fuzzy System (HT2BFS). For the system optimization, two main tasks of structure learning and parameter tuning are applied. The structure learning phase aims to evolve and learn the structures of a population of HT2BFS in a multiobjective context taking into account the optimization of both the accuracy and the interpretability metrics. The parameter tuning phase is applied to refine and adjust the parameters of the system. To accomplish these two tasks in the most optimal and faster way, we further employ a multi-agent architecture to provide both a distributed and a cooperative management of the optimization tasks. Agents are divided into two different types based on their functions: a structure agent and a parameter agent. The main function of the structure agent is to perform a multi-objective evolutionary structure learning step by means of the Multi-Objective Immune Programming algorithm (MOIP). The parameter agents have the function of managing different hierarchical structures simultaneously to refine their parameters by means of the Hybrid Harmony Search algorithm (HHS). In this architecture, agents use cooperation and communication concepts to create high-performance HT2BFSs. The performance of the proposed system is evaluated by several comparisons with various state of art approaches on noise-free and noisy time series prediction data sets and regression problems. The results clearly demonstrate a great improvement in the accuracy rate, the convergence speed and the number of used rules as compared with other existing approaches

HYBRID FUZZY CONTROL AND ANT COLONY OPTIMIZATION BASED PATH PLANNING FOR WHEEL MOBILE ROBOT NAVIGATION

Wheeled Mobile Robot (WMR) is extremely important for active target tracking control and reactive obstacle avoidance in an unstructured environment. A WMR needs the best control performance an automatic path planning to maintain a very high level of accuracy. Therefore, the development of control strategies and path planning is very significant. Hence, research was carried out to investigate the control and path planning issues of WMR in dynamic environment. Several controllers such as conventional controller Proportional (P), Integral (I), Derivative (D) and Fuzzy Logic controller were investigated. A Hybrid Controller for differential WMR was proposed. Various aspects of the research on WMR such as kinematics model, conventional controller, fuzzy controller and hybrid controller were discussed. Overall it was found that on average the Hybrid Controller gives the best performance with 5.5s, 5.4s and 11s for target of 10x 10y, 30x10y and 60x20y respectively

Decentralized algorithm of dynamic task allocation for a swarm of homogeneous robots

The current trends in the robotics field have led to the development of large-scale swarm robot systems, which are deployed for complex missions. The robots in these systems must communicate and interact with each other and with their environment for complex task processing. A major problem for this trend is the poor task planning mechanism, which includes both task decomposition and task allocation. Task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize the cost while satisfying operational constraints. Task allocation mechanism must be run by each robot, which integrates the swarm whenever it senses a change in the environment to make sure the robot is assigned to the most appropriate task, if not, the robot should reassign itself to its nearest task. The main contribution in this thesis is to maximize the overall efficiency of the system by minimizing the total time needed to accomplish the dynamic task allocation problem. The near-optimal allocation schemes are found using a novel hybrid decentralized algorithm for a dynamic task allocation in a swarm of homogeneous robots, where the number of the tasks is more than the robots present in the system. This hybrid approach is based on both the Simulated Annealing (SA) optimization technique combined with the Discrete Particle Swarm Optimization (DPSO) technique. Also, another major contribution in this thesis is the formulation of the dynamic task allocation equations for the homogeneous swarm robotics using integer linear programming and the cost function and constraints are introduced for the given problem. Then, the DPSO and SA algorithms are developed to accomplish the task in a minimal time. Simulation is implemented using only two test cases via MATLAB. Simulation results show that PSO exhibits a smaller and more stable convergence characteristics and SA technique owns a better quality solution. Then, after developing the hybrid algorithm, which combines SA with PSO, simulation instances are extended to include fifteen more test cases with different swarm dimensions to ensure the robustness and scalability of the proposed algorithm over the traditional PSO and SA optimization techniques. Based on the simulation results, the hybrid DPSO/SA approach proves to have a higher efficiency in both small and large swarm sizes than the other traditional algorithms such as Particle Swarm Optimization technique and Simulated Annealing technique. The simulation results also demonstrate that the proposed approach can dislodge a state from a local minimum and guide it to the global minimum. Thus, the contributions of the proposed hybrid DPSO/SA algorithm involve possessing both the pros of high quality solution in SA and the fast convergence time capability in PSO. Also, a parameters\u27 selection process for the hybrid algorithm is proposed as a further contribution in an attempt to enhance the algorithm efficiency because the heuristic optimization techniques are very sensitive to any parameter changes. In addition, Verification is performed to ensure the effectiveness of the proposed algorithm by comparing it with results of an exact solver in terms of computational time, number of iterations and quality of solution. The exact solver that is used in this research is the Hungarian algorithm. This comparison shows that the proposed algorithm gives a superior performance in almost all swarm sizes with both stable and small execution time. However, it also shows that the proposed hybrid algorithm\u27s cost values which is the distance traveled by the robots to perform the tasks are larger than the cost values of the Hungarian algorithm but the execution time of the hybrid algorithm is much better. Finally, one last contribution in this thesis is that the proposed algorithm is implemented and extensively tested in a real experiment using a swarm of 4 robots. The robots that are used in the real experiment called Elisa-III robots

HYBRID FUZZY CONTROL AND ANT COLONY OPTIMIZATION BASED PATH PLANNING FOR WHEEL MOBILE ROBOT NAVIGATION

Wheeled Mobile Robot (WMR) is extremely important for active target tracking control and reactive obstacle avoidance in an unstructured environment. A WMR needs the best control performance an automatic path planning to maintain a very high level of accuracy. Therefore, the development of control strategies and path planning is very significant. Hence, research was carried out to investigate the control and path planning issues of WMR in dynamic environment. Several controllers such as conventional controller Proportional (P), Integral (I), Derivative (D) and Fuzzy Logic controller were investigated. A Hybrid Controller for differential WMR was proposed. Various aspects of the research on WMR such as kinematics model, conventional controller, fuzzy controller and hybrid controller were discussed. Overall it was found that on average the Hybrid Controller gives the best performance with 5.5s, 5.4s and 11s for target of 10x 10y, 30x10y and 60x20y respectively

Modelado, control y optimización de un sistema multi-robot autónomo para transporte inteligente

La robótica es el área de la ingeniería dedicada al diseño, construcción y control de máquinas capaces de resolver problemas a los humanos. Es una rama interdisciplinar que, aunando los conocimientos de diversas ciencias, ingenierías y tecnologías, busca imitar o extender las acciones y capacidades de las personas. Son incontables las labores en las que los robots demuestran un desempeño mayor que los humanos, como bien es consciente el sector industrial. Su velocidad, precisión y repetibilidad en tareas monótonas están un orden de magnitud por encima de las habilidades humanas. Sin embargo, la habilidad de establecer vínculos entre ellos, coordinarse e integrarse en su entorno de manera eficaz sigue siendo una tarea pendiente. Con esta finalidad, el presente trabajo expone los problemas diversos e interrelacionados entre sí que se deben abordar para conseguir aunar los esfuerzos de múltiples robots para conseguir un objetivo común. Estos retos comienzan con la percepción del entorno y la manera en la que se modela. Mediante dispositivos tipo lidar y el mapeado y localización simultáneos (SLAM) se consigue una descripción fiel del mundo que rodea al robot. Aun así, también se manifiesta en el trabajo las limitaciones que sufren estos dispositivos. La construcción de un mapa es la base sobre la que se asienta una navegación autónoma eficiente. Los algoritmos de navegación continúan estudiándose desde los inicios de la robótica móvil, sin embargo, cada uno de ellos ofrece un enfoque distinto que se adecua a cada caso. Por ello, se han llevado a cabo pruebas para comprobar cuales permiten desplazarse en un menor tiempo para su implementación en el robot. Una vez tiene la capacidad de navegar por sí mismo, el siguiente gran paso es otorgarle la habilidad de sortear obstáculos imprevistos. En el mundo real, y teniendo en mente la aplicación de estos robots en almacenes o invernaderos, estos podrán ser tanto estáticos como dinámicos. Aunando estos dos requerimientos, se ha demostrado que los algoritmos de navegación basados en bandas elásticas temporales ofrecen una gran velocidad y robustez en la navegación autónoma a la vez que muestran gran habilidad en el sorteo de obstáculos. Esto permite una navegación multi-agente en un mismo espacio físico, ya que se detectan mutuamente como impedimentos en su trayectoria y la modifican para no colisionar. Este control se ha implementado a nivel local, para asegurar una mayor tolerancia a fallos en caso de problemas de comunicación con el servidor central. Finalmente, la cooperación de la que se hablaba al inicio de este resumen se alcanza mediante un reparto eficiente de las tareas a realizar. El enfoque adoptado está basado en un esquema de subasta y licitador, en el que cada tarea es ejecutada por aquel robot que muestre una mayor predisposición para ello. De esta forma, se logra reducir a la mitad el tiempo requerido para llevar a cabo una serie de tareas de transporte cuando se duplica el número de robots en el sistema. Estos resultados se han comprobado tanto en simulación como con los robots físicos, validando así la implementación del sistema multi-robot. Abstract: Robotics is the branch of engineering devoted to design, construction and control of machines that can resolve human problems. It is an interdisciplinary research field which, combining various sciences, engineerings and technologies, aims to mimic or extend humans’ capacities. There are countless chores in which robots show a better performance than human beings, as the industrial sector is well aware of. Their speed, precision, and repeatability in monotonous jobs are in a superior order of magnitude. In contrast, their hability to establish links and coordinate between themselves and with their environment is still a pending task. In this regard, this work reveals the diverse and interrelated problems that arise when trying to merge cooperatively the efforts of a multi-agent system towards a common goal. These challenges begin with the perception of the world and the way to model it. With lidar-based simultaneous localization and mapping (SLAM), as for this project, a faithful description of the robots’ surroundings is accomplished. However, this devices present some limitations under certain conditions. Building a map is the essential pillar in which an efficient autonomous navigation bases its foundations. Navigation algorithms have been studied since the dawn of mobile robotics, but each of them offers a different approach that fits a particular requirement. For that reason, several experiments were carried out to determine the most suitable one in terms of time spent for its later implementation in the real robots. Once this demand is met, the next big step is to give the robot the hability to avoid unforeseen obstacles. In the real world, and minding the application of the robots in warehouses or greenhouses environments, these can be static or dynamic. Joining those two specifications, it has been shown that time elastic bands navigation algorithms are a great solution in means of speed, robustness in autonomous navigation as well as in the capability of obstacles avoidance. This allows multi-agent navigation in the same confined space, as they can detect each other and modify their trajectory to steer clear of the other’s. This control has been developed in a local way for a safer fault tolerance in case of communication issues with the central server. Finally, the cooperation this abstract started with, is achieved with an efficient distribution of tasks. This approach is based in a auctioneer and bidder scheme, in which each chore is executed by the most suited agent of the system. Therefore, the time required to perform these transport jobs is halved when the number of robots doubles. These results have been tested in simulation and real experiments, which validates the implementation of the multi-robot system

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Effects of Dynamically Weighting Autonomous Rules in a UAS Flocking Model

Within the U.S. military, senior decision-makers and researchers alike have postulated that vast improvements could be made to current Unmanned Aircraft Systems (UAS) Concepts of Operation through inclusion of autonomous flocking. Myriad methods of implementation and desirable mission sets for this technology have been identified in the literature; however, this thesis posits that specific missions and behaviors are best suited for autonomous military flocking implementations. Adding to Craig Reynolds\u27 basic theory that three naturally observed rules can be used as building blocks for simulating flocking behavior, new rules are proposed and defined in the development of an autonomous flocking UAS model. Simulation validates that missions of military utility can be accomplished in this method through incorporation of dynamic event- and time-based rule weights. Additionally, a methodology is proposed and demonstrated that iteratively improves simulated mission effectiveness. Quantitative analysis is presented on data from 570 simulation runs, which verifies the hypothesis that iterative changes to rule parameters and weights demonstrate significant improvement over baseline performance. For a 36 square mile scenario, results show a 100% increase in finding targets, a 40.2% reduction in time to find a target, a 4.5% increase in area coverage, with a 0% attribution rate due to collisions and near misses