Heterogeneous Self-Reconfiguring Robotics: Ph.D. Thesis Proposal

Self-reconfiguring robots are modular systems that can change shape, or reconfigure, to match structure to task. They comprise many small, discrete, often identical modules that connect together and that are minimally actuated. Global shape transformation is achieved by composing local motions. Systems with a single module type, known as homogeneous systems, gain fault tolerance, robustness and low production cost from module interchangeability. However, we are interested in heterogeneous systems, which include multiple types of modules such as those with sensors, batteries or wheels. We believe that heterogeneous systems offer the same benefits as homogeneous systems with the added ability to match not only structure to task, but also capability to task. Although significant results have been achieved in understanding homogeneous systems, research in heterogeneous systems is challenging as key algorithmic issues remain unexplored. We propose in this thesis to investigate questions in four main areas: 1) how to classify heterogeneous systems, 2) how to develop efficient heterogeneous reconfiguration algorithms with desired characteristics, 3) how to characterize the complexity of key algorithmic problems, and 4) how to apply these heterogeneous algorithms to perform useful new tasks in simulation and in the physical world. Our goal is to develop an algorithmic basis for heterogeneous systems. This has theoretical significance in that it addresses a major open problem in the field, and practical significance in providing self-reconfiguring robots with increased capabilities

Heterogeneous Self-Reconfiguring Robotics

Self-reconfiguring (SR) robots are modular systems that can autonomously change shape, or reconfigure, for increased versatility and adaptability in unknown environments. In this thesis, we investigate planning and control for systems of non-identical modules, known as heterogeneous SR robots. Although previous approaches rely on module homogeneity as a critical property, we show that the planning complexity of fundamental algorithmic problems in the heterogeneous case is equivalent to that of systems with identical modules. Primarily, we study the problem of how to plan shape changes while considering the placement of specific modules within the structure. We characterize this key challenge in terms of the amount of free space available to the robot and develop a series of decentralized reconfiguration planning algorithms that assume progressively more severe free space constraints and support reconfiguration among obstacles. In addition, we compose our basic planning techniques in different ways to address problems in the related task domains of positioning modules according to function, locomotion among obstacles, self-repair, and recognizing the achievement of distributed goal-states. We also describe the design of a novel simulation environment, implementation results using this simulator, and experimental results in hardware using a planar SR system called the Crystal Robot. These results encourage development of heterogeneous systems. Our algorithms enhance the versatility and adaptability of SR robots by enabling them to use functionally specialized components to match capability, in addition to shape, to the task at hand

A Game-theoretic Formulation of the Homogeneous Self-Reconfiguration Problem

In this paper we formulate the homogeneous two- and three-dimensional self-reconfiguration problem over discrete grids as a constrained potential game. We develop a game-theoretic learning algorithm based on the Metropolis-Hastings algorithm that solves the self-reconfiguration problem in a globally optimal fashion. Both a centralized and a fully distributed algorithm are presented and we show that the only stochastically stable state is the potential function maximizer, i.e. the desired target configuration. These algorithms compute transition probabilities in such a way that even though each agent acts in a self-interested way, the overall collective goal of self-reconfiguration is achieved. Simulation results confirm the feasibility of our approach and show convergence to desired target configurations.Comment: 8 pages, 5 figures, 2 algorithm

Autonomous Task-Based Evolutionary Design of Modular Robots

In an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is introduced based on design optimization concepts and evolutionary algorithms to search for the optimal design. Designing modular robotic systems faces two main challenges: the lack of basic rules of thumb and design bias introduced by human designers. The space of possible designs cannot be easily grasped by human designers especially for new tasks or tasks that are not fully understood by designers. Therefore, evolutionary computation is employed to design modular robots autonomously. Evolutionary algorithms can efficiently handle problems with discrete search spaces and solutions of variable sizes as these algorithms offer feasible robustness to local minima in the search space; and they can be parallelized easily to reducing system runtime. Moreover, they do not have to make assumptions about the solution form. This dissertation proposes a novel autonomous system for task-based modular robotic design based on evolutionary algorithms to search for the optimal design. The introduced system offers a flexible synthesis algorithm that can accommodate to different task-based design needs and can be applied to different modular shapes to produce homogenous modular robots. The proposed system uses a new representation for modular robotic assembly configuration based on graph theory and Assembly Incidence Matrix (AIM), in order to enable efficient and extendible task-based design of modular robots that can take input modules of different geometries and Degrees Of Freedom (DOFs). Robotic simulation is a powerful tool for saving time and money when designing robots as it provides an accurate method of assessing robotic adequacy to accomplish a specific task. Furthermore, it is difficult to predict robotic performance without simulation. Thus, simulation is used in this research to evaluate the robotic designs by measuring the fitness of the evolved robots, while incorporating the environmental features and robotic hardware constraints. Results are illustrated for a number of benchmark problems. The results presented a significant advance in robotic design automation state of the art

3D reconfiguration using graph grammars for modular robotics

The objective of this thesis is to develop a method for the reconfiguration of three-dimensional modular robots. A modular robot is composed of simple individual building blocks or modules. Each of these modules needs to be controlled and actuated individually in order to make the robot perform useful tasks. The presented method allows us to reconfigure arbitrary initial configurations of modules into any pre-specified target configuration by using graph grammar rules that rely on local information only. Local in a sense that each module needs just information from neighboring modules in order to decide its next reconfiguration step. The advantage of this approach is that the modules do not need global knowledge about the whole configuration. We propose a two stage reconfiguration process composed of a centralized planning stage and a decentralized, rule-based reconfiguration stage. In the first stage, paths are planned for each module and then rewritten into a ruleset, also called a graph grammar. Global knowledge about the configuration is available to the planner. In stage two, these rules are applied in a decentralized fashion by each node individually and with local knowledge only. Each module can check the ruleset for applicable rules in parallel. This approach has been implemented in Matlab and currently, we are able to generate rulesets for arbitrary homogeneous input configurations.MSCommittee Chair: Magnus Egerstedt; Committee Member: Jeff Shamma; Committee Member: Patricio Antonio Vel

TOWARDS A NOVEL RESILIENT ROBOTIC SYSTEM

Resilient robotic systems are a kind of robotic system that is able to recover their original function after partial damage of the system. This is achieved by making changes on the partially damaged robot. In this dissertation study, a general robot, which makes sense by including active joints, passive joints, passive links, and passive adjustable links, was proposed in order to explore its resilience. Note that such a robot is also called an under-actuated robot. This dissertation presents the following studies. First, a novel architecture of robots was proposed, which is characterized as under-actuated robot. The architecture enables three types of recovery strategy, namely (1) change of the robot behavior, (2) change of the robot state, and (3) change of the robot configuration. Second, a novel docking system was developed, which allows for the realization of real-time assembly and disassembly and passive joint and adjustable passive link, and this thus enables the realization of the proposed architecture. Third, an example prototype system was built to experiment the effectiveness of the proposed architecture and to demonstrate the resilient behavior of the robot. Fourth, a novel method for robot configuration synthesis was developed, which is based on the genetic algorithm (GA), to determine the goal configuration of a partially damaged robot, at which the robot can still perform its original function. The novelty of the method lies in the integration of both discrete variables such as the number of modules, type of modules, and assembly patterns between modules and the continuous variables such as the length of modules and initial location of the robot. Fifth, a GA-based method for robot reconfiguration planning and scheduling was developed to actually change the robot from its initial configuration to the goal configuration with a minimum effort (time and energy). Two conclusions can be drawn from the above studies. First, the under-actuated robotic architecture can build a cost effective robot that can achieve the highest degree of resilience. Second, the design of the under-actuated resilient robot with the proposed docking system not only reduces the cost but also overcomes the two common actuator failures: (i) an active joint is unlocked (thus becoming a passive joint) and (ii) an active joint is locked (thus becoming an adjustable link). There are several contributions made by this dissertation to the field of robotics. The first is the finding that an under-actuated robot can be made more resilient. In the field of robotics, the concept of the under-actuated robot is available, but it has not been considered for reconfiguration (in literature, the reconfiguration is mostly about fully actuated robots). The second is the elaboration on the concept of reconfiguration planning, scheduling, and manipulation/control. In the literature of robotics, only the concept of reconfiguration planning is precisely given but not for reconfiguration scheduling. The third is the development of the model along with its algorithm for synthesis of the goal reconfiguration, reconfiguration planning, and scheduling. The application of the proposed under-actuated resilient robot lies in the operations in unknown or dangerous environments, for example, in rescue missions and space explorations. In these applications, replacement or repair of a damaged robot is impossible or cost-prohibited

Evolutionary Modular Robotics: Survey and Analysis

This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.https://doi.org/10.1007/s10846-018-0902-

Locomotion through morphology, evolution and learning for legged and limbless robots

Mención Internacional en el título de doctorRobot locomotion is concerned with providing autonomous locomotion capabilities to mobile robots. Most current day robots feature some form of locomotion for navigating in their environment. Modalities of robot locomotion includes: (i) aerial locomotion, (ii) terrestrial locomotion, and (iii) aquatic locomotion (on or under water). Three main forms of terrestrial locomotion are, legged locomotion, limbless locomotion and wheel-based locomotion. A Modular Robot (MR), on the other hand, is a robotic system composed of several independent unit modules, where, each module is a robot by itself. The objective in this thesis is to develop legged locomotion in a humanoid robot, as well as, limbless locomotion in modular robotic configurations. Taking inspiration from biology, robot locomotion from the perspective of robot’s morphology, through evolution, and through learning are investigated in this thesis. Locomotion is one of the key distinguishing characteristics of a zoological organism. Almost all animal species, and even some plant species, produce some form of locomotion. In the past few years, robots have been “moving out” of the factory floor and research labs, and are becoming increasingly common in everyday life. So, providing stable and agile locomotion capabilities for robots to navigate a wide range of environments becomes pivotal. Developing locomotion in robots through biologically inspired methods, also facilitates furthering our understanding on how biological processes may function. Connected modules in a configuration, exert force on each other as a result of interaction between each other and their environment. This phenomenon is studied and quantified, and then used as implicit communication between robot modules for producing locomotion coordination in MRs. Through this, a strong link between robot morphology and the gait that emerge in it is established. A variety of locomotion controller, some periodic-function based and some morphology based, are developed for MR locomotion and bipedal gait generation. A hybrid Evolutionary Algorithm (EA) is implemented for evolving gaits, both in simulation as well as in the real-world on a physical modular robotic configuration. Limbless gaits in MRs are also learnt by learning optimal control policies, through Reinforcement Learning (RL).En robótica, la locomoción trata de proporcionar capacidades de locomoción autónoma a robots móviles. La mayoría de los robots actuales tiene alguna forma de locomoción para navegar en su entorno. Los modos de locomoción robótica se pueden repartir entre: (i) locomoción aérea, (ii) locomoción terrestre, y (iii) locomoción acuática (sobre o bajo el agua). Las tres formas básicas de locomoción terrestre son la locomoción mediante piernas, la locomoción sin miembros, y la locomoción basada en ruedas. Un Robot Modular, por otra parte, es un sistema robótico compuesto por varios módulos independientes, donde cada módulo es un robot en sí mismo. El objetivo de esta tesis es el desarrollo de la locomoción mediante piernas para un robot humanoide, así como el de la locomoción sin miembros para varias configuraciones de robots modulares. Inspirándose en la biología, también se investiga en esta tesis el desarrollo de la locomoción del robot según su morfología, gracias a técnicas de evolución y de aprendizaje. La locomoción es una de las características distintivas de un organismo zoológico. Casi todas las especies animales, e incluso algunas especies de plantas, poseen algún tipo de locomoción. En los últimos años, los robots han “migrado” desde las fábricas y los laboratorios de investigación, y se están integrando cada vez más en nuestra vida diaria. Por estas razones, es crucial proporcionar capacidades de locomoción estables y ágiles a los robots para que puedan navegar por todo tipo de entornos. El uso de métodos de inspiración biológica para alcanzar esta meta también nos ayuda a entender mejor cómo pueden funcionar los procesos biológicos equivalentes. En una configuración de módulos conectados, puesto que cada uno interacciona con su entorno, los módulos ejercen fuerza los unos sobre los otros. Este fenómeno se ha estudiado y cuantificado, y luego se ha usado como comunicación implícita entre los módulos para producir la coordinación en la locomoción de este robot. De esta manera, se establece un fuerte vínculo entre la morfología de un robot y el modo de andar que este desarrolla. Se han desarrollado varios controladores de locomoción para robots modulares y robots bípedos, algunos basados en funciones periódicas, otros en la morfología del robot. Un algoritmo evolutivo híbrido se ha implementado para la evolución de locomociones, tanto en simulación como en el mundo real en una configuración física de robot modular. También se pueden generar locomociones sin miembros para robots modulares, determinando las políticas de control óptimo gracias a técnicas de aprendizaje por refuerzo. Se presenta en primer lugar en esta tesis el estado del arte de la robótica modular, enfocándose en la locomoción de robots modulares, los controladores, la locomoción bípeda y la computación morfológica. A continuación se describen cinco configuraciones diferentes de robot modular que se utilizan en esta tesis, seguido de cuatro controladores de locomoción. Estos controladores son el controlador heterogéneo, el controlador basado en funciones periódicas, el controlador homogéneo y el controlador basado en la morfología del robot. Se desarrolla como parte de este trabajo un controlador de locomoción lineal, periódico, basado en features, para la locomoción bípeda de robots humanoides. Los parámetros de control se ajustan primero a mano para reproducir un modelo cart-table, y el controlador se evalúa en un robot humanoide simulado. A continuación, gracias a un algoritmo evolutivo, la optimización de los parámetros de control permite desarrollar una locomoción sin modelo predeterminado. Se desarrolla como parte de esta tesis un enfoque sobre algoritmos de Embodied Evolución, en otras palabras el uso de robots modulares físicos en la fase de evolución. La implementación material, la configuración experimental, y el Algoritmo Evolutivo implementado para Embodied Evolución, se explican detalladamente. El trabajo también incluye una visión general de las técnicas de aprendizaje por refuerzo y de los Procesos de Decisión de Markov. A continuación se presenta un algoritmo popular de aprendizaje por refuerzo, llamado Q-Learning, y su adaptación para aprender locomociones de robots modulares. Se proporcionan una implementación del algoritmo de aprendizaje y la evaluación experimental de la locomoción generada.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Antonio Barrientos Cruz.- Secretario: Luis Santiago Garrido Bullón.- Vocal: Giuseppe Carbon