The Propulsion of Reconfigurable Modular Robots in Fluidic Environments

Reconfigurable modular robots promise to transform the way robotic systems are designed and operated. Fluidic or microgravity environments, which can be difficult or dangerous for humans to work in, are ideal domains for the use of modular systems. This thesis proposes that combining effective propulsion, large reconfiguration space and high scalability will increase the utility of modular robots. A novel concept for the propulsion of reconfigurable modular robots is developed. Termed Modular Fluidic Propulsion (MFP), this concept describes a system that propels by routing fluid though itself. This allows MFP robots to self-propel quickly and effectively in any configuration, while featuring a cubic lattice structure. A decentralized occlusion-based motion controller for the system is developed. The simplicity of the controller, which requires neither run-time memory nor computation via logic units, combined with the simple binary sensors and actuators of the robot, gives the system a high level of scalabilty. It is proven formally that 2-D MFP robots are able to complete a directed locomotion task under certain assumptions. Simulations in 3-D show that robots composed of 125 modules in a variety of configurations can complete the task. A hardware prototype that floats on the surface of water is developed. Experiments show that robots composed of four modules can complete the task in any configuration. This thesis also investigates the evo-bots, a self-reconfigurable modular system that floats in 2-D on an air table. The evo-bot system uses a stop-start propulsion mechanism to choose between moving randomly or not moving at all. This is demonstrated experimentally for the first time. In addition, the ability of the modules to detect, harvest and share energy, as well as self-assemble into simple structures, is demonstrated

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

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

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Heterogeneous Robot Swarm – Hardware Design and Implementation

Swarm robotics is one the most fascinating, new research areas in the field of robotics, and one of it's grand challenge is the design of swarm robots that are both heterogeneous and self-sufficient. This can be crucial for robots exposed to environments that are unstructured or not easily accessible for a human operator, such as a collapsed building, the deep sea, or the surface of another planet. In Swarm robotics; self-assembly, self-reconfigurability and self-replication are among the most important characteristics as they can add extra capabilities and functionality to the robots besides the robustness, flexibility and scalability. Developing a swarm robot system with heterogeneity and larger behavioral repertoire is addressed in this work. This project is a comprehensive study of the hardware architecture of the homogeneous robot swarm and several problems related to the important aspects of robot's hardware, such as: sensory units, communication among the modules, and hardware components. Most of the hardware platforms used in the swarm robot system are homogeneous and use centralized control architecture for task completion. The hardware architecture is designed and implemented for UB heterogeneous robot swarm with both decentralized and centralized control, depending on the task requirement. Each robot in the UB heterogeneous swarm is equipped with different sensors, actuators, microcontroller and communication modules, which makes them distinct from each other from a hardware point of view. The methodology provides detailed guidelines in designing and implementing the hardware architecture of the heterogeneous UB robot swarm with plug and play approach. We divided the design module into three main categories - sensory modules, locomotion and manipulation, communication and control. We conjecture that the hardware architecture of heterogeneous swarm robots implemented in this work is the most sophisticated and modular design to date

Deployment of Heterogeneous Swarm Robotic Agents Using a Task-Oriented Utility-Based Algorithm

In a swarm robotic system, the desired collective behavior emerges from local decisions made by robots, themselves, according to their environment. Swarm robotics is an emerging area that has attracted many researchers over the last few years. It has been proven that a single robot with multiple capabilities cannot complete an intended job within the same time frame as that of multiple robotic agents. A swarm of robots, each one with its own capabilities, are more flexible, robust, and cost-effective than an individual robot. As a result of a comprehensive investigation of the current state of swarm robotic research, this dissertation demonstrates how current swarm deployment systems lack the ability to coordinate heterogeneous robotic agents. Moreover, this dissertation's objective shall define the starting point of potential algorithms that lead to the development of a new software environment interface. This interface will assign a set of collaborative tasks to the swarm system without being concerned about the underlying hardware of the heterogeneous robotic agents. The ultimate goal of this research is to develop a task-oriented software application that facilitates the rapid deployment of multiple robotic agents. The task solutions are created at run-time, and executed by the agents in a centralized or decentralized fashion. Tasks are fractioned into smaller sub-tasks which are, then, assigned to the optimal number of robots using a novel Robot Utility Based Task Assignment (RUTA) algorithm. The system deploys these robots using it's application program interfaces (API's) and uploads programs that are integrated with a small routine code. The embedded routine allows robots to configure solutions when the decentralized approach is adopted. In addition, the proposed application also offers customization of robotic platforms by simply defining the available sensing and actuation devices. Another objective of the system is to improve code and component reusability to reduce efforts in deploying tasks to swarm robotic agents. Usage of the proposed framework prevents the need to redesign or rewrite programs should any changes take place in the robot's platform

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

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