High-Level Control Of Modular Robots

Reconfigurable modular robots can exhibit different specializations by rearranging the same set of parts comprising them. Actuating modular robots can be complicated because of the many degrees of freedom that scale exponentially with the size of the robot. Effectively controlling these robots directly relates to how well they can be used to complete meaningful tasks. This paper discusses an approach for creating provably correct controllers for modular robots from high-level tasks defined with structured English sentences. While this has been demonstrated with simple mobile robots, the problem was enriched by considering the uniqueness of reconfigurable modular robots. These requirements are expressed through traits in the high-level task specification that store information about the geometry and motion types of a robot. Given a high-level problem definition for a modular robot, the approach in this paper deals with generating all lower levels of control needed to solve it. Information about different robot characteristics is stored in a library, and two tools for populating this library have been developed. The first approach is a physics-based simulator and gait creator for manual generation of motion gaits. The second is a genetic algorithm framework that uses traits to evaluate performance under various metrics. Demonstration is done through simulation and with the CKBot hardware platform

Duplication of modules facilitates the evolution of functional specialization

The evolution of simulated robots with three different architectures is studied. We compared a non-modular feed forward network, a hardwired modular and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state

Modular Self-Reconfigurable Robot Systems

The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing fiel

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.The research leading to these results has received funding from RoboCity2030-II-CM (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds os the EUPublicad

Towards Python-based Domain-specific Languages for Self-reconfigurable Modular Robotics Research

This paper explores the role of operating system and high-level languages in the development of software and domain-specific languages (DSLs) for self-reconfigurable robotics. We review some of the current trends in self-reconfigurable robotics and describe the development of a software system for ATRON II which utilizes Linux and Python to significantly improve software abstraction and portability while providing some basic features which could prove useful when using Python, either stand-alone or via a DSL, on a self-reconfigurable robot system. These features include transparent socket communication, module identification, easy software transfer and reliable module-to-module communication. The end result is a software platform for modular robots that where appropriate builds on existing work in operating systems, virtual machines, middleware and high-level languages.Comment: Presented at DSLRob 2011 (arXiv:1212.3308

A Proposal for a Multi-Drive Heterogeneous Modular Pipe- Inspection Micro-Robot

This paper presents the architecture used to develop a micro-robot for narrow pipes inspection. Both the electromechanical design and the control scheme will be described. In pipe environments it is very useful to have a method to retrieve information of the state of the inside part of the pipes in order to detect damages, breaks and holes. Due to the di_erent types of pipes that exists, a modular approach with di_erent types of modules has been chosen in order to be able to adapt to the shape of the pipe and to chose the most appropriate gait. The micro-robot has been designed for narrow pipes, a _eld in which there are not many prototypes. The robot incorporates a camera module for visual inspection and several drive modules for locomotion and turn (helicoidal, inchworm, two degrees of freedom rotation). The control scheme is based on semi-distributed behavior control and is also described. A simulation environment is also presented for prototypes testing

AltURI: a thin middleware for simulated robot vision applications

Fast software performance is often the focus when developing real-time vision-based control applications for robot simulators. In this paper we have developed a thin, high performance middleware for USARSim and other simulators designed for real-time vision-based control applications. It includes a fast image server providing images in OpenCV, Matlab or web formats and a simple command/sensor processor. The interface has been tested in USARSim with an Unmanned Aerial Vehicle using two control applications; landing using a reinforcement learning algorithm and altitude control using elementary motion detection. The middleware has been found to be fast enough to control the flying robot as well as very easy to set up and use