A MODULAR DESIGN OF A WALL-CLIMBING ROBOT AND ITS MECHATRONICS CONTROLLER

<p>ENGLISH ABSTRACT: The modular design of a wall-climbing robot, implementing two articulated legs per module (biped robotic modules), is presented in this paper. Modular design improves a wall-climbing robot’s manoeuvrability and flexibility during surface changes or while walking on uneven surfaces. The design of the articulated legs uses four motors to control the posture of the vacuum cups, achieving the best possible contact with the surface. Each leg can contain more than five sensors for effective feedback control, and additional sensors such as gyros, CCD sensors, etc, can be fitted on a module, depending on the robot’s application. As the number of modules used in the design of the robot is increased, the number of actuators and sensors increases exponentially. A distributed mechatronics controller of such systems is presented.</p><p>AFRIKAANSE OPSOMMING: Modulêre ontwerp van 'n muurklim-robot met twee geskarnierde bene per module (twee-benige robotmodules) word in hierdie artikel weergegee. Modulêre ontwerp verbeter die muurklim-robot se beweeglikheid en aanpasbaarheid tydens veranderings in die loopvlak of terwyl dit loop op ongelyke oppervlaktes. Ontwerp van geskarnierde bene implementeer vier motors wat die oriëntasie van vakuumsuigdoppe beheer om die bes moontlike kontak met die loopvlak te handhaaf. Elke been kan meer as vyf sensors hê vir doeltreffende terugvoerbeheer, en bykomende sensors soos giroskope, CCD sensors, ens. kan by 'n module gevoeg word soos die toepassing van die robot dit mag vereis. Soos die aantal modules wat in die ontwerp van die robot gebruik word, toeneem, neem die aantal aktiveerders en sensors eksponensiëel toe. 'n Verdeelde megatroniese beheerder van sulke stelsels word aangebied.</p&gt

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

Software integration in mobile robotics, a science to scale up machine intelligence

The present work tackles integration in mobile robotics. Integration is often considered to be a mere technique, unworthy of scientific investigation. On the contrary, we show that integrating capabilities in a mobile robot entails new questions that the parts alone do not feature. These questions reflect the structure of the application and the physics of the world. We also show that a successful integration process transforms the parts themselves and allows to scale up mobile-robot intelligence in real-world applications. In Chapter 2 we present the hardware. In Chapter 3, we show that building a low-level control architecture considering the mechanic and electronic reality of the robot improves the performances and allows to integrate a large number of sensors and actuators. In Chapter 4, we show that globally optimising mechatronic parameters considering the robot as a whole allows to implement slam using an inexpensive sensor with a low processor load. In Chapter 5, we show that based on the output from the slam algorithm, we can combine infrared proximity sensors and vision to detect objects and to build a semantic map of the environment. We show how to find free paths for the robot and how to create a dual geometric-symbolic representation of the world. In Chapter 6, we show that the nature of scenarios influences the implementation of a task-planning algorithm and changes its execution properties. All these chapters contribute results that together prove that integration is a science. In Chapter 7, we show that combining these results improves the state of the art in a difficult application : autonomous construction in unknown environments with scarce resources. This application is interesting because it is challenging at multiple levels : For low-level control, manipulating objects in the real world to build structures is difficult. At the level of perceptions, the fusion of multiple heterogeneous inexpensive sensors is not trivial, because these sensors are noisy and the noise is non-Gaussian. At the level of cognition, reasoning about elements from an unknown world in real time on a miniature robot is demanding. Building this application upon our other results proves that integration allows to scale up machine intelligence, because this application shows intelligence that is beyond the state of the art, still only combining basic components that are individually slightly behind the state of the art