Razvoj bioinspiriranog zmijolikog robota

Razvoj i primjena radnih alata podrazumijevaju svrhovito proširenje i uvećanje individualnih ljudskih sposobnosti. Stoga se, prirodno, u alatima istodobno nalaze odrazi ljudima karakterističnih sposobnosti. Taj se čovječji odraz u alatima posebno vidi u evoluciji industrijskih manipulatora: od jednostavnih linearnih osiju, preko prvotne uvriježenosti pojedinačnih robotskih ruku, do kasnijeg razvoja dvorukih robota. No, humanocentričnost izvedbe alata ili stroja neće uvijek biti najprikladnije i najefikasnije rješenje za neke radne zadatke i okružja, već se inspiracija za izvedbu alata treba tražiti kod drugih živih bića te u njihovim značajkama i specifičnim sposobnostima

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

Algorithms for Modular Self-reconfigurable Robots: Decision Making, Planning, and Learning

Modular self-reconfigurable robots (MSRs) are composed of multiple robotic modules which can change their connections with each other to take different shapes, commonly known as configurations. Forming different configurations helps the MSR to accomplish different types of tasks in different environments. In this dissertation, we study three different problems in MSRs: partitioning of modules, configuration formation planning and locomotion learning, and we propose algorithmic solutions to solve these problems. Partitioning of modules is a decision-making problem for MSRs where each module decides which partition or team of modules it should be in. To find the best set of partitions is a NP-complete problem. We propose game theory based both centralized and distributed solutions to solve this problem. Once the modules know which set of modules they should team-up with, they self-aggregate to form a specific shaped configuration, known as the configuration formation planning problem. Modules can be either singletons or connected in smaller configurations from which they need to form the target configuration. The configuration formation problem is difficult as multiple modules may select the same location in the target configuration to move to which might result in occlusion and consequently failure of the configuration formation process. On the other hand, if the modules are already in connected configurations in the beginning, then it would be beneficial to preserve those initial configurations for placing them into the target configuration as disconnections and re-connections are costly operations. We propose solutions based on an auction-like algorithm and (sub) graph-isomorphism technique to solve the configuration formation problem. Once the configuration is built, the MSR needs to move towards its goal location as a whole configuration for completing its task. If the configuration’s shape and size is not known a priori, then planning its locomotion is a difficult task as it needs to learn the locomotion pattern in dynamic time – the problem is known as adaptive locomotion learning. We have proposed reinforcement learning based fault-tolerant solutions for locomotion learning by MSRs

Parallel Platform-Based Robot for Operation in Active Water Pipes

This thesis presents a novel design for a pipe inspection robot. The main aim of the design has been to allow the robot to operate in a water pipe while it is still in service. Water pipes form a very crucial part of the infrastructure of the world we live in today. Despite their importance, water leakage is a major problem suffered by water companies worldwide, costing them billions of dollars every year. There are a wide variety of different techniques used for leak detection and localisation, but no one method is capable of accurately pinpointing the leak location and severity in all pipe conditions with minimal labour. A survey of existing pipe inspection robots showed that there have been many designs implemented that are capable of navigating the pipeline environment. However, none of these were capable of fully autonomous control in a live water pipe. It was concluded that an autonomous pipe inspection robot capable of working in active pipelines would be of great industrial benefit as it would be able to carry a wide range of sensors directly to the source of the leak with minimal, if any, human intervention. An inchworm robot prototype was constructed based on a Gough-Stewart parallel platform. The robot’s inverse kinematics equations were derived and a simulation model of the robot was constructed. These were verified using a motion capture suite, confirming that they are valid representations of the robot. The simulation was used to determine the robot’s movement limitations and minimum bend radius it could navigate. Several CFD simulations were carried out in order to estimate the maximum fluid force exerted on the robot. It was found that the robot’s design successfully minimised the fluid force such that off-the-shelf actuators had the capability to overcome it. The prototype was successfully tested in both a straight and bent pipe, demonstrating its ability to navigate a dry pipe environment. Overall, the robot prototype served as a successful proof of concept for a design of pipe inspection robot that would be capable of operating in active pipelines

Challenges in the Locomotion of Self-Reconfigurable Modular Robots

Self-Reconfigurable Modular Robots (SRMRs) are assemblies of autonomous robotic units, referred to as modules, joined together using active connection mechanisms. By changing the connectivity of these modules, SRMRs are able to deliberately change their own shape in order to adapt to new environmental circumstances. One of the main motivations for the development of SRMRs is that conventional robots are limited in their capabilities by their morphology. The promise of the field of self-reconfigurable modular robotics is to design robots that are robust, self-healing, versatile, multi-purpose, and inexpensive. Despite significant efforts by numerous research groups worldwide, the potential advantages of SRMRs have yet to be realized. A high number of degrees of freedom and connectors make SRMRs more versatile, but also more complex both in terms of mechanical design and control algorithms. Scalability issues affect these robots in terms of hardware, low-level control, and high-level planning. In this thesis we identify and target three major challenges: (i) Hardware design; (ii) Planning and control; and, (iii) Application challenges. To tackle the hardware challenges we redesigned and manufactured the Self-Reconfigurable Modular Robot Roombots to meet desired requirements and characteristics. We explored in detail and improved two major mechanical components of an SRMR: the actuation and the connection mechanisms. We also analyzed the use of compliant extensions to increase locomotion performance in terms of locomotion speed and power consumption. We contributed to the control challenge by developing new methods that allow an arbitrary SRMR structure to learn to locomote in an efficient way. We defined a novel bio-inspired locomotion-learning framework that allows the quick and reliable optimization of new gaits after a morphological change due to self-reconfiguration or human construction. In order to find new suitable application scenarios for SRMRs we envision the use of Roombots modules to create Self-Reconfigurable Robotic Furniture. As a first step towards this vision, we explored the use and control of Plug-n-Play Robotic Elements that can augment existing pieces of furniture and create new functionalities in a household to improve quality of life