Inertial measurement unit modelling

Inerciální měřící jednotka (IMU) patří mezi základní senzorické vybavení současných mobilních robotů, kde se používá především pro odhadování ohamžité orientace robotu v prostoru. V této práci se komplexně zabývám simulací IMU v robotickém simulátoru Gazebo za účelem co nejvěrnějšího modelování odhadovaného úhlu natočení robotu, který je jedním z přímých výstupů IMU senzoru Bosch BNO055. Pro podporu vyhodnocení kvality IMU modelu vzhledem k reálným datům z BNO055 jsem navrhl a implementoval simulační prostřední v rámci Robotického Operačního Systému (ROS), které aproximuje zadanou trajektorii, uloží data ze simulovaného IMU a vygeneruje podklady pro vyhodnocení kvality IMU modelu vzhledem k reálným datům z BNO055. Na základě nejlepších dostupných IMU pluginů v Gazebu jsem implementoval dva URDF/SDF modely IMU sensoru, jejichž funkčnost byla následně ověřena řadou experimentů v simulátoru. Provedené simulace potvrdily funkčnost modelů a zároveň poukázaly na limity realističnosti současných pluginů v Gazebu a nastínily možnosti dalšího vývoje pro zvýšení věrnosti simulací IMU.The inertial measurement unit (IMU) sensors are massively used in mobile service robots to provide orientation estimation. This thesis is concerned with modeling and simulation of IMU sensor in the robotics simulator Gazebo. The main goal of this thesis is to simulate the heading angle output of a real IMU sensor Bosch BNO055 with high fidelity. To enable the IMU model evaluation I designed and implmented a custom IMU simulation framework as a ROS package. This framework approximates the given trajectory with the help of a Gazebo simulation of a robot model with attached IMU sensor model, captures the simulated IMU output and generates data for the comparison concerning provided dataset measured by real BNO055. I used the best currently available IMU plugins to implement two different URDF/SDF IMU models. The simulations demonstrated the functionality of implemented IMU models, but also revealed the fidelity limitations of current IMU plugins in Gazebo, and led to a discussion about possible future improvements

Portable dVRK: an augmented V-REP simulator of the da Vinci Research Kit

The da Vinci Research Kit (dVRK) is a first generation da Vinci robot repurposed as a research platform and coupled with software and controllers developed by research users. An already quite wide community is currently sharing the dVRK (32 systems in 28 sites worldwide). The access to the robotic system for training surgeons and for developing new surgical procedures, tools and new control modalities is still difficult due to the limited availability and high maintenance costs. The development of simulation tools provides a low cost, easy and safe alternative to the use of the real platform for preliminary research and training activities. The Portable dVRK, which is described in this work, is based on a V-REP simulator of the dVRK patient side and endoscopic camera manipulators which are controlled through two haptic interfaces and a 3D viewer, respectively. The V-REP simulator is augmented with a physics engine allowing to render the interaction of new developed tools with soft objects. Full integration in the ROS control architecture makes the simulator flexible and easy to be interfaced with other possible devices. Several scenes have been implemented to illustrate performance and potentials of the developed simulator

Support polygon in the hybrid legged-wheeled CENTAURO robot: modelling and control

Search for the robot capable to perform well in the real-world has sparked an interest in the hybrid locomotion systems. The hybrid legged-wheeled robots combine the advantages of the standard legged and wheeled platforms by switching between the quick and efficient wheeled motion on the flat grounds and the more versatile legged mobility on the unstructured terrains. With the locomotion flexibility offered by the hybrid mobility and appropriate control tools, these systems have high potential to excel in practical applications adapting effectively to real-world during locomanipuation operations. In contrary to their standard well-studied counterparts, kinematics of this newer type of robotic platforms has not been fully understood yet. This gap may lead to unexpected results when the standard locomotion methods are applied to hybrid legged-wheeled robots. To better understand mobility of the hybrid legged-wheeled robots, the model that describes the support polygon of a general hybrid legged-wheeled robot as a function of the wheel angular velocities without assumptions on the robot kinematics or wheel camber angle is proposed and analysed in this thesis. Based on the analysis of the developed support polygon model, a robust omnidirectional driving scheme has been designed. A continuous wheel motion is resolved through the Inverse Kinematics (IK) scheme, which generates robot motion compliant with the Non-Sliding Pure-Rolling (NSPR) condition. A higher-level scheme resolving a steering motion to comply with the non-holonomic constraint and to tackle the structural singularity is proposed. To improve the robot performance in presence to the unpredicted circumstances, the IK scheme has been enhanced with the introduction of a new reactive support polygon adaptation task. To this end, a novel quadratic programming task has been designed to push the system Support Polygon Vertices (SPVs) away from the robot Centre of Mass (CoM), while respecting the leg workspace limits. The proposed task has been expressed through the developed SPV model to account for the hardware limits. The omnidirectional driving and reactive control schemes have been verified in the simulation and hardware experiments. To that end, the simulator for the CENTAURO robot that models the actuation dynamics and the software framework for the locomotion research have been developed

Simulating Ionising Radiation in Gazebo for Robotic Nuclear Inspection Challenges

From MDPI via Jisc Publications RouterHistory: accepted 2021-07-02, pub-electronic 2021-07-07Publication status: PublishedFunder: UK Research and Innovation; Grant(s): EP/P018505/1, EP/R026084/1Funder: Royal Academy of Engineering; Grant(s): CiET1819\13The utilisation of robots in hazardous nuclear environments has potential to reduce risk to humans. However, historical use has been largely limited to specific missions rather than broader industry-wide adoption. Testing and verification of robotics in realistic scenarios is key to gaining stakeholder confidence but hindered by limited access to facilities that contain radioactive materials. Simulations offer an alternative to testing with actual radioactive sources, provided they can readily describe the behaviour of robotic systems and ionising radiation within the same environment. This work presents a quick and easy way to generate simulated but realistic deployment scenarios and environments which include ionising radiation, developed to work within the popular robot operating system compatible Gazebo physics simulator. Generated environments can be evolved over time, randomly or user-defined, to simulate the effects of degradation, corrosion or to alter features of certain objects. Interaction of gamma radiation sources within the environment, as well as the response of simulated detectors attached to mobile robots, is verified against the MCNP6 Monte Carlo radiation transport code. The benefits these tools provide are highlighted by inclusion of three real-world nuclear sector environments, providing the robotics community with opportunities to assess the capabilities of robotic systems and autonomous functionalities

Goal Based Human Swarm Interaction for Collaborative Transport

Human-swarm interaction is an important milestone for the introduction of swarm-intelligence based solutions into real application scenarios. One of the main hurdles towards this goal is the creation of suitable interfaces for humans to convey the correct intent to multiple robots. As the size of the swarm increases, the complexity of dealing with explicit commands for individual robots becomes intractable. This brings a great challenge for the developer or the operator to drive robots to finish even the most basic tasks. In our work, we consider a different approach that humans specify only the desired goal rather than issuing individual commands necessary to obtain this task. We explore this approach in a collaborative transport scenario, where the user chooses the target position of an object, and a group of robots moves it by adapting themselves to the environment. The main outcome of this thesis is the design of integration of a collaborative transport behavior of swarm robots and an augmented reality human interface. We implemented an augmented reality (AR) application in which a virtual object is displayed overlapped on a detected target object. Users can manipulate the virtual object to generate the goal configuration for the object. The designed centralized controller translate the goal position to the robots and synchronize the state transitions. The whole system is tested on Khepera IV robots through the integration of Vicon system and ARGoS simulator

Simulation Tools for the Study of the Interaction between Communication and Action in Cognitive Robots

In this thesis I report the development of FARSA (Framework for Autonomous Robotics Simulation and Analysis), a simulation tool for the study of the interaction between language and action in cognitive robots and more in general for experiments in embodied cognitive science. Before presenting the tools, I will describe a series of experiments that involve simulated humanoid robots that acquire their behavioural and language skills autonomously through a trial-and-error adaptive process in which random variations of the free parameters of the robots’ controller are retained or discarded on the basis of their effect on the overall behaviour exhibited by the robot in interaction with the environment. More specifically the first series of experiments shows how the availability of linguistic stimuli provided by a caretaker, that indicate the elementary actions that need to be carried out in order to accomplish a certain complex action, facilitates the acquisition of the required behavioural capacity. The second series of experiments shows how a robot trained to comprehend a set of command phrases by executing the corresponding appropriate behaviour can generalize its knowledge by comprehending new, never experienced sentences, and by producing new appropriate actions. Together with their scientific relevance, these experiments provide a series of requirements that have been taken into account during the development of FARSA. The objective of this project is that to reduce the complexity barrier that currently discourages part of the researchers interested in the study of behaviour and cognition from initiating experimental activity in this area. FARSA is the only available tools that provide an integrated framework for carrying on experiments of this type, i.e. it is the only tool that provides ready to use integrated components that enable to define the characteristics of the robots and of the environment, the characteristics of the robots’ controller, and the characteristics of the adaptive process. Overall this enables users to quickly setup experiments, including complex experiments, and to quickly start collecting results

Simple individual behavioural rules for improving the collective behaviours of robot swarms

Swarm robotics is an ongoing area of research that is expected to revolutionise various real-world domains such as agriculture and space exploration. Swarm robotics systems are composed of a large number of simple and autonomous robots. Each robot locally interacts with other robots and with the environment following a set of behavioural rules. These individual interactions enable the swarm to exhibit interesting collective behaviours and to accomplish specific tasks. The main challenge in designing robot swarms is to determine the behavioural rules that each robot should follow so that the swarm as a whole can perform the desired task. The performance of robot swarms in a given task depends on the designer's choice of appropriate individual behavioural rules. In this thesis, we investigate simple individual behavioural rules for improving the performance of robot swarms in two major tasks. Using simple behavioural rules makes the designed solutions possibly usable with simpler platforms such as micro- and nanorobots. The first task we address is known as the best-of-n decision problem where the swarm is required to select the best option among n available alternatives. Solving the best-of-n decision problem is considered to be a fundamental cognitive skill for robot swarms as it influences the swarm's success in other tasks. In this thesis, we introduce individual behavioural rules to improve the performance of robot swarms in the best-of-n problem. Through these rules, robots vary their interaction strength over time in a decentralised fashion to balance the acquisition and the dissemination of information. The proposed behavioural rules allow swarms of simple noisy robots with constrained communication to limit the effect of individual errors and make highly accurate collective decisions in a predictable time. In some scenarios where the best option changes over time, the swarm is required to switch its decision accordingly. In this thesis, we introduce individual behavioural rules through which the robots process new information and discard outdated beliefs. These behavioural rules enable robot swarms to adapt their decisions to various environmental changes, including the appearance of better choices or the disappearance of the current swarm's choice. Our analysis shows that relying on local communication is more favourable for achieving adaptation. This result highlights the benefit of the local sensing and communication characterising biological and artificial swarms. The second task we address in this thesis is the collective resource collection task. In this task, the robots are asked to retrieve objects that are clustered at unknown locations in the environment. We address this task because of its numerous potential real-world applications. In many of these applications, the objects to collect are assigned different importance or value. In this thesis, we introduce a bio-inspired individual behaviour that allows robot swarms to perform quality-based resource collection. Similarly to foraging ants, in our proposed behaviour, the robots coordinate their collection efforts by laying and sensing virtual pheromone trails. The use of pheromone trails offers an advantageous implementation of the memory and communication capabilities necessary for the efficient collection of clustered objects. The proposed behaviour allows robot swarms to satisfy various collection objectives and achieve an optimal resource collection behaviour in the case of relatively small swarms. In this thesis, we analyse swarm robotics systems using both minimalistic tools such as stochastic and multi-agent simulations, and more advanced tools such as physics-based simulations and real robot experiments. Using these tools, we demonstrate the effectiveness of the proposed individual behavioural rules in improving the performance of robot swarms in the addressed tasks. The results we present in this thesis are of potential interest to both engineers designing robot swarms, and biologists investigating the behavioural rules followed by individuals in living collective organisms

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Search and restore: a study of cooperative multi-robot systems

Swarm intelligence is the study of natural biological systems with the ability to transform simple local interactions into complex global behaviours. Swarm robotics takes these principles and applies them to multi-robot systems with the aim of achieving the same level of complex behaviour which can result in more robust, scalable and flexible robotic solutions than singular robot systems. This research concerns how cooperative multi-robot systems can be utilised to solve real world challenges and outperform existing techniques. The majority of this research is focused around an emergency ship hull repair scenario where a ship has taken damage and sea water is flowing into the hull, decreasing the stability of the ship. A bespoke team of simulated robots using novel algorithms enable the robots to perform a coordinated ship hull inspection, allowing the robots to locate the damage faster than a similarly sized uncoordinated team of robots. Following this investigation, a method is presented by which the same team of robots can use self-assembly to form a structure, using their own bodies as material, to cover and repair the hole in the ship hull, halting the ingress of sea water. The results from a collaborative nature-inspired scenario are also presented in which a swarm of simple robots are tasked with foraging within an initially unexplored bounded arena. Many of the behaviours implemented in swarm robotics are inspired by biological swarms including their goals such as optimal distribution within environments. In this scenario, there are multiple items of varying quality which can be collected from different sources in the area to be returned to a central depot. The aim of this study is to imbue the robot swarm with a behaviour that will allow them to achieve the most optimal foraging strategy similar to those observed in more complex biological systems such as ants. The author’s main contribution to this study is the implementation of an obstacle avoidance behaviour which allows the swarm of robots to behave more similarly to systems of higher complexity