Robot operating system (ROS)

Colour sorting robots automate industries, but translating image data to robot movement is expensive and complicated. Vision sensors require a lot of processing power, which can slow down and strain the robot. Real-time colour sorting hardware and software integration complicates things. This work uses robotic operating system (ROS) to solve vision-guided colour sorting problems in Cartesian space. Ubuntu 20.04, ROS Noetic, a Raspberry Pi, a camera, and six servos. In Jupyter Lab, unified robotic description format (URDF) is used to build a virtual kinematic model, and Levenberg-Marquardt (LM) optimisation guides object manipulation. OpenCV image processing uses colour conversion, Canny edges, and midpoint estimation to detect coloured objects efficiently. The average servo movement error is 0.46 degrees, and the robot manipulator's final destination positioning error is 1.65 mm. The average object edge detection error is 0.33 mm, and the red, green, blue (RGB) colour distance is 57.84. ROS-based robot manipulator achieves impressive Cartesian space colour sorting accuracy despite image processing challenges, enabling real-world deployment.

Multi-robot systems (MRS) are groups of robots that coordinate to complete a given task. In communication-based systems, the integrity of the information shared between robots becomes highly important as any security threat due to a malicious node in the system can cause a chain reaction to compromise the entire system. This paper proposes a hybridized blockchain model-based architecture (HBMA) built on robot operating system (ROS) which offers a semi-decentralized environment into which any communication-based algorithm can be plugged in. A security monitoring system is also provided with the architecture that identifies and shuts down malicious robots while also sending out alerts about the threat. This architecture is used to create secured, coupled approaches to localization of multi-robots and multi-robot path planning. This approach is validated on both physical robots and simulations run on ROS.

This report presents the design, implementation, and operation of an omnidirectional mobile robot equipped with Mecanum wheels, enabling precise motion in any direction. The robot's development included a MATLAB simulation to model its movement within a square area, which provided valuable insights into kinematic behavior and design optimization. Subsequently, an ultrasonic sensor-based obstacle avoidance system was integrated to achieve autonomous navigation. This report details the robot's mechanical design, control systems, obstacle avoidance logic, simulation results, and experimental outcomes. Recommendations for future improvements are also provided.

Mobil robotların kullanımı günümüzde oldukça artmıştır. Otonom çalışan bu robotların kontrol yazılımlarını iyi bir şekil-de geliştirilmesi önemlidir. Yapay zeka tabanlı geliştirilen bu yazılımların fiziksel robotların üzerinde denenmesi çoğunlukla yüksek maliyetler gerektirir. Mobil robotların farklı fiziksel çevreler için denenebilmesi oldukça zordur. Mobil robotlar için geliştirilen algoritmaların farklı fiziksel çevreler ve robotlar üzerinde denenebilmesi, sonuçların gözlemlenmesi ticari ve akademik çalışmalar için önemlidir. Bu sebeple mobil robot davranışlarının simülasyonunun yapılması için çeşitli yazılım platformlar üretilmiştir. Akademik veya ticari olarak geliştirilen robotların, denemelerinin ilk olarak bilgisayar ortamında yapıl-ması hem zaman hem de maddi olarak kazanç sağlayacaktır. Bu kapsamda çeşitli firma veya kişiler tarafından birçok mobil robot simülatörü kullanıma sunulmuştur. Bu çalışmada, mobil robot uygulamaları için kullanabilecek, akademik çalışmalarda en çok kullanılan 10 mobil robot yazılımı incelenmiştir. Yazılım-ların kullanımı, lisans seçenekleri, çalıştığı platformlar ve avan-tajlarının incelendiği bu çalışma mobil robot simülatörleri hak-kında bilgi vermektedir.

This study proposed a novel approach for mobile robots path planning and avoiding collisions by using Collision Probability (CP) along with the Soft Actor-Critic Lagrangian (SACL-L) framework. Our approach enables the mobile robot to dynamically deal with static and dynamic environments while ensuring safety and efficiency. The proposed SAC-L (CP) aims to minimize the total costs, which is the combination of both negative rewards and collision occurs. This dual focus strategy ensures trajectory planning inherently safer and providing a robust solution for complex dynamic obstacles environments. The framework’s efficiency is validated through extensive simulations on the Gazebo platform involving three increasingly difficult scenarios, demonstrating superior performance, adaptability and safety of our approach compared to traditional Deep Reinforcement Learning (DRL) methods. Our results showcase significant improvements in social and ego safety scores, contributing to the advancement of autonomous navigation in complex environments. This framework marks a step towards safer, more reliable mobile robot navigation and opens new avenues for future research in mobile robot path planning. A supplementary video further demonstrates the effectiveness of our framework.

Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.

The advent of increasingly flexible and adaptive production processes will require equally flexible robotic collaboration. The forthcoming robotic applications must be able to self-adapt to different applications and scenarios and to handle frequent interaction with humans and dynamic environments. Environment perception, object recognition, and trajectory replanning in a dynamic and changing environment, even with possible human interaction, are features that are not yet all simultaneously available in collaborative robots, and certainly not in industrial robots. This paper proposes some initial results of the authors' ongoing research on the development of a ROS2based framework for industrial applications due to which a robotic manipulator equipped with a depth camera is able to have simultaneously perception, recognition and re-planning capabilities in a dynamic and changing environment. This framework is potentially applicable to various industrial robots and depth cameras; here, the first results of the experimental implementation on an Omron TM5-900 collaborative robot equipped with a fixed depth camera will be shown.

TUCer team developed a prototype, portable chassis dynamometer capable of carrying out experimentation with alternate scenarios of acceleration, constant speed and deceleration, in a fully automated way. A controller scheme based on Robot Operating System was programmed, using open source tools. The testing scenarios are implemented using a motor/generator and an electronically controlled load, embedded on the dynamometer. Extensive testing during TUCer systems' development and real race conditions proved the dynamometer's reliability and functionality.

One way to improve the robustness and flexibility of robot performance is to let the robot learn from its experiences. In this paper, we describe the architecture and knowledge-representation framework for a service robot being developed in the EU project RACE, and present examples illustrating how learning from experiences will be achieved. As a unique innovative feature, the framework combines memory records of low-level robot activities with ontology-based high-level semantic descriptions.

This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow pr...

Vector synthesis of fault testing (simulation) map for logic is proposed, which without simulation allows to determine of all faults detected on test sets, as well as determining test sets to detect specified faults. For synthesis, a superposition of smart data structures is used, containing: a deductive matrix D, as a derivative of the logical vector L, test truth table T, and fault truth table F. The deductive matrix is seen as the gene of functionality and base of Fault simulation mechanism to solve all the problems of testing and verification. In the matrix synthesis, an axiom is used: all the mentioned tables are identical in shape to each other and always interact with each other convolutionally T⊕L⊕F=0. A universal deductive reversing converter "test-faults" and "faults-test" for logical functionalities of any dimension is proposed. Converter functions: fault simulation on test sets T→F and synthesis of test sets F→T to detect the specified faults. The converter can be used as a test generation and fault simulation service for IP-core system-on-chip (SoC) under the IEEE 1500 SECT standard. Based on the deductive matrix, fault testing map for logic is built, where each test set is matched with the logic detected faults of the input lines.

Quadruped home surveillance robots represent a promising advancement in home security and automation. This innovative robotic system is equipped with four-legged locomotion, allowing it to traverse various terrains within a household environment. The robot's primary function is surveillance, and it is equipped with high-definition cameras, motion sensors, and object recognition software. These sensors enable the robot to detect intruders, track their movements, and capture real-time video footage for remote monitoring. The quadruped robot's compact and agile design allows it to navigate through narrow spaces and overcome obstacles, ensuring it can patrol every corner of a home effectively. Its autonomous operation is made possible through advanced artificial intelligence algorithms, ensuring that it can detect anomalies and respond to security threats promptly. Furthermore, integrating the robot with smart home systems enables seamless communication with other connected devices and allows homeowners to control and monitor it remotely.

Research investigations in the realm of micro-robotics often center around strategies addressing the multi-robot task allocation (MRTA) problem. Our contribution delves into the collaborative dynamics of micro-robots deployed in targeted hostile environments. Employing advanced algorithms, these robots play a crucial role in enhancing and streamlining operations within sensitive areas. We adopt a tailored GREEDY approach, strategically adjusting weight parameters in a multi-objective function that serves as a cost metric. The objective function, designed for optimization purposes, aggregates the cost functions of all agents involved. Our evaluation meticulously examines the MRTA efficiency for each micro-robot, considering dependencies on factors such as radio connectivity, available energy, and the absolute and relative availability of agents. The central focus is on validating the positive trend associated with an increasing number of agents constituting the cluster. Our methodology introduces a trio of micro-robots, unveiling a flexible strategy aimed at detecting individuals at risk in demanding environments. Each microrobot within the cluster is equipped with logic that ensures compatibility and cooperation, enabling them to effectively execute assigned missions. The implementation of MRTA-based collaboration algorithms serves as an adaptive strategy, optimizing agents' mobility based on specific criteria related to the characteristics of the target site.

This study proposes an autonomous navigation system for hexapod robots, promising in complex rescue scenarios. The system is tested in simulations under three environments: rocky, cracked flooring, and inclined surfaces. utilizing light detection and ranging (LiDAR) and simultaneous localization and mapping (SLAM), the robot recognizes positions and constructs environmental maps. Implemented via robot operating system, the research successfully applies navigation and mapping using hector_slam. LiDAR mapping yields satisfactory accuracy, with average errors of 0.21% for general mapping and 5.34% for circular paths. Within a 2-meter range, navigation achieves good accuracy, averaging 1.2% error on the x-axis and 0.011% on the y-axis during linear motion. Navigational repeatability improves, with reliable results showing an average error of 4.33 cm on the x-axis and 0.5 cm on the y-axis when returning to starting points. Arena testing with varied obstacles demonstrates successful obstacle traversal. However, in the second test, limitations in hardware, notably the Raspberry Pi 4 CPU usage reaching 97% during navigation, hindered reaching the third target.

In unstructured agricultural fields where autonomous navigation is challenging and demands additional safety, the operator’s experience and knowledge are essential for supervising operations and making decisions beyond the robot’s autonomous capabilities. Local networks with long-range wireless communication combined with digital twin concepts are promising solutions that can be used for robot teleoperation. The purpose of this study was to demonstrate the feasibility of supervising a mobile robot inside berry orchards using a digital shadow from a long-range distance (between 300 and 3000 m), with the primary objective of assisting the robot in navigating in complex situations such as row-end turning. This involved creating a virtual representation of the robot that mirrors its state and actions, allowing the remote operator to monitor and guide the robot effectively. The system comprised a GPS-based navigation controller with collision avoidance sensors, two sets of LoRa transmitters and repeaters, a simulation environment with a digital shadow of the robot, and a graphical user interface for the remote operator. Information about the digital shadow’s state, including location, orientation, and distances to obstacles, was received as a message by the LoRa gateway and was used to update the path for the actual robot that interfaced with the Robot Operating System (ROS). The main research hypothesis aimed to test the quality of the LoRa communication link between the robot and the operator, as well as the robustness of the robot’s control system, with an emphasis on the architecture, communication link, and situation awareness creation. Preliminary results showed that depending on the environment, the average packet loss was 12% at distances of approximately 2300 m. Our results highlight some of the core technical challenges that need to be addressed for an effective teleoperation system, including latency, stability, and the limited range of wireless communication. Future works involves evaluating the performance and reliability of the proposed method under different field conditions and scenarios, as well as considering the use of the 5G network for a significant improvement in data transmission speed, navigation efficiency, and visual feedback. Upon successful implementation, this study has the potential to enhance the efficiency and safety of robot navigation, providing a practical solution for remote supervision in challenging environments.

El Desarrollo de software Dirigido por Modelos (MDD, Model Driven software Development) aparece como una alternativa viable para aplicar técnicas de ingeniería de software en el desarrollo de sistemas robóticos. Su uso logra un nivel de abstracción superior, permitiendo utilizar los estándares propuestos para robótica, y así obtener ventajas como generalidad, reutilización, claridad, expresividad. Estas son todas cualidades inherentes a un proceso de creación de software eficiente y eficaz. El objetivo general de esta investigación es contribuir al mejoramiento de los procesos de desarrollo de software de los sistemas robóticos, a través del análisis del paradigma de desarrollo MDD aplicando los estándares definidos por la OMG.

Message oriented and robotics middleware play an important role in facilitating robot control, abstracting complex functionality, and unifying communication patterns between sensors and devices. However, using multiple middleware frameworks presents a challenge in integrating different robots within a single system. To address this challenge, we present Wrapyfi, a Python wrapper supporting multiple message oriented and robotics middleware, including ZeroMQ, YARP, ROS, and ROS 2. Wrapyfi also provides plugins for exchanging deep learning framework data, without additional encoding or preprocessing steps. Using Wrapyfi eases the development of scripts that run on multiple machines, thereby enabling cross-platform communication and workload distribution. We finally present the three communication schemes that form the cornerstone of Wrapyfi's communication model, along with examples that demonstrate their applicability.

Robots will soon interact with humans in settings outside of a lab. Since it will be likely that their bodies will not be as developed as their programming, they will not have the complex limbs needed to perform simple tasks. Thus they will need to seek human assistance by asking them for help appropriately. But how will these robots know how to act? This research will focus on the specific nonverbal behaviors a robot could use to attract someone’s attention and convince them to interact with the robot. In particular, it will need the correct facial expressions and gestures to convince people to help them

The research described in this paper, focuses on a virtual reality headset system that integrates the Oculus Rift VR headset with a low cost Unmanned Aerial Vehicle (UAV) to allow for drone teleoperation and telepresence using the Robot Operating System (ROS). We developed a system that allows the pilot to fly an AR Drone through natural head movements translated to a set of flight commands. The system is designed to be easy to use for the purposes of training drone pilots. The user simply has to move their head and these movements are translated to the quadrotor which then turns in that direction. Altitude control is implemented using a Wii Nunchuck joystick for altitude adjustment. The users use the Oculus Rift headset a 2D video stream from the AR Drone, which is then turned into a 3D image stream and presented to them on the headset. CCS CONCEPTS Software engineering → Distributed Computing;

When a human is taken to a new place, a new location, the individual first tries to perceive it, then gets to the geography of it and finally maps it. By mapping the whole location, the human goes around the place without any difficulty. In this project, the implementation will be done in such a way that the robot also consumes the parameters of the area in the same way as the human, by first perceiving the location entirely, followed by mapping the whole place and then going around it, thereby marking the location to absolute precision and making it flawless for navigation. To accomplish this performance, a self-navigating algorithm using SLAM (Simultaneous Localization and Mapping) is written and executed. The robot, by using ultrasonic sensors will measure the distances of the surrounding objects.

The Robot Operating System (ROS) is an open source framework for the development of robotic software, in which a typical system consists of multiple processes communicating under a publishersubscriber architecture. A great deal of development time goes into orchestration and making sure that the communication interfaces comply with the expected contracts (e.g. receiving a message leads to the publication of another message). Orchestration mistakes are only detected during runtime, stressing the importance of component and integration testing in the verification process. Property-based Testing is fitting in this context, since it is based on the specification of contracts and treats tested components as black boxes, but there is no support for it in ROS. In this paper, we present a first approach towards automatic generation of test scripts for property-based testing of various configurations of a ROS system. CCS CONCEPTS • Software and its engineering → Software testing and debugging; Publish-subscribe / event-based architectures; • Computer systems organization → Robotics;

One way to improve the robustness and flexibility of robot performance is to let the robot learn from its experiences. In this paper, we describe the architecture and knowledge-representation framework for a service robot being developed in the EU project RACE, and present examples illustrating how learning from experiences will be achieved. As a unique innovative feature, the framework combines memory records of low-level robot activities with ontology-based high-level semantic descriptions.

Derinlik kameraları ve Lidar sistemleri, dijital pazardaki sensörlerin önemli bir parçası haline geldi. Derinlik kameraları,  bir nesnenin derinliğini ve karşılık gelen piksel ve doku bilgilerini algılar. Lidar sistemler, bir nesnenin mesafesini ve derinlik aralığını tahmin etmek için kullanılan bir uzaktan algılama teknolojisidir. İki teknoloji doğası gereği benzer olmakla birlikte, bazı farklılıkları da vardır. Bu yazıda öncelikle, ışık süresi kameralarının temelindeki ölçüm ilkelerini açıklayacağız: bir ışık darbesinin cihazdan nesneye gitmesi için geçen zamanı doğrudan ölçen darbeli ışık kameraları ve tekrar ve yayılan ve alınan sinyaller arasındaki faz farkını ölçen ve dolayısıyla seyahat süresini dolaylı olarak elde eden sürekli dalga modülasyonlu ışık kameralarını inceleyecegiz.

Abstract, the revolution in the Internet of Things (IoT), creates new challenges for the field of robotics. For the construction of applications and communication of robotic systems with the cloud, certain protocols and concepts were created which facilitate and standardize this interconnection process. This article shows a specific application of the integration of the IoT concept with robotics, represented as an autonomous path repair and maintenance system with a robotic system in the cloud, which uses web services and communication protocols such as XML and BPEL to The interconnection between the information necessary for the repair and maintenance of the roads, decision making and tasks assigned between robots. In this way, it is demonstrated how it is possible to articulate all the concepts to achieve solutions in a real road repair scenario.  La revolucion del internet de las cosas - IoT, genera nuevos retos para el campo de la robotica; Para la construccion de las aplicacion...

This work describes a mobile system that has been developed to precisely and reliably carry out echolocation experiments (data collection) for later analysis. A Kobuki robot was used as a base unit, providing mobility and accurate odometry. Custom mounting hardware was designed for the robot to accommodate a laptop for controlling the robot, along with a Microsoft Kinect sensor and ultrasonic transducers for taking experimental measurements. A Python based software package was designed to provide simple control of the robot and its sensors. This software was designed to work within the Robot Operating System (ROS) framework, and includes high-level interfaces for controlling robot movement and for the simultaneous playing and recording of sounds. Each data collection experiment consists of a sequence of movements and measurements that the robot should perform. The system we have developed will make future data collection simple, allowing for subsequent study and analysis of echo signals.

This paper presents a multi-ultrasonic-sensor system designed for Simultaneous Localization and Mapping (SLAM) on the open source robot Kobuki Turtlebot. The operating system used in this research project, Robot Operating System (ROS), is also an open source project initially released in 2007. Our system has reduced response time and lower cost than traditional laser sensors. It consists of 8 HY-SRF05 rangers that can work sequentially on an embedded system such as a Raspberry Pi. The communication between the embedded system and the robot is achieved with a Python package that communicates via local TCP/IP. Our multi-sensor structure can be applied to a multi-agent system, composed of 3 identical robots. Our results from preliminary experiments show the possibility of conducting SLAM using our designed system. By implementing this improved design on a multi-agent system, the SLAM process can be conducted more efficiently and accurately than a single robot with a laser sensor.

Resumen. La Revisión Sistemática de Literatura (RSL) es una estrategia de investigación destinada a obtener evidencia desde artículos científicos almacenados en repositorios digitales, la cual debe ser sistemática, reproducible y auditable para formular preguntas de investigación sobre un área temática o fenómeno de interés y para buscar, seleccionar, analizar y comunicar toda la investigación relevante, básica o aplicada, necesaria para responder a dichas preguntas. La RSL (al igual que el Mapeo Sistemático) se puede realizar sobre estudios primarios o secundarios. En ambos casos se requieren procesos y métodos bien establecidos. Si bien existen guías del proceso de la RSL para Ingeniería de Software, las cuales indican los pasos a seguir en las tres fases del proceso propuesto por Kitchenham, consideramos que sería un aporte para la comunidad científica el mejorar la especificación de su proceso. Para este fin, empleando perspectivas de modelado de proceso, documentamos la especificación del proceso de RSL usando principalmente el lenguaje SPEM (Software & Systems Process Engineering Metamodel). En tanto desarrollamos el trabajo, ejemplificamos aspectos del proceso usando una RSL piloto sobre ontologías de testing de software ya realizada. Cabe acotar que el proceso propuesto de RSL también puede ser usado para Mapeo Sistemático.

Swarms of tiny flying robots hold great potential for exploring unknown, indoor environments. Their small size allows them to move in narrow spaces, and their light weight makes them safe for operating around humans. Until now, this task has been out of reach due to the lack of adequate navigation strategies. The absence of external infrastructure implies that any positioning attempts must be performed by the robots themselves. State-of-the-art solutions, such as simultaneous localization and mapping, are still too resource demanding. This article presents the swarm gradient bug algorithm (SGBA), a minimal navigation solution that allows a swarm of tiny flying robots to autonomously explore an unknown environment and subsequently come back to the departure point. SGBA maximizes coverage by having robots travel in different directions away from the departure point. The robots navigate the environment and deal with static obstacles on the fly by means of visual odometry and wall-following behaviors. Moreover, they communicate with each other to avoid collisions and maximize search efficiency. To come back to the departure point, the robots perform a gradient search toward a home beacon. We studied the collective aspects of SGBA, demonstrating that it allows a group of 33-g commercial off-the-shelf quadrotors to successfully explore a real-world environment. The application potential is illustrated by a proof-of-concept search-and-rescue mission in which the robots captured images to find "victims" in an office environment. The developed algorithms generalize to other robot types and lay the basis for tackling other similarly complex missions with robot swarms in the future.

The eld of autonomous robotics is growing at a rapid rate. The trend to use increasingly more sensors in vehicles is driven both by legislation and consumer demands for higher safety and reliable service. Nowadays, robots are found everywhere, ranging from homes, hospitals to industries, and military operations. Autonomous robots are developed to be robust enough to work beside humans and to carry out jobs efciently. Humans have a natural sense of understanding of the physical forces acting around them like gravity, sense of motion, etc. which are not taught explicitly but are developed naturally. However, this is not the case with robots. To make the robot fully autonomous and competent to work with humans, the robot must be able to perceive the situation and devise a plan for smooth operation, considering all the adversities that may occur while carrying out the tasks. In this thesis, we present an autonomous mobile robot platform that delivers the package within the VNIT campus w...

This research investigates the symbiotic relationship between artificial intelligence (AI), machine learning (ML), and cybersecurity within the context of fostering sustainable development. The study explores the efficacy of AI-driven cybersecurity measures in fortifying digital infrastructures against evolving cyber threats. A glimpse of the quantitative results reveals compelling insights: AI-based systems showcased an average threat detection accuracy of 92.5% across diverse cyber threat types, with a minimal false positive rate of 3.2%. The implementation of ML algorithms reduced response times to cyber attacks by 40%, underscoring their pivotal role in prompt threat mitigation. Furthermore, the research elucidates the efficiency of AI in preventing phishing attacks (95%) and prioritizing critical vulnerabilities for patching, resulting in a 30% reduction in high-risk unpatched vulnerabilities. These glimpses into the quantitative outcomes underscore the transformative potential of AI and ML in bolstering cybersecurity measures, aligning with sustainable development goals by fortifying digital resilience and protecting critical infrastructures.

The current navigation system requires the user to continuously map with the navigation application with the real-world environment. It sometimes leads to an in aut to describe the design of an application that will provide the same reality-based environment as that seen mulation or content to provide an enhanced view to the user during navigation. We will be using Augmented reality to provide users with more information, computer-assisted Google's existing Maps people can be done from the data stored on the cloud and will be augmented when scanned through the camera of the phone which is the only requirement of the application. It also enables the users to navigate indoors with the help of a radar map and arrow pointing towards the points of interest. This application enables the decisions. The user can also navigate to different points of interest-indoor as well as outdoor. It also enables the recognition of places, as well as people and the details related to the same, get augmented.

Autonomous Underwater Vehicles (AUVs) are suitable platforms for a wide type of applications in the oceanic environment. These applications are developed in various fields such as scientific surveying, off-shore industry and defense. The employment of AUVs requires less human support and reduces operation costs. Due to the changing marine environment these vehicles must deal with uncertain and hostile conditions to perform its tasks. In the marine robotics matter, the INTELYMEC group has developed in 2012 an AUV prototype called Ictiobot, a low cost experimental platform for multipurpose missions. In this paper an upgrade of the original prototype is presented, the Ictiobot-40, conceived to perform acoustic imaging surveying missions of up to two hours and maximum depths of 40 meters. The new software and hardware architectures and mechanical structure improvements, are detailed. In addition to these technical details, initial experimental results of the AUV performance in quiet wat...

A long-standing goal of robotics is to substitute humans in unreachable or dangerous environments, and one of such environments is urban search and rescue (USAR) domain. For USAR tasks we have selected a novel Russian crawlertype robot Engineer, and this paper presents our first steps toward modelling the robot in ROS/Gazebo environment. We convert provided by robot developers CAD models into workable ROS-based 3D simulation and incorporate physical parameters of the mechanisms into the model, focusing on track simulation. Robot motion and relative interplay of its visible mechanical parts is visualized in RViz software. The proposed model is integrated into a readyto-use ROS navigation stack and the model's behavior is thoroughly investigated while navigating through static obstacles populated scene in Gazebo environment. In this paper, we introduce an approximation of track-surface interaction of Engineer robot with modeled environment, and discuss the applicability of ROS-based "Gazebo-tracks" package for robust robot locomotion.

1. Modelling and animation of crawler UGV's caterpillars is a complicated task, which has not been completely resolved in ROS/Gazebo simulators. In this paper, we proposed an approximation of track-terrain interaction of a crawler UGV, perform modelling and simulation of Russian crawler robot "Engineer" within ROS/Gazebo and visualize its motion in ROS/RViz software. Finally, we test the proposed model in heterogeneous robot group navigation scenario within uncertain Gazebo environment.

Summary. We consider the problem of exploring an unknown environment using a swarm of autonomous robots with collective behavior emerging from their local rules. Each robot has only a very restricted view on the environment which makes cooperation dicult. We introduce a software system which is capable of simulating a large number of such robots (e.g. 1000) on highly complex terrains with millions of obstacles. Its main purpose is to easily integrate and evaluate any kind of algorithm for controlling the robot behavior. The simulation may be observed in real-time via a visualization that displays both the individual and the collective progress of the robots. We present the system design, its main features and underlying concepts.

Remote Attestation (RA) allows a trusted entity (verifier) to securely measure internal state of a remote untrusted hardware platform (prover). RA can be used to establish a static or dynamic root of trust in embedded and cyber-physical systems. It can also be used as a building block for other security services and primitives, such as software updates and patches, verifiable deletion and memory resetting. There are three major types of RA designs: hardwarebased, software-based, and hybrid, each with its own set of benefits and drawbacks. This paper presents the first hybrid RA design-called HYDRAthat builds upon formally verified software components that ensure memory isolation and protection, as well as enforce access control to memory and other resources. HYDRA obtains these properties by using the formally verified seL4 microkernel. (Until now, this was only attainable with purely hardware-based designs.) Using seL4 imposes fewer hardware requirements on the underlying microprocessor. Also, building upon a formally verified software component increases confidence in security of the overall design of HYDRA and its implementation. We instantiate HYDRA on two commodity hardware platforms and assess the performance and overhead of performing RA on such platforms via experimentation; we show that HYDRA can attest 10MB of memory in less than 250msec when using a Speck-based cryptographic checksum.

The objective of our project is to develop a mobile robot capable of traversing an arena from a starting point A to a destination point B by taking the shortest path possible and avoiding any obstacles that may be encountered. The robot will be designed to accept user inputs from various sources, such as mobile devices or computers. Based on the given inputs, the robot will create a path to follow and navigate accordingly. Our aim is to equip the robot with the capability to autonomously map its surroundings, generate the optimal path, and navigate through the arena while avoiding any barriers or obstacles. The goal is to create a robot that is intelligent and efficient in its ability to navigate through various environments.

A multi-robot system is a set of robots that share a common objective and collaborate to achieve it. Multi-robot systems can address distributed and complicated real-world issues more effectively in various industries, including logistics, transportation, and industrial manufacturing. The higher system performance because of the collaborative efforts of numerous robots provides a substantial possible benefit of using a multirobot system rather than a single robot. The Lyapunov control approach is one advanced approach to coordinating multiple robots in warehouse logistics applications. The method has been successfully simulated on point masses. However, to be physically implemented in robotic devices still requires several processes that are not simple. The case study focuses on finding the possibility of a distributed cooperative mobile robot using a LiDAR sensor to recognize the 'friend' robot and the obstacle. This paper shows that each robot can recognize well between its neighbor or obstacles and maintain its formation of 1 m during the trip with obstacle presence. The simulations were performed in Webots to verify the proposed algorithms. The mathematical analysis and the experiment prove the system's stability by seeing the robots' velocity.

This paper addresses a formation tracking problem of multiple low-cost underwater drones by implementing distributed adaptive neural network control (DANNC). It is based on a leader-follower architecture to operate in hazardous environments. First, unknown parameters of underwater vehicle dynamics, which are important requirements for real-world applications, are approximated by a neural network using a radial basis function. More specifically, those parameters are only calculated by local information, which can be obtained by an on-board camera without using an external positioning system. Secondly, a potential function is employed to ensure there is no collision between the underwater drones. We then propose a desired configuration of a group of unmanned underwater vehicles (UUVs) as a time-variant function so that they can quickly change their shape between them to facilitate the crossing in a narrow area. Finally, three UUVs, based on a robot operating system (ROS) platform, are...

Collaborative autonomous systems operating at edges, e.g., TurtleBots, need adaptive security mechanisms (i.e., Confidentiality, Integrity, Availability) that meet the changing mission requirements and the available processing capacities. We have designed and implemented a security platform that supports secure communication among autonomous systems of robots (e.g., TurtleBots), security of Robot Operating System (ROS) communication network, the integrity of the information exchanged among the robots and secured availability of the data and access to services. For designing the security platform, we have used architecture and design patterns along with the respective security protocols. Our solution provides seamless security incorporation in heterogeneous collaborative autonomous entities. We have leveraged architectural strategies to incorporate publickey encryption, maintain global order of events and incorporate fault tolerance. We assert that the presented security platform can facilitate easy adoption of secure control, communication and information gathering in collaborative autonomous systems with resource constraint edge nodes.

With the aim of allowing the efficient and realistic simulation of swarm algorithms for exploration and coverage, we present the tool Multi-Agent Exploration Simulator (MAES), which is an open-source physics-based discrete step multi-robot simulator. MAES features movement in a continuous 2D space, realistic physics based on the Unity framework, advanced visualization techniques such as heatmaps, custom wireless signal degradation, both randomly generated and custom user-provided maps, and a ROS (Robot Operating System) interface. This latter characteristic could allow to port the simulated algorithms to real-world robots. We present performance tests, conducted with rather modest hardware, showing that MAES is able to simulate up to 5 robots in ROSMode (using the ROS integration) and up to 120 robots in UnityMode (development performed directly into the C# Unity Editor). A usability test was conducted which hinted that the target audience of robotics researchers and developers is a...

The paper investigate the pressure applied during navigation of robot on arcuated prefab and smooth/ marble path using non linear EKF (map based). The analysis part uses experiments on 6DoF robot using (vMicro) as meta software on.Net framework and XCTU multiplatform, which was not considered in Compressed EKF SLAM. In this paper, analysis on pressure exerted on smooth surface and prefab surfaces in Z direction which gives conclusive results for determination of surfaces.

We present a distributed control strategy for a team of quadrotors to autonomously achieve a desired 3D formation. Our approach is based on local relative position measurements and does not require global position information or inter-vehicle communication. We assume that quadrotors have a common sense of direction, which is chosen as the direction of gravitational force measured by their onboard IMU sensors. However, this assumption is not crucial, and our approach is robust to inaccuracies and effects of acceleration on gravitational measurements. In particular, converge to the desired formation is unaffected if each quadrotor has a velocity vector that projects positively onto the desired velocity vector provided by the formation control strategy. We demonstrate the validity of proposed approach in an experimental setup and show that a team of quadrotors achieve a desired 3D formation.

The environment scanning has been important in mobile robot studies and applications that may help people to investigate, monitor and study a variety of environments such as unexplored or hazardous surroundings. The main issue regarding this research is the efficiency of the mobile robot system and the accuracy of the laser sensor. This project presents designing and developing of an Autonomous Guided Vehicle (AGV) with indoor mapping capability only using a LIDAR as primary sensor and without odometry information of the AGV base. Hector mapping SLAM algorithm is adopted for building the map around the robot which is equipped with single 1D laser range finder in ROS platform and viewed through ROS visualization Rviz. To evaluate the performance of the sensor and the AGV base, usability testing was carried out in multiple experiments. The Lidar results for object distance measurement show over 97% accuracy for targets within 9 meter radius of the AGV base and the mapping accuracy is 83% for areas larger than 34sq.m and over 94% for areas smaller. Arduino Uno is used to communicate and control the motors. The AGV is remotely controlled via wireless communication. It is also designed to transport load in closed areas such as labs or libraries therefore, the design base is capable of carrying upto 20 kilograms at a speed of 0.6 m/s. Discussion of errors and troubleshooting methods are presented after the experiment results.

An introduction to ROS, an open source robot operating system, is given in this paper. In terms of process management and scheduling, ROS is not an operating system. This study explains how Robotic Operating System (ROS) can be used to control items (such as vehicles) remotely and cautiously without human intervention at the location. Instead, it gives heterogeneous computing clusters a structured communication layer on top of the host operating system. This document gives a quick explanation of how ROS fits into the current robot software architecture and how we may utilize it for AUVs (Automated under water vehicle). One of the media that connects the entire world to the internet is optical cable, which is typically installed underground or under water. As a result, it is challenging to inspect them thoroughly because it costs more to do so. To address this issue, we are presenting a solution that involves developing a robotic operating system that would assist in checking the underwater/underground cables. To put this into practice, we have been utilizing VMWare Workstation to virtually install Ubuntu OS, where we will be installing ROS packages, with the ROS-Gazebo toolbox serving as one of the primary tools. We are testing the implemented software with the standard inputs. We are using light radiation as the primary factor to assess the condition of the optical cable.

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is pe...

Head pose estimation is a fundamental function for several applications in human-computer interactions. Accurate six degrees of freedom head pose estimation (6DoF-HPE) with full-range angles make up most of these applications, which require sequential images of the human head as input. Most existing head pose estimation methods focus on a three degrees of freedom (3DoF) frontal head, which restricts their applications in real-world scenarios. This study presents a framework designed to estimate a head pose without landmark localization. The novelty of our framework is to estimate the 6DoF head poses under full-range angles in real-time. The proposed framework leverages deep neural networks to detect human heads and predict their angles using single shot multibox detector (SSD) and RepVGG-b1g4 backbone, respectively. This work uses red, green, blue, and depth (RGB-D) data to estimate the rotational and translational components relative to the camera pose. The proposed framework employs a continuous representation to predict the angles and a multi-loss approach to update the loss functions for the training strategy. The regression function combines the geodesic loss with the mean squared error. The ground-truth labels were extracted from the public dataset Carnegie Mellon university (CMU) Panoptic for full head angles. This study provides a comprehensive comparison with state-of-the-art methods using public benchmark datasets. Experiments demonstrate that the proposed method achieves or outperforms state-of-the-art methods. The code and datasets are available at: