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Industrial robotic systems (IRS) consist of industrial robots that automate industrial processes. They accurately perform repetitive tasks, replacing or assisting with dangerous jobs like assembly in the automotive and chemical industries. Failures in these systems can be catastrophic, so it is important to ensure their quality and safety before using them. One way to do this is by applying a software testing process to find faults before they become failures. However, software testing in industrial robotic systems has some challenges. These include differences in perspectives on software testing from people with diverse backgrounds, coordinating and collaborating with diverse teams, and performing software testing within the complex integration inherent in industrial environments. In traditional systems, a well-known development process uses simple, structured sentences in English to facilitate communication between project team members and business stakeholders. This process is called behavior-driven development (BDD), and one of its pillars is the use of templates to write user stories, scenarios, and automated acceptance tests. We propose a software testing (ST) approach called automated acceptance testing for industrial robotic systems (AAT4IRS) that uses natural language to write the features and scenarios to be tested. We evaluated our ST approach through a proof-of-concept, performing a pick-and-place process and applying mutation testing to measure its effectiveness. The results show that the test suites implemented using AAT4IRS were highly effective, with 79% of the generated mutants detected, thus instilling confidence in the robustness of our approach. [ABSTRACT FROM AUTHOR]

This survey introduces 101 new publications on applications of Clifford's geometric algebras (GAs) newly published during 2022 (until mid‐January 2023). The selection of papers is based on a comprehensive search with Dimensions.ai, followed by detailed screening and clustering. Readers will learn about the use of GA for mathematics, computation, surface representations, geometry, image, and signal processing, computing and software, quantum computing, data processing, neural networks, medical science, physics, electric engineering, control and robotics. [ABSTRACT FROM AUTHOR]

Current practice in simulation and implementation of robot controllers is usually undertaken with guidance from high-level design diagrams and pseudocode. Thus, no rigorous connection between the design and the development of a robot controller is established. This paper presents a framework for designing robotic controllers with support for automatic generation of executable code and automatic property checking. A state-machine based notation, RoboChart, and a tool (RoboTool) that implements the automatic generation of code and mathematical models from the designed controllers are presented. We demonstrate the application of RoboChart and its related tool through a case study of a robot performing an exploration task. The automatically generated code is platform independent and is used in both simulation and two different physical robotic platforms. Properties are formally checked against the mathematical models generated by RoboTool, and further validated in the actual simulations and physical experiments. The tool not only provides engineers with a way of designing robotic controllers formally but also paves the way for correct implementation of robotic systems. [ABSTRACT FROM AUTHOR]

The Geometric Algebra Fulcrum Library (GA-FuL) version 1.0 is introduced in this paper as a comprehensive computational library for geometric algebra (GA) and Clifford algebra (CA), in addition to other classical algebras. As a sophisticated software system, GA-FuL is useful for practical applications requiring numerical or symbolic prototyping, optimized code generation, and geometric visualization. A comprehensive overview of the GA-FuL design is provided, including its core design intentions, data-driven programming characteristics, and extensible layered design. The library is capable of representing and manipulating sparse multivectors of any dimension, scalar kind, or metric signature, including conformal and projective geometric algebras. Several practical and illustrative use cases of the library are provided to highlight its potential for mathematical, scientific, and engineering applications. The metaprogramming code optimization capabilities of GA-FuL are found to be unique among other software systems. This allows for the automated production of highly efficient code, based on powerful geometric modeling formulations provided by geometric algebra. [ABSTRACT FROM AUTHOR]

Robot software development can be considered as a component-driven process, and existing ROS components, such as an ROS node, can be reused to construct robot applications. By reusing the ROS node, the development process of robot software can be significantly accelerated. However, the challenges in reusing ROS nodes primarily lie in the scattered organization of ROS node information. To address this challenge, this paper proposes a method to construct an ROS node knowledge graph (RNKG) based on high-quality open-source robot projects. In order to build a high-quality knowledge graph of ROS nodes, we first constructed a high-quality dataset of open-source robot projects. Since ROS node knowledge can exist in both text and code formats, we initially separated the data in the dataset into code data and text data, and then applied different knowledge extraction methods to extract corresponding entities. Finally, we integrated a series of ROS node knowledge and organized it into a knowledge graph. To validate the effectiveness of the constructed ROS node knowledge graph, we first verified the completeness of the entities and the accuracy of relationships in the knowledge graph. Next, we evaluated the performance of the ROS node knowledge graph in assisting developers with the downstream task of finding ROS nodes. These findings suggest that our proposed method for constructing an ROS node knowledge graph is feasible and demonstrate that the ROS node knowledge graph helps search ROS nodes. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method for minimum distance calculation between a robot and its environment and the implementation framework as a tool for the verification of robotic systems' safety. Collision is the most fundamental safety problem in robotic systems. Therefore, robotic system software must be verified to ensure that there are no risks of collision during development and implementation. The online distance tracker (ODT) is intended to provide minimum distances between the robots and their environments for verification of system software to inspect whether it causes a collision risk. The proposed method employs the representations of the robot and its environment with cylinders and an occupancy map. Furthermore, the bounding box approach improves the performance of the minimum distance calculation regarding computational cost. Finally, the method is applied to a realistically simulated twin of the ROKOS, which is an automated robotic inspection cell for quality control of automotive body-in-white and is actively used in the bus manufacturing industry. The simulation results demonstrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is the second part of a two-part study on promoting the use of Open-Source Software (OSS) in Mechatronics and Robotics Engineering (MRE) education. Part I demonstrated the capabilities and limitations of several popular OSS, namely, Python, Java, Modelica, and GNU Octave, in model simulation and analysis of dynamic systems, through a DC motor example. The DC motor was chosen as a representative of a large class of dynamic systems described by linear differential equations. The perceptions of MRE community members about the OSS and their applications, gathered through an online survey, were also presented in Part I. In this paper, another fundamental pillar of MRE systems development, i.e., controller implementation, is considered. To this end, the OSS above, along with Gazebo, are used to simulate the closed-loop trajectory tracking performance of a 2-DOF robot manipulator, controlled by a PID controller. Robot manipulators represent a broader category of dynamic systems, which are described by nonlinear differential equations. Furthermore, PID controllers are one of the most versatile closed-loop control methodologies which have been established as the industry standard. Showcasing the implementation of this important category of controllers through OSS can promote their use in a wide range of MRE problems and projects. This paper also provides an overview of the potentials, limitations, and challenges regarding the use of each of the above OSS in solving the aforementioned problems. The OSS are increasingly being adapted as industry standard; furthermore, their numerous benefits pose them as a viable option to complement traditional higher education courses along with facilitating online and remote education. Therefore, this two-part paper and the various problems showcased and solved therein aim to pave the way towards further utilization of the OSS in MRE higher education, reaping the wide range of their benefits, and preparing the students for the future workforce. Full scripts of the codes summarized and discussed in this paper, along with Matlab scripts included to enable comparison, are made freely available on the Github repository of this paper. [ABSTRACT FROM AUTHOR]

Autonomous aerospace technology is an innovation activity currently included in NATO’s operation areas having a profound impact on security and defense. SpaceSysLab facility is the new Laboratory from Romania, able to test control algorithms for autonomous missions and to develop hardware (coupling mechanisms, navigation sensors and actuators). This robot-based test lab is used to test, verify and validate the R&D systems. The degree of novelty is expressed by the fact that the SpaceSysLab designed as a SIL+HIL (Software-in-the-Loop+Hard-inthe-Loop) simulator is a combination of hardware and software simulator that can test and verify space robots, autonomous VTOL (Vertical Take-off and Landing) of UAVs on mobile platforms in complex coupling phases. This technology allows hardware/software robotic broadband systems to be controlled by simulating every phase. SpaceSysLab consists of a virtual platform (VP) based on GNC system (guidance, navigation, and control) and an experimental platform (EP) based on a robotic system. This system permits to simulate the meeting and coupling maneuvers of the robots. VP+EP test system is a hybrid simulator, designed to simulate simultaneously the relative dynamics of two motion bodies on relatively short distances in meeting/coupling maneuvers (rendezvous, proximity and docking – RP&D, VTOL). [ABSTRACT FROM AUTHOR]

Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehouse. [ABSTRACT FROM AUTHOR]

Swarm Robotics is inspired by the biological swarms of social insects such as ants and bees, where individuals performing basic tasks give rise to complex behavior. It utilizes a team of cooperating robots to perform tasks more efficiently than possible by isolated robots. In this research, we study the exploration of unknown indoor areas using robots that coordinate with each other. In particular, we implement the Reverse Nearest Neighbor coordination algorithm with certain modifications to account for real-world constraints. The library developed as part of this work provides scripts to help with robotic tasks for exploration and robotic arm control that can be used to set up simulation tools like VREP, without much prior experience thereby lowering the barrier for entry and making the robotics projects more accessible. [ABSTRACT FROM AUTHOR]

Background: The development of robotic systems has provided an alternative to frame-based stereotactic procedures. The aim of this experimental phantom study was to compare the mechanical accuracy of the Robotic Surgery Assistant (ROSA) and the Leksell stereotactic frame by reducing clinical and procedural factors to a minimum. Methods: To precisely compare mechanical accuracy, a stereotactic system was chosen as reference for both methods. A thin layer CT scan with an acrylic phantom fixed to the frame and a localizer enabling the software to recognize the coordinate system was performed. For each of the five phantom targets, two different trajectories were planned, resulting in 10 trajectories. A series of five repetitions was performed, each time based on a new CT scan. Hence, 50 trajectories were analyzed for each method. X-rays of the final cannula position were fused with the planning data. The coordinates of the target point and the endpoint of the robot- or frame-guided probe were visually determined using the robotic software. The target point error (TPE) was calculated applying the Euclidian distance. The depth deviation along the trajectory and the lateral deviation were separately calculated. Results: Robotics was significantly more accurate, with an arithmetic TPE mean of 0.53 mm (95% CI 0.41–0.55 mm) compared to 0.72 mm (95% CI 0.63–0.8 mm) in stereotaxy (p < 0.05). In robotics, the mean depth deviation along the trajectory was −0.22 mm (95% CI −0.25 to −0.14 mm). The mean lateral deviation was 0.43 mm (95% CI 0.32–0.49 mm). In frame-based stereotaxy, the mean depth deviation amounted to −0.20 mm (95% CI −0.26 to −0.14 mm), the mean lateral deviation to 0.65 mm (95% CI 0.55–0.74 mm). Conclusion: Both the robotic and frame-based approach proved accurate. The robotic procedure showed significantly higher accuracy. For both methods, procedural factors occurring during surgery might have a more relevant impact on overall accuracy. [ABSTRACT FROM AUTHOR]

Unlike conventional software, robotic software suffers from a lack of methods and processes that could systematize and facilitate development. Thus, the application of software engineering techniques is at the heart of current issues in robotics. The work presented in this paper aims to facilitate the development of robotic software and to facilitate communication between experts in the field through the use of software engineering techniques and methods. It proposes RsaML (Robotic Software Architecture Modeling Language), a Domain Specific Modeling Language (DSML) dedicated to robotics, which takes into account the different categories of robotic software architectures and makes it possible to describe the latter independently from the implementation platform. The conceptual model defining the terminology and the hierarchy of concepts used for the description and representation of robotic software architectures in RsaML are presented in this article. RsaML is defined through a meta-model which represents the abstract syntax of the language. The real-time properties of robotic software architectures are identified and included in the meta-model. The use of RsaML is illustrated through several experimental scenarios of the language: the definition of a robotic system and the description of its software architecture, the verification of the semantics of a robotic software architecture, and the modeling of a robotic system whose software architecture does not belong to the usual categories. The support tool used for implementations and experimentation is Eclipse Modeling Framework (EMF). The results of experimentation showed good working of the proposed solution and made it possible to validate the main concepts of the RsaML language. [ABSTRACT FROM AUTHOR]

Mobile robots operate in various environments (e.g. aquatic, aerial, or terrestrial), they come in many diverse shapes and they are increasingly becoming parts of our lives. The successful engineering of mobile robotics systems demands the interdisciplinary collaboration of experts from different domains, such as mechanical and electrical engineering, artificial intelligence, and systems engineering. Research and industry have tried to tackle this heterogeneity by proposing a multitude of model-driven solutions to engineer the software of mobile robotics systems. However, there is no systematic study of the state of the art in model-driven engineering (MDE) for mobile robotics systems that could guide research or practitioners in finding model-driven solutions and tools to efficiently engineer mobile robotics systems. The paper is contributing to this direction by providing a map of software engineering research in MDE that investigates (1) which types of robots are supported by existing MDE approaches, (2) the types and characteristics of MRSs that are engineered using MDE approaches, (3) a description of how MDE approaches support the engineering of MRSs, (4) how existing MDE approaches are validated, and (5) how tools support existing MDE approaches. We also provide a replication package to assess, extend, and/or replicate the study. The results of this work and the highlighted challenges can guide researchers and practitioners from robotics and software engineering through the research landscape. [ABSTRACT FROM AUTHOR]

Flocking is a social animals' common behaviour observed in nature. It has a great potential for real-world applications such as exploration in agri-robotics using low-cost robotic solutions. In this paper, an extended model of a self-organised flocking mechanism using heterogeneous swarm system is proposed. The proposed model for swarm robotic systems is a combination of a collective motion mechanism with obstacle avoidance functions, which ensures a collision-free flocking trajectory for the followers. An optimal control model for the leader is also developed to steer the swarm to a desired goal location. Compared to the conventional methods, by using the proposed model, the swarm network has less requirement for power and storage. The feasibility of the proposed self-organised flocking algorithm is validated by realistic robotic simulation software. [ABSTRACT FROM AUTHOR]

Model-driven engineering (MDE) addresses central aspects of robotics software development. MDE could enable domain experts to leverage the expressiveness of models, while implementation details on different hardware platforms would be handled by automatic code generation. Today, despite strong MDE efforts in the robotics research community, most evidence points to manual code development being the norm. A possible reason for MDE not being accepted by robot software developers could be the wide range of applications and target platforms, which make all-encompassing MDE IDEs hard to develop and maintain. Therefore, we chose to leverage a large corpus of open-source software widely adopted by the robotics community to extract common structures and gain insight on how and where MDE can support the developers to work more efficiently. We pursue modeling as a complement, rather than imposing MDE as separate solution. Our previous work introduced metamodels to describe components, their interactions, and their resulting composition. In this paper, we present two methods based on metamodels for automated generation of models from manually written artifacts: (1) through static code analysis and (2) by monitoring the execution of a running system. For both methods, we present tools that leverage the potentials of our contributions, with a special focus on their application at runtime to observe and diagnose a real system during its execution. A comprehensive example is provided as a walk-through for robotics software practitioners. [ABSTRACT FROM AUTHOR]

Along the continuous transformations of enterprises, centralized service delivery models have been transitioning from an offshore employee sourcing to digital sourcing. Deploying software robots with robotics process automation (RPA) became one of the most important and fastest growing technologies in the recent years. In this research, the blended qualitative approach was applied, as a survey was followed by interpretative interviews and narrative analysis. This exploratory, empirical study discusses the key drivers behind RPA implementations (financials, employee impact, risks) identified among experts involved in the deployment of intelligent automation solutions at selected Nordic enterprises. The findings highlight the focus on the operational benefits like releasing employees from the most monotonous, non- value adding assignments, strengthening enterprise efficiency, compliance, and quality. Notably, the strategic and external (customer) impacts were not at the core of the RPA decision-making process among the studied case companies. The findings suggest that RPA is mainly perceived as an operational tool to automate and improve internal processes with little consideration of its strategic and external impact, including impact on customers and service quality. This is in line with the approach to the way of how benefits are calculated, looking mainly at savings on labor costs. [ABSTRACT FROM AUTHOR]

The research aims to investigate the effectiveness of the use of educational robotics applications used in linear equations unit on seventh grade students‟ algebraic reasoning. Teaching experiment research design was adopted for the research. In the study, usage of idea o-bot robotics software was taught to six 7th grade students. During the research process, three teaching experiment sessions applied after pretesting. Experiment sessions involved activities such as code writing in the face of problem situations, creating algorithms from the written code, monitoring algorithms in the simulation, and improving the algorithms when the desired result is not achieved. Lastly posttesting was performed. Comparing the pretest and posttest results, the data analyses showed that an observable development has been recorded in the algebraic reasoning skills of students‟ due to the fact that there are certain patterns and generalization processes in the developed algorithms. In addition, there has been an improvement in the participants' ability to use, understand and transform multiple representations in scientific process skills and problem solving skills. [ABSTRACT FROM AUTHOR]

The Robot Operating System (ROS) is a commonly used framework in many fields of robotics research, with increasing presence in the industry. The next iteration of this framework, ROS2, aims to improve observed shortcomings of its predecessor like deterministic memory allocation and real-time characteristics. The officially supported languages in ROS2 are C++ and Python, although several other contributed APIs for other languages exist. RCLAda is an API and accompanying tools for the ROS2 framework that enable the programming of ROS2 nodes in pure Ada with seamless integration into the ROS2 workflow. [ABSTRACT FROM AUTHOR]

Autonomous robots operating in everyday environments, such as hospitals, private houses, and public roads, are context-aware self-adaptive systems, i.e. they exploit knowledge about their resources and the environment to trigger runtime adaptation, so that they exhibit a behavior adequate to the current context. For these systems, context-aware self-adaptation requires to design the robot control application as a dynamically reconfigurable software architecture and to specify the adaptation logic for reconfiguring its variable aspects (e.g. the modules that implement various obstacle detection algorithms or control different distance sensors) according to specific criteria (e.g. enhancing robustness against variable illumination conditions). Despite self-adaptation is an intrinsic capability of autonomous robots, ad-hoc approaches are used in practice to design reconfigurable robot architectures. In order to enhance system maintainability, the control logic and the adaptation logic should be loosely coupled. For this purpose, the adaptation logic should be defined against an explicit representation of software variability in the robot control architecture. In this paper we propose a modeling approach, which consists in explicitly representing robot software variability with the MARTE::ARM-Variability metamodel, which has been designed as an extension of the UML MARTE profile. We evaluate the applicability of the proposed approach by exemplifying the software architecture design of a robot navigation framework and by analyzing the support provided by the ROS infrastructure for runtime reconfiguration of its variable aspects. • Dynamic reconfiguration of robotic systems is an emerging topic in robotics. • Maintainability requires to separate the application logic and the adaptation logic. • We propose a UML MARTE profile for modeling software variability in robotics. • We exemplify the proposed approach with a navigation case study. • We analyze the support to variability management of ROS and ROS2. [ABSTRACT FROM AUTHOR]

Robotic is a great substitute for human to explore the dangerous areas, and will also be a great help for disaster management. Although the rise of depth sensor technologies gives a huge boost to robotic vision research, traditional approaches cannot be applied to disaster-handling robots directly due to some limitations. In this paper, we focus on the 3D robotic perception, and propose a view-invariant Convolutional Neural Network (CNN) Model for scene understanding in disaster scenarios. The proposed system is highly distributed and parallel, which is of great help to improve the efficiency of network training. In our system, two individual CNNs are used to, respectively, propose objects from input data and classify their categories. We attempt to overcome the difficulties and restrictions caused by disasters using several specially-designed multi-task loss functions. The most significant advantage in our work is that the proposed method can learn a view-invariant feature with no requirement on RGB data, which is essential for harsh, disordered and changeable environments. Additionally, an effective optimization algorithm to accelerate the learning process is also included in our work. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks. [ABSTRACT FROM AUTHOR]

With the rising popularity of robotics in our modern world there is an increase in the number of engineering programs that offer the basic Introduction to Robotics course. This common introductory robotics course generally covers the fundamental theory of robotics including robot kinematics, dynamics, differential movements, trajectory planning and basic computer vision algorithms commonly used in the field of robotics. Joint programming, the task of writing a program that directly controls the robot’s joint motors, is an activity that involves robot kinematics, dynamics, and trajectory planning. In this paper, we introduce a new educational robotics tool developed for teaching joint programming. The tool allows the student to write a program in a modified C language that controls the movement of the arm by controlling the velocity of each joint motor. This is a very important activity in the robotics course and leads the student to gain knowledge of how to build a robotic arm controller. Sample assignments are presented for different levels of difficulty. [ABSTRACT FROM AUTHOR]

Validation and Verification (V&V) of autonomous robotic system software is becoming a critical issue. Among the V&V techniques at our disposal, formal approaches are among the most rigorous and trustworthy ones. Yet, the level of skills and knowledge required to use and deploy formal methods is usually quite high and rare. In this paper, we describe an approach that starts from a regular, but rigorous, framework to specify and deploy robotic software components, which can also automatically synthesize a formal model of these components. We describe how we can execute the resulting formal model, in place of a traditional implementation, and show how this provides the opportunity to add powerful monitoring and runtime verification capabilities to a system, e.g., to prevent collisions, or trigger an emergency landing. Since the runtime used to execute formal models is specifically designed to be faithful to their semantics, every execution (in the implementation) can be mapped to a trace in the specification. As a result, we can also prove many interesting properties offline, using model-checking techniques. We give several examples, such as properties about schedulability, worst-case traversal time, or mutual exclusion. We believe that having a consistent workflow, from an initial specification of our system, down to a formal, executable specification is a major advance in robotics and opens the way for verification of functional components of autonomous robots and beyond. We illustrate this claim by describing a complete example based on a genuine drone flight controller. • Programming and verifying the functional components of robotic systems. • Functional components are fully specified with a DSL by robot programmers. • Components implementation automatically synthesized from high-level specifications. • Formal model automatically synthesized from the same specifications. • Formal model is consistent with the semantics of the component implementation. • The synthesized model is used offline for verification with model-checking. • The formal model is used online for runtime verification and properties enforcement. [ABSTRACT FROM AUTHOR]

Partially observable Markov decision processes (POMDPs) are used in many robotic task classes from soccer to household chores. Determining an approximately optimal action policy for POMDPs is PSPACE-complete, and the exponential growth of computation time prohibits solving large tasks. This paper describes two techniques to extend the range of robotic tasks that can be solved using a POMDP. Our first technique reduces the motion constraints of a robot and, then, uses state-of-the-art robotic motion planning techniques to respect the true motion constraints at runtime. We then propose a novel task decomposition that can be applied to some indoor robotic tasks. This decomposition transforms a long time horizon task into a set of shorter tasks. We empirically demonstrate the performance gain provided by these two techniques through simulated execution in a variety of environments. Comparing a direct formulation of a POMDP to solving our proposed reductions, we conclude that the techniques proposed in this paper can provide significant enhancement to current POMDP solution techniques, extending the POMDP instances that can be solved to include large continuous-state robotic tasks. [ABSTRACT FROM PUBLISHER]

Advanced mobile robot systems need to accomplish increasingly complex task sets. However, to solve demanding problems, they are typically optimized to a very restricted set of tasks and environments. This work will therefore propose a self-reconfigurable software and hardware architecture to allow the dynamic optimization of a robot system depending on the current situation, i. e. the current task, the robot inner state, and the environment. The proposed framework is based on organic computing principles and unsupervised machine learning techniques. It further uses dynamically reconfigurable Field Programmable Gate Arrays (FPGA) as hardware accelerators. Preliminary results will be presented, which demonstrate the feasibility of the self-reconfiguration approach. [ABSTRACT FROM AUTHOR]

Robotic systems are complex, software intensive and heterogeneous composite systems. Software systems engineering and system integration is still a major challenge in robotics. We describe how component based software engineering (CBSE), model-driven software development (MDSD) and domain-specific languages (DSLs) for variability management complement each other in addressing the robotics software challenge. We outline how these approaches pave the way towards a software business ecosystem in robotics. We put a focus onto challenges still being considered as open and worth being addressed next. [ABSTRACT FROM AUTHOR]

Purpose – This paper aims to provide details of recent developments in random bin picking (RBP) technologies and products, together with an insight into its commercial status and prospects. Design/methodology/approach – Following an introduction to RBP, this article discusses the technology, benefits and limitations of RBP. It then considers a number of products and applications and concludes with a brief discussion. Findings – This article shows that RBP offers significant economic and operational benefits, but it is a complex technology and applications remain limited. It is underpinned by advanced machine vision and sophisticated image processing algorithms and continues to be the topic of academic research. Many RBP products have been launched in the past but the latest generation of dedicated vision systems, software packages and fully integrated robotic systems suggest that more widespread applications are imminent. Originality/value – This paper provides a timely introduction to the rapidly developing field of RBP by discussing the technologies and a range of products and applications. [ABSTRACT FROM AUTHOR]

Systems integration is a difficult matter particularly when its components are varied. The problem becomes even more difficult when such components are heterogeneous such as humans, robots and software systems. Currently, the humans are regarded as users of artificial systems (robots and software systems). This has several disadvantages such as: (1) incoherence of artificial systems exploitation where humans' roles are not clear and (2) vain research of a user's universal model. In this paper, we adopted a cooperative approach where the system's components are regarded as being of the same level and they cooperate for the service of the global system. We concretized such approach by considering humans, robots and software systems as autonomous agents assuming roles in an organization. The latter will be implemented as a multi-agent system developed using a multi-agent development methodology. [ABSTRACT FROM AUTHOR]

Abstract: This article presents a newly developed knowledge methodology/model that was designed to support the IEEE Robotics and Automation Society’s Ontologies for Robotics and Automation Working Group. This methodology/model allows for the creation of systems that demonstrate flexibility, agility, and the ability to be rapidly re-tasked. The methodology/model will be illustrated through a case study in the area of robotic kit building. Through this case study, the knowledge model will be presented, and automatic tools for optimizing the knowledge representation for planning systems and execution systems will be discussed. [Copyright &y& Elsevier]

Abstract: Robotics is an area of research in which the paradigm of Multi-Agent Systems (MAS) can prove to be highly useful. Multi-Agent Systems come in the form of cooperative robots in a team, sensor networks based on mobile robots, and robots in Intelligent Environments, to name but a few. However, the development of Multi-Agent Robotic Systems (MARS) still presents major challenges. Over the past decade, a high number of Robotics Software Frameworks (RSFs) have appeared which propose some solutions to the most recurrent problems in robotics. Some of these frameworks, such as ROS, YARP, OROCOS, ORCA, Open-RTM, and Open-RDK, possess certain characteristics and provide the basic infrastructure necessary for the development of MARS. The contribution of this work is the identification of such characteristics as well as the analysis of these frameworks in comparison with the general-purpose Multi-Agent System Frameworks (MASFs), such as JADE and Mobile-C. [Copyright &y& Elsevier]