Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots have arisen. AI-based robotic systems are strongly becoming one of the main areas of focus, as flexibility and deep understanding of complex manufacturing processes are becoming the key advantage to raise competitiveness. This review first expresses the significance of smart industrial robot control in manufacturing towards future factories by listing the needs, requirements and introducing the envisioned concept of smart industrial robots. Secondly, the current trends that are based on different learning strategies and methods are explored. Current computer-vision, deep reinforcement learning and imitation learning based robot control approaches and possible applications in manufacturing are investigated. Gaps, challenges, limitations and open issues are identified along the way.

Smart manufacturing and smart factories depend on automation and robotics, whereas human–robot collaboration (HRC) contributes to increasing the effectiveness and productivity of today’s and future factories. Industrial robots especially in HRC settings can be hazardous if safety is not addressed properly. In this review, we look at the collaboration levels of HRC and what safety actions have been used to address safety. One hundred and ninety-three articles were identified from which, after screening and eligibility stages, 46 articles were used for the extraction stage. Predefined parameters such as: devices, algorithms, collaboration level, safety action, and standards used for HRC were extracted. Despite close human and robot collaboration, 25% of all reviewed studies did not use any safety actions, and more than 50% did not use any standard to address safety issues. This review shows HRC trends and what kind of functionalities are lacking in today’s HRC systems. HRC systems can be a tremendously complex process; therefore, proper safety mechanisms must be addressed at an early stage of development.

In this paper, we address the most pressing challenges faced by the manufacturing sector, particularly the manufacturing of small and medium-sized enterprises (SMEs), where the transition towards high-mix low-volume production and the availability of cost-effective solutions are crucial. To overcome these challenges, this paper presents 14 innovative solutions that can be utilized to support the introduction of agile manufacturing processes in SMEs. These solutions encompass a wide range of key technologies, including reconfigurable fixtures, low-cost automation for printed circuit board (PCB) assembly, computer-vision-based control, wireless sensor networks (WSNs) simulations, predictive maintenance based on Internet of Things (IoT), virtualization for operator training, intuitive robot programming using virtual reality (VR), autonomous trajectory generation, programming by demonstration for force-based tasks, on-line task allocation in human–robot collaboration (HRC), projector-based graphical user interface (GUI) for HRC, human safety in collaborative work cells, and integration of automated ground vehicles for intralogistics. All of these solutions were designed with the purpose of increasing agility in the manufacturing sector. They are designed to enable flexible and modular manufacturing systems that are easy to integrate and use while remaining cost-effective for SMEs. As such, they have a high potential to be implemented in the manufacturing industry. They can be used as standalone modules or combined to solve a more complicated task, and contribute to enhancing the agility, efficiency, and competitiveness of manufacturing companies. With their application tested in industrially relevant environments, the proposed solutions strive to ensure practical implementation and real-world impact. While this paper presents these solutions and gives an overview of their methodologies and evaluations, it does not go into their details. It provides summaries of comprehensive and multifaceted solutions to tackle the evolving needs and demands of the manufacturing sector, empowering SMEs to thrive in a dynamic and competitive market landscape.

Imitation learning is a discipline of machine learning primarily concerned with replicating observed behavior of agents known to perform well on a given task, collected in demonstration data sets. In this paper, we set out to introduce a pipeline for collecting demonstrations and training models that can produce motion plans for industrial robots. Object throwing is defined as the motivating use case. Multiple input data modalities are surveyed, and motion capture is selected as the most practicable. Two model architectures operating autoregressively are examined—feedforward and recurrent neural networks. Trained models execute throws on a real robot successfully, and a battery of quantitative evaluation metrics is proposed. Recurrent neural networks outperform feedforward ones in most respects, but this advantage is not universal or conclusive. The data collection, pre-processing and model training aspects of our proposed approach show promise, but further work is required in developing Cartesian motion planning tools before it is applicable in production applications.

This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms.

Mapping the environment is a powerful technique for enabling autonomy through localization and planning in robotics. This article seeks to provide a global overview of actionable map construction in robotics, outlining the basic problems, introducing techniques for overcoming them, and directing the reader toward established research covering these problem and solution domains in more detail. Multiple levels of abstraction are covered in a non-exhaustive vertical slice, starting with the fundamental problem of constructing metric occupancy grids with Simultaneous Mapping and Localization techniques. On top of these, topological meshes and semantic maps are reviewed, and a comparison is drawn between multiple representation formats. Furthermore, the datasets and metrics used in performance benchmarks are discussed, as are the challenges faced in some domains that deviate from typical laboratory conditions. Finally, recent advances in robot control without explicit map construction are touched upon.