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.

Parcel sorting is becoming a significant challenge for delivery distribution centers and is mostly automated by using high-throughput sorting machinery, but manual work is still used to feed these machines by placing the parcels on the conveyor belt. In this paper, an AI-based robotic solution that automates the parcel placement task was developed. The architecture of the proposed system along with methods on how to implement it are described by using the currently available hardware and software components. The described choices lead to a well-functioning system and the gained insights will facilitate building similar systems for parcel delivery automation.

This research article outlines the design and methodology employed in the development of a vision-based micro-manipulation system, emphasizing its constituent components. While the system is initially tailored for applications involving living cells, its adaptability to other objects is highlighted. The integral components include an image enhancement module for data preparation, an object detector trained on the pre-processed data, and a precision micro-manipulator for actuating towards detected objects. Each component undergoes rigorous precision testing, revealing that the proposed image enhancement, when combined with the object detector, outperforms conventional methods. Additionally, the micro-manipulator shows excellent results for working with living cells the size of yeast. In the end, the components are also tested in a combined system as a proof-of-concept.

This paper outlines a conceptual design for a multi-level natural language-based planning system and describes a demonstrator. The main goal of the demonstrator is to serve as a proof-of-concept by accomplishing end-to-end execution in a real-world environment, and showing a novel way of interfacing an LLM-based planner with open-set semantic maps. The target use-case is executing sequences of tabletop pick-and-place operations using an industrial robot arm and RGB-D camera. The demonstrator processes unstructured user prompts, produces high-level action plans, queries a map for object positions and grasp poses using open-set semantics, then uses the resulting outputs to parametrize and execute a sequence of action primitives. In this paper, the overall system structure, high-level planning using language models, low-level planning through action and motion primitives, as well as the implementation of two different environment modeling schemes—2.5 or fully 3-dimensional—are described in detail. The impacts of quantizing image embeddings on object recall are assessed and high-level planner performance is evaluated using a small reference scene data set. We observe that, for the simple constrained test command data set, the high-level planner is able to achieve a total success rate of 96.40%, while the semantic maps exhibit maximum recall rates of 94.69% and 92.29% for the 2.5d and 3d versions, respectively.