Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

Recent industrial production paradigms have seen the promotion of the outsourcing of low-value-added operations to robotic cells as a service, particularly end-of-line packaging. As a result, various types of research have emerged, offering different approaches to the trajectory design optimization of robotic manipulators and their applications. Over time, numerous improvements and updates have been made to the proposed methodologies, addressing the limitations and restrictions of earlier work. This survey-type article compiles research articles published in recent years that focus on the main algorithms proposed for addressing placement and minimum-time path planning for a manipulator responsible for performing pick-and-place tasks. Specifically, the research examines the construction of an automated robotic cell for the palletizing of regular heterogeneous boxes on a collision-free mixed pallet. By reviewing and synthesizing the most recent research, this article sheds light on the state-of-the-art manipulator planning algorithms for pick-and-place tasks in palletizing applications.

Nosotros consideramos el problema de localización y ruteo de vehículos con flota heterogénea (LRPH) en el cual la meta es determinar los depósitos a ser abiertos, los clientes asignados a cada deposito, y las rutas que satisfagan la demanda de los clientes considerando una flota heterogénea. Nosotros proponemos una comparación de algoritmos granulares de Recocido Simulado (GSA), Búsqueda de Vecindario Variable (GVNS) y Tabú Search probabilístico (pGTS) para el LRPH. De esta manera, los algoritmos propuestos consideran un subconjunto del espacio en el cual los movimientos menos favorables son descartados según un factor de granularidad. Los algoritmos propuestos son comparados experimentalmente para la solución del LRPH, considerando el tiempo de CPU y la calidad de la solución obtenida en instancias adaptadas de la literatura. Los resultados computacionales muestran que el algoritmos GSA es capaz de obtener buenas soluciones en tiempos computacionales reducidos, mejorando los resultados obtenidos por los otros algoritmos propuestos.

The rapid growth of electric mobility is reshaping active distribution networks (ADNs), where electric vehicle charging stations (EVCS) introduce spatially concentrated, time-dependent, and highly simultaneous demand. This paper develops a network-oriented framework to evaluate EVCS integration in ADNs by coupling Colombian EV demand characterization, photovoltaic (PV) generation, battery energy storage system (BESS) operation, and AC power flow feasibility. The framework is applied to a 33-bus distribution feeder through four EVCS deployment cases and three support architectures: PV-only, PV–BESS colocated, and PV–BESS dispersed operation. The results show that non-coordinated EVCS deployment may increase losses, reduce voltage margins, and produce thermal overloads when feeder electrical sensitivity is ignored. They also reveal that optimized EVCS siting is insufficient under PV-only support, since PV generation lacks the controllability required to reshape feeder power flows during charging peaks. By contrast, BESS-assisted architectures substantially improve feeder operation, with dispersed storage achieving the best performance by decoupling charging demand locations from grid support locations. SOC and SOH analyses further demonstrate that storage feasibility and degradation must be assessed together with voltage, loading, and loss indicators. The proposed framework provides an operationally consistent basis for technically feasible EVCS planning in ADNs, linking local EV demand characterization, AC feasibility, support-architecture selection, and battery lifetime assessment.

Marker-less hand–eye calibration permits the acquisition of an accurate transformation between an optical sensor and a robot in unstructured environments. Single monocular cameras, despite their low cost and modest computation requirements, present difficulties for this purpose due to their incomplete correspondence of projected coordinates. In this work, we introduce a hand–eye calibration procedure based on the rotation representations inferred by an augmented autoencoder neural network. Learning-based models that attempt to directly regress the spatial transform of objects such as the links of robotic manipulators perform poorly in the orientation domain, but this can be overcome through the analysis of the latent space vectors constructed in the autoencoding process. This technique is computationally inexpensive and can be run in real time in markedly varied lighting and occlusion conditions. To evaluate the procedure, we use a color-depth camera and perform a registration step between the predicted and the captured point clouds to measure translation and orientation errors and compare the results to a baseline based on traditional checkerboard markers.

Dense depth completion is critical for 3D computer vision but remains challenging when depth data are sparse and misaligned with color images due to sensor offsets. We propose a fully convolutional neural network architecture that simultaneously performs depth completion and color image registration, effectively addressing the problem of sparse depth maps and misaligned RGB inputs. Our model is trained with a novel synthetic depth generation strategy that mimics real time-of-flight (ToF) sensor noise and occlusion artifacts, helping to bridge the simulation-to-real gap. In addition, we adopt a staged curriculum learning paradigm that progressively increases task complexity over three training phases, from easy alignment scenarios to full-depth completion with simulated sensor noise. By leveraging shared features between the depth and color tasks, the joint model outperforms separate single-task approaches. At the KITTI Depth Completion benchmark, the proposed approach achieves competitive accuracy while using significantly fewer parameters and achieving faster inference than existing methods, demonstrating its effectiveness and efficiency.