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.

This paper presents a real-time control strategy for the parallel robots such as the Stewart-Gough platform. The strategy is implemented using xPC Target (a Matlab toolbox), achieving sample rates higher than 1 kHz for the control loop. A modified explicit dynamic model, based on the virtual work principle, allows the formulation of dynamic control schemes, commonly used to control serial manipulators. Based on this approach, a computed torque control with friction compensation is implemented for a Stewart-Gough platform. Experimental results are compared with a classic uncoupled control strategy.

El aumento de unidades instaladas de robots industriales y los requerimientos de sostenibilidad exigen el estudio del consumo energético. Se propone una estrategia de reducción del consumo energético, basada en el diseño de una trayectoria punto a punto (PP). Se utiliza la optimización bayesiana que permite incluir información de un prototipo experimental en conjunto con un modelo matemático. Primero, se presenta el modelo cinético basado en el trabajo virtual y el problema de optimización bayesiana. Segundo, se realiza una comparación entre el consumo energético de trayectorias genéricas, métodos de optimización tradicionales, que utilizan trayectorias multi punto construidas por splines y trayectorias PP, y la optimización bayesiana propuesta, que utiliza una trayectoria PP. Se encuentra que en simulaciones computacionales los métodos tradicionales de optimización consiguen un consumo de energía menor que a través del método de optimización bayesiana. Sin embargo, a través de pruebas experimentales se verifica la ventaja del método de optimización bayesiana que, al incorporar datos reales del prototipo y dinámicas no modeladas, logran obtener un consumo energético menor.

Reducing the energy consumption of industrial robots performing pick-and-place tasks is required to increase profitability while reducing carbon footprint. Natural motion stands out as a mixed-energy-reduction strategy, especially useful for cyclical tasks. An optimization approach is proposed for calculating the elastic parameters, namely the stiffness and equilibrium position, of constant-stiffness springs parallel to the actuators of parallel robots. Three typical trajectory-dependent methods for calculating these parameters are presented: free-vibration response, optimized, and predefined trajectory. As the set of springs and the task specification are strongly coupled, deviations from the nominal task would require replacing or removing the springs. Therefore, two adjustment strategies, one based on trajectory optimization and the other on equilibrium position update, are proposed to further exploit the natural motion. All optimization problems are solved and compared in a case study of a five-bar linkage performing a nominal pick-and-place task. Then, a palletizing pick-and-place scenario is introduced to perform the proposed trajectory and equilibrium adjustments. It is shown that using nominal springs reduces energy consumption near the nominal task, and implementing the proposed adjustments reduces energy over a wider region.