The precise localization of mobile robots in unstructured environments is of utmost importance for many industrial and field applications, especially when the mobile robot is part of a more complex kinematic chain, such as a mobile manipulator. Being able to precisely localize affects the outcome of tasks that rely on an open-loop kinematic computation, such as work-station docking procedures. To achieve a repeatable and precise localization and positioning, mobile robots generally rely on onboard sensors, most commonly 2D laser scanners, whose readings are subjected to noise and numerous disturbing factors (e.g., materials reluctance). Problems arise when precise localization is needed in dynamic and unstructured environments where generally applicable methods won’t perform adequately or might be time-consuming to set up. In this work, we propose a cloud-edge computing architecture to deploy a recurrent neural network (RNN) based registration system, which uses a pair of consecutive LiDAR readings to estimate a fixed transformation. The capability of RNNs to process contiguous inputs will help neglect errors embedded in punctual laser scanner readings and output a more precise registration estimation. In such a way, the RNN can estimate a displacement error based on multiple consecutive readings and act as a sensor to be employed in a closed-loop control scheme. To tackle the dynamic and unstructured environments, the model is firstly tuned on synthetic LiDAR data to embed rigid transformations into the deep learning model, for then rapidly fine-tuned on local scenarios. After model architecture and optimization of hyperparameters, the devised model is tested in different scenarios, comparing the precise positioning capability of the AMR(autonomous mobile robot) with that of a classical registration algorithm. The results suggest that an RNN model can greatly improve the registration precision of laser scanner signals and, consequently, the precise positioning efficiency of AMRs.

Modern hybrid robot cells leverage heterogeneous agents to provide agile production solutions. Effective agent coordination is crucial to avoid inefficiencies and potential hazards for human operators working among robots. This paper proposes a new human-aware task allocation and scheduling model based on Mixed Integer Non-Linear Programming (MINLP) to optimize efficiency and safety during task planning, scheduling, and allocation. The approach introduces a synergy index that encodes the coupling effects between pairs of tasks executed in parallel by the agents. These terms are learned from previous process executions by means of a Bayesian linear estimation. The task planning model is enhanced by the knowledge of synergy terms to adapt the nominal duration of the plan to consider the effect of the operator's presence. Simulations and experimental results demonstrate the effectiveness of the proposed method in obtaining a proactive human-aware solution starting from the task planning level. The proposed model reduces process execution time and achieves solutions with less agent interference, more considerable human-robot distance, and, thus, safer for agents.

The precise localization of mobile robots is of utmost importance for many industrial applications, especially when the mobile robot is part of a more complex kinematic chain, such as in a mobile manipulator. Furthermore, precise localization hugely affects the outcome of tasks that rely on an open-loop kinematic computation, such as work-station docking procedures. To achieve a repeatable and precise localization and positioning, mobile robots generally rely on onboard sensors, most commonly 2D laser scanners, whose readings are subjected to noise and numerous disturbing factors ( e.g., materials reluctance). In this work, we propose a recurrent neural network (RNN) based registration system, which uses a pair of consecutive LiDAR readings and estimates a fixed transformation. The capability of RNNs to process contiguous inputs will help neglect errors embedded in punctual laser scanner reading and output a more precise registration estimation. In such a way, the RNN can estimate a displacement error based on multiple consecutive readings and act as a sensor to be employed in a closed-loop control scheme. After a model architecture and optimization of hyperparameters, the devised model is tested in different scenarios, comparing the AMR precise positioning capability with a classical registration algorithm. The results suggest that an RNN model can greatly improve the registration precision of laser scanner signals and, consequently, the precise positioning efficiency of AMRs.