The current industrial scenario demands advances that depend on expensive and sophisticated solutions. Augmented Reality (AR) can complement, with virtual elements, the real world. Faced with this features, an AR experience can meet the demand for prototype testing and new solutions, predicting problems and failures that may only exist in real situations. This work presents an environment for experimentation of advanced behaviors in smart factories, allowing experimentation with multi-robot systems (MRS), interconnected, cooperative, and interacting with virtual elements. The concept of ARENA introduces a novel approach to realistic and immersive experimentation in industrial environments, aiming to evaluate new technologies aligned with the Industry 4.0. The proposed method consists of a small-scale warehouse, inspired in a real scenario characterized in this paper, managing by a group of autonomous forklifts, fully interconnected, which are embodied by a swarm of tiny robots developed and prepared to operate in the small scale scenario. The AR is employed to enhance the capabilities of swarm robots, allowing box handling and virtual forklifts. Virtual laser range finders (LRF) are specially designed as segmentation of a global RGB-D camera, to improve robot perception, allowing obstacle avoidance and environment mapping. This infrastructure enables the evaluation of new strategies to improve manufacturing productivity, without compromising the production by automation faults.
Smart factories are introducing new technologies to improve production and expand flexibility, which denotes the integration of intelligent, autonomous, and interconnected agents. The conceptual transition to dynamic multiple agents generates some dilemmas, mainly regarding the occurrence of unexpected situations. This paper aims to discuss the collective behavior of multi-agent systems in smart factories for achieving fault resilience. The proposed approach is based on three hierarchical plans: imposition, negotiation, and consensus. Fault restoration is achieved through the collective behavior that manages the ternary decisions made in these plans. The approach can help the smart factories that employ autonomous multi-agents improve their production, reliability, and robustness to failure. The proposed method was evaluated using a virtual warehouse logistics but employing real scenarios. Experiments were performed through logistic tasks to prove the collective behavior implemented in the approach for fault resilience. Quantitative analysis of the experiments shows the efficiency of the approach under various situations.
This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Preemptive Task Scheduling with Fault Recovery (MRPF). It considers the interaction between running processes and their tasks for management at each new event, prioritizing the more relevant tasks without idleness and latency. The benefit of this approach is the optimization of production in smart factories, where autonomous robots are being employed to improve efficiency and increase flexibility. The evaluation of MRPF is performed through experimentation in small-scale warehouse logistics, referred to as Augmented Reality to Enhanced Experimentation in Smart Warehouses (ARENA). An analysis of priority scheduling, task preemption, and fault recovery is presented to show the benefits of the proposed approach.
This paper presents a novel approach for creating virtual LiDAR scanners through the active segmentation of point clouds. The method employs top-view point cloud segmentation in virtual LiDAR sensors that can be applied to the intelligent behavior of autonomous agents. Segmentation is correlated with the visual tracking of the agent for localization in the environment and point cloud. Virtual LiDAR sensors with different characteristics and positions can then be generated. This method is referred to as the DepthLiDAR approach, and is rigorously evaluated to quantify its performance and determine its advantages and limitations. An extensive set of experiments is conducted using real and virtual LiDAR sensors to compare both approaches. The objective is to propose a novel method to incorporate spatial perception in warehouses, aiming to achieve Industry 4.0. Thus, it is tested in a low-scale warehouse to incorporate realistic features. The analysis of the experiments shows a measurement improvement of 52.24% compared to the conventional LiDAR.
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