Search Results (26)

In the domain of application of PN theory, the system deadlock problem of a flexible manufacturing system (FMS) is a thorny problem that needs to be solved urgently. All the research has the same objective of designing optimal controllers with maximal permissiveness and liveness. Plenty of the past literature used deadlock prevention as the main control strategy that is implemented by control places. However, these methods usually forbid undesirable system states from being reached, while reducing the system’s liveness. This study employed the resource flow graph (RFG)-based method to achieve a deadlock recovery policy that can maintain maximal permissiveness by adding control transitions (CTs). Also, we improved the current definition of RFG and developed a systematic approach for generating the corresponding RFG, which is based on flow mirroring pair (FMP) functions and the software Graphviz 12.2.1. Furthermore, this study proposed an automatic method that forms DOT script for generating Graphviz images, which is convincingly demonstrated in this study to enhance the execution efficiency and recognition of circular waiting situations. Full article

With the rapid development of the economy, urban road congestion has become more serious. The travel times for vehicles are becoming more uncontrollable, making it challenging to reach destinations on time. In order to find an optimal route and arrive at the destination with the shortest travel time, this paper proposes a dynamic shortest travel time path planning algorithm with an overtaking function (DSTTPP-OF) based on a vehicular ad hoc network (VANET) environment. Considering the uncertainty of driving vehicles, the target vehicle (vehicle for special tasks) is influenced by surrounding vehicles, leading to possible deadlock or congestion situations that extend travel time. Therefore, overtaking planning should be conducted through V2V communication, enabling surrounding vehicles to coordinate with the target vehicle to avoid deadlock and congestion through lane changing and overtaking. In the proposed DSTTPP-OF, vehicles may queue up at intersections, so we take into account the impact of traffic signals. We classify road segments into congested and non-congested sections, calculating travel times for each section separately. Subsequently, in front of each intersection, the improved Dijkstra algorithm is employed to find the shortest travel time path to the destination, and the overtaking function is used to prevent the target vehicle from entering a deadlocked state. The real-time traffic data essential for dynamic path planning were collected through a VANET of symmetrically deployed roadside units (RSUs) along the roadway. Finally, simulations were conducted using the SUMO simulator. Under different traffic flows, the proposed DSTTPP-OF demonstrates good performance; the target vehicle can travel smoothly without significant interruptions and experiences the fewest stops, thanks to the proposed algorithm. Compared to the shortest distance path planning (SDPP) algorithm, the travel time is reduced by approximately 36.9%, and the waiting time is reduced by about 83.2%. Compared to the dynamic minimum time path planning (DMTPP) algorithm, the travel time is reduced by around 18.2%, and the waiting time is reduced by approximately 65.6%. Full article

Industry 5.0 aims to integrate humans and machines to achieve greater productivity, personalization, and sustainable development in the production process. Built on the foundation of Industry 4.0 which emphasizes automation, digitalization, and intelligent production processes, Industry 5.0 highlights the importance of human resources in modern manufacturing. Robotic arms have replaced traditional manpower, particularly in flexible manufacturing systems. However, integrating advanced machinery into workflows has increased competition in terms of securing resources, resulting in frequent deadlocks. Various deadlock prevention policies have been proposed to address this issue. Despite these efforts, resolving system deadlocks while achieving the optimal number of reachable states remains challenging. Based on existing research, we have developed a novel deadlock recovery method applicable to various flexible manufacturing systems. We designed an adaptable system and a controller that can restore the system to its fully operational state. Full article

The deadlock control problem in flexible manufacturing systems (FMSs) has received much attention in recent years. The formalism of the Petri net is employed to effectively model, analyze, and control deadlocks in an FMS case study. There are many kinds of deadlock prevention strategies based on the Petri net approach, where computational complexity is a major problem that needs to be considered. Based on the Petri net theory, this paper focuses on the two special subclasses in the ${\mathrm{S}}^3$PR net, namely the dual-process ${\mathrm{S}}^3$PR and the dual-process ${\mathrm{US}}^3$PR, in a bid to prevent deadlocks in an FMS. The relationship between the net structural characteristics and the deadlocks reached was analyzed, and then a regular method of adding controllers for these two models was proposed to reduce computational complexity. Full article

This paper is devoted to Petri net (PN)-based models of automated manufacturing systems (AMSs) with resources in order to prevent deadlocks in them. Their sustainability can be seen as the result of their deadlock freeness, leading to correct and fluent production, because AMSs with deadlocks work neither correctly nor fluently, need reconstruction and cause downtime in production. The paradigm of such PN models, S³PRs (systems of simple sequential processes with resources), is well known from the deadlock prevention point of view. Here, an extended S³PR (ES3PR) will be explored, with respect to its modelling and deadlock prevention. While in the case of S³PRs, ordinary Petri nets (OPNs) are used for these aims, here, for ES³PRs, generalized Petri nets (GPNs) are used. The reason for such a procedure is the possible presence of multiplex-directed arcs in the structure of PN models of AMSs. The significant alternation is that while, in the former case, the elementary siphons and dependent ones are sufficient for supervisor synthesis, here, in the later case, the GPNs and their siphons have to satisfy the max cs property. Full article

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts. Full article

Controlling access to critical zones by drones is crucial for ensuring safety and efficient operations in various applications. In this research, we propose a strategy for controlling the access of a set of drones to a critical zone using timed automata and UPPAAL. UPPAAL is a model checker and simulator for real-time systems, which allows for the modeling, simulation, and verification of timed automata. Our system consists of six drones, a controller, and a buffer, all modeled as timed automata. We present a formal model capturing the behavior and interactions of these components, considering the constraints of allowing only one drone in the critical zone at a time. Timed automata are a powerful formalism for modeling and analyzing real-time systems, as they can capture the temporal aspects of system behavior. The advantages of using timed automata include the ability to model time-critical systems, analyze safety and liveness properties, and verify the correctness of the system. We design a strategy that involves signaling the approaching drones, preventing collisions, and ensuring orderly access to the critical zone. We utilize UPPAAL for simulating and verifying the system, including the evaluation of properties such as validation properties, safety properties, liveness properties, and absence of deadlocks. However, a limitation of timed automata is that they can become complex and difficult to model for large-scale systems, and the analysis can be computationally expensive as the number of components and behaviors increases. Through simulations and formal verification, we demonstrate the effectiveness and correctness of our proposed strategy. The results highlight the ability of timed automata and UPPAAL to provide reliable and rigorous analysis of drone access control systems. Our research contributes to the development of robust and safe strategies for managing drone operations in critical zones. Full article

Aiming to resolve the problems of slow convergence speed and inability to plan in real time when ant colony optimization (ACO) performs global path planning, we propose a path-planning method that improves adaptive ant colony optimization (IAACO) with the dynamic window approach (DWA). Firstly, the heuristic information function is modified, and the adaptive adjustment factor is added to speed up the algorithm’s convergence rate; secondly, elite ants and max–min ants systems are implemented to enhance the global pheromone updating process, and an adaptive pheromone volatilization factor is aimed at preventing the algorithm from enhancing its global search capabilities; then, the path optimization and withdrawal mechanism is utilized to enable smoother functioning and to avoid the deadlocks; finally, a new distance function is introduced in the evaluation function of DWA to the enhance real-time obstacle-avoidance ability. The simulation experiment results reveal that the path length of the IAACO can be shortened by 10.1% and 13.7% in contrast to the ACO. The iteration count can be decreased by 63.3% and 63.0%, respectively, leading to an enhanced optimization performance in global path planning and achieving dynamic real-time obstacle avoidance for local path planning. Full article

With the implementation of AGV technology and automated scheduling, storage and retrieval systems have become widely utilized in warehouse management. However, due to the use of unidirectional channels, AGV movement is restricted, and detours may occur frequently. Additionally, as the number of AGVs increases, deadlocks can arise, which lead to delays in order packaging and a decrease in overall warehouse performance. Hence, this paper proposes a dynamic scheduling method for task assignment and route optimization of AGVs to prevent collisions. The routing optimization method is based on an improved A* algorithm, which takes into account the dynamic map as input. Moreover, this paper investigates highly complex collision scenarios in bidirectional channels. Through simulation experiments, it is evident that scheduling methods based on bidirectional channels offer a clear advantage in terms of efficiency compared to those based on unidirectional channels. Full article

After the fourth industrial evolution, precision and automatic manufacturing have become increasingly widely accepted in production. With highly variable productivity and flexibility, flexible manufacturing systems (FMS) lower production costs and increase efficiency. Due to its resource shareability, unexpected system deadlock may occur in some specific situations. Many existing works use deadlock prevention as the primary control methodology in research on system deadlock control, while this type of control policy would constrain the transportation resources and reduce the system’s liveness. This paper adopts a new transition-based deadlock recovery policy as the direct control strategy, which uses generating and comparing aiding matrix (GCAM) to determine the optimal control transition. We also improve the existing GCAM-based method by reducing the computational redundancy. This kind of control strategy and its benefit could be demonstrated through two typical systems of simple sequential processes with resource (S³PR) nets and their Petri nets model. Full article

In the flexible manufacturing system deadlock prevention domain, researchers’ almost final target is to seek the maximally permissive controllers for solving the deadlock problems of flexible manufacturing systems. However, it seems a challenging work. Whatever you adopt, what kinds of methods, policies, and strategies, it seems complicated to obtain optimal controllers for deadlock prevention even if they claim their algorithms are optimal until the deadlock recovery is developed. Therefore, many experts, including us, have decided to design all kinds of actual maximally permissive recovery policies based on control transitions. It is a pity that these policies usually solve some particular flexible manufacturing systems’ deadlock problems. The controllers failed to recover the deadlock situation of flexible manufacturing systems once the idle or resource places were changed. In other words, these policies could never know or identify the real number of maximally permissive controllers in the same structure with different idle places. The precise combination of the number of idle places is called the saturated line, and it divides the reachability states into two region (saturated region and unsaturated region). Accordingly, this paper proposes a novel concept that achieves the precise maximum permissiveness and fits all kinds of flexible manufacturing systems. The experimental results show how our policy does in practice. Full article

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning. Full article

Rapid shifts in consumer preferences have prompted enterprises to offer products in small quantities and various options. To meet market demands, enterprises must be able to research the development of modern conceptions of manufacturing systems which has revolved around new practical and scientific results that are able to meet the assumptions of focused flexible manufacturing systems (FMSs) and the challenges of the Industry 4.0 philosophy. These FMSs, which incorporate automated facilities and computer control systems, play a crucial role in boosting the productivity of enterprises. In this study, the development trajectory and applications of FMS research were investigated. Scopus was used to collect and organize voluminous data, and main path analysis was used to identify the most relevant studies on FMS research. The results revealed that early FMS research concentrated on fundamental property analysis. After the flexibility and productivity of these systems were enhanced, the elimination of loading problems was discussed. Generally, FMS research has emphasized factor identification, flexibility evaluation, pre-simulation, and optimization. In this study, cluster analysis was used to identify five subfields: loading problem mitigation through scheduling, decision-making facilitation through simulation, FMS deadlock prevention, FMS flexibility measurement, and FMS composition. This study provides planning directions for industry, and the findings serve as a reference for manufacturing systems. The integrated analysis successfully determined the trajectory of FMS based technological development and applications as well as forecast the direction of future research. Full article

The importance of an external interaction interface (eHMI) has grown in recent years. Most eHMI concepts focus on communicating autonomous vehicle (AV)’s yielding intention to pedestrians at a crossing. However, according to previous studies, pedestrians at a crossing rely mainly on the vehicle’s movement information (implicit communication) rather than information from eHMIs (explicit communication). This paper has the purpose of proposing a specific use case in which the eHMI of future AVs could play an indispensable role in the safety of other road users (ORUs). Often VRUs cannot see the traffic flow due to a series of parked or stopped vehicles, which is a frequent cause of fatal traffic collision accidents. Drivers may also not be able to see approaching pedestrians or other cars from the side for the same reason. In this paper, the impact of an eHMI is tested from the perspective of drivers with limited visibility when a jaywalker steps into the road. A combination of colors, shapes, and information levels is presented on an eHMI. We show that our proposed eHMI design, in the deadlock scenario of a jaywalker and a driver who both lack visibility, significantly reduced the reaction time compared to when there was no eHMI. In the experiment, the willingness to stop, varying from 0 to 5, was measured from the driver’s perspective. The results showed that most users felt uncertainty and did not move quickly when seeing the light band color alone. Textual information on the eHMI was significantly more effective in providing an urgent warning of this specific scenario than vertical and horizontal light bands with color without text. In addition, red color, blinking rapidly above 3 Hz, and egocentric messages were also necessary to reduce the PRT(perception response time). By using text-added eHMI (Vertical + Text eHMI), the mean time to achieve a score above 4 for willingness to stop was 2.113 s faster than when there was no eHMI. It was 2.571 s faster than the time until the slider of the participants reached the maximum level for willingness to stop. This is a meaningful amount of difference when considering a PRT of 2.5 s, which is the Korean road design standard. As eHMIs tend to be applied for smarter mobility, it is expected that they will be more effective in preventing accidents if the eHMI is standardized in autonomous driving level 2 to 3 vehicles driven by humans before fully autonomous driving becomes a reality. Full article

Automated Manufacturing Systems (AMS) consisting of many cooperating devices incorporated into multiple cooperating production lines, sharing common resources, represent industrial Multi-Agent Systems (MAS). Deadlocks may occur during operation of such MAS. It is necessary to deal with deadlocks (more precisely said, to prevent them) to ensure the correct behavior of AMS. For this purpose, among other methods, methods based on Petri nets (PN) are used too. Because AMS are very often described by PN models, two PN-based methods will be presented here, namely based on (i) PN place invariants (P-invariants); and (ii) PN siphons and traps. Intended final results of usage these methods is finding a supervisor allowing a deadlock-free activity of the global MAS. While the former method yields results in analytical terms, latter one need computation of siphons and traps. Full article