In smart manufacturing workshops, automated guided vehicles (AGVs) are increasingly used to transport materials required for machine tools. This paper studies the AGV path planning problem of a ...one-line production line in the workshop, establishes a mathematical model with the shortest transportation time as the objective function, and proposes an improved particle swarm optimization(IPSO) algorithm to obtain an optimal path. In order to be suitable for solving the path planning problem, we propose a new coding method based on this algorithm, design a crossover operation to update the particle position, and adopt a mutation mechanism to avoid the algorithm from falling into the local optimum. By calculating the shortest transportation time obtained, the improved algorithm is compared with other intelligent optimization algorithms. The experimental results show that the algorithm can improve the efficiency of AGV in material transportation and verify the effectiveness of related improvement mechanisms.
This paper studies an unequal area layout problem whose objective is to find a flexible bay layout with the best shortest single loop. The problem can be used for production systems in which material ...handling is carried out by automated guided vehicles (AGVs) that move along a single-loop path. The designed layout is robust against future changes in material flows, which are caused by different factors such as demand fluctuations, technology advancement, and new product introduction. It is first proved that there exists a feasible single loop for any bay layout, which indicates the problem is well-defined and always has a feasible solution. Then, a powerful hybrid algorithm based on Memetic and Simulated Annealing is developed. The algorithm is tested on many randomly generated instances and well-known instances from the literature. The numerical experiment shows that the proposed heuristic is both efficient and effective. The new layout-design approach significantly improves the loop-length when compared with the traditional approach in which the layout is first determined based on rectilinear distances, and then the best single loop is found. Our study highlights the importance of adequately incorporating the material handling system into the layout design phase.
Abstract
This paper proposes a method for automatic adjustment of PID(Proportion Integration Differentiation) based on deep reinforcement learning in order to solve the problem of smooth movement of ...AGV(Automated Guided Vehicle). First, based on reinforcement learning, the problem of AGV smooth operation is transformed into the solution of PID adjustment operation sequence. The action value model is constructed using the Deep Q-Learning Network(DQN) algorithm. Then, take the AGV adjustment PID as the research object. The goal is to achieve smooth movement of AGV. The specific design of AGV automatic PID adjustment method based on deep reinforcement learning is introduced. Finally, the simulation data is used to train the AGV action value network model. The method is validated in the ROS(Robot Operating System) simulation environment. Then compare with the actual environment. The results show that the automatic adjustment method of AGV’s PID based on deep reinforcement learning can adjust PID to the optimum without manual work. It can make AGV run smoothly. The feasibility and effectiveness of the method are illustrated and the problem of automatic PID determination in AGV movement control is solved.
The position or movement control of an automated guided vehicle (AGV) is crucial for its operation. However, choosing the AGV position sensor is not a trivial task. This paper investigates the ...sensors and sensing techniques in machine vision applied in AGV position control in the past 5 years of published academic research. Using a systematic literature review method, we seek to answer the main research question: which sensors and sensing techniques are used in indoor AGV positioning control problems according to the past 5 years of published research and their technological impact. In doing so, we address three sub-question: (i) is the sensor/sensing technique related to the AGV application area; (ii) is the sensor/sensing technique applied to the problem related to the control strategy and/or the AGV guide; (iii) is the sensor/sensing technique related to the required AGV accuracy/sensitivity level. The paper contributions are the application of a systematic method of literature review in AGV position sensors, the research area overview from the selected 31 articles of the past 5 years, and a research agenda proposal.
With a growth tendency, the employment of the Adaptive Monte Carlo Localization (AMCL) Robot Operational System (ROS) package does not reflect a more in-depth discussion on its parameters’ tuning ...process. The authors usually do not describe it. This work aims to extend the analysis of the package’s parameters’ distinct influence in an automated guided vehicle (AGV) indoor localization. The experiments test parameters of the filter, the laser model, and the odometry model. Extending the previous analysis of seven parameters, the present research discusses another ten from the 22 configurable parameters of the package. An external visual vehicle pose tracking is used to compare the pose estimation from the localization package. Although the article does not propose the best parameter tuning, its results discuss how each tested parameter affects the localization. The paper’s contribution is discussing the parameters’ variation impact on the AGV localization using the covariance matrix results. It may help new researchers in the AMCL ROS package parameter tuning process. The results show minor changes in the default parameters which can improve the localization results, even modifying one parameter at a time.
This paper deals with
machines, automated guided vehicles and tools simultaneous scheduling in multi-machine flexible manufacturing system considering jobs' transport times among machines to minimize ...makespan. Only one copy of every tool type is made available due to economic restrictions.
The tools are stored in central tool magazine that shares with and serves for several machines. This simultaneous scheduling problem is highly complex in nature as it involves the allocation of automated guided vehicles and tools to job-operations, job-operations sequencing on machines, and associated trip operations including the dead heading trip and loaded trip times of automated guided vehicles. This paper proposes symbiotic organisms search algorithm for solving this problem. An industrial problem in a manufacturing company is used for verification. The results show that SOSA is providing better solutions than Jaya algorithm, and the obtained schedule can be implemented practically with reduction in cost.
Based on automated guided vehicle (AGV), the intelligent parking system provides a novel solution to the difficulty of parking in large cities. The automation of parking/pick-up in the system hinges ...on the path planning efficiency of the AGV. Considering the numerous disconnected paths in intelligent parking systems, this paper introduces the fallback strategy to improve ant colony optimization (ACO) for path planning in AGV-based intelligent parking system. Meanwhile, the valuation function was adopted to optimize the calculation process of the heuristic information, and the reward/penalty mechanism was employed to the pheromone update strategy. In this way, the improved ACO could plan the optimal path for the AGV from the starting point to the destination, without sacrificing the search efficiency. Next, the optimal combination of ACO parameters was identified through repeated simulations. Finally, a typical parking lot was abstracted into a topological map, and used to compare the path planning results between the improved ACO and the classic ACO. The comparison confirms the effectiveness of the improved ACO in path planning for AGV-based intelligent parking system.
The content of this work is the development of a localization system, which enables several driverless transport vehicles to navigate locally in a highly flexible and free way. The ceiling mounted ...sensor system detects and analyzes objects within the field of view. The detected properties of all objects are made available via an interface and enable vehicles to identify themselves and navigate freely without a sensor mounted on the vehicle.
•Inclusion of human factors in the design of Human-Robot collaborative interfaces•UX-based approach to include human factors in human-robot interface design and practically support user-centred ...design•Set of UX specific tools to collect user requirements and support the design of human-robot interfaces•Validation on an industrial case study focusing on collaborative assembly
In recent years Human-Robot Collaboration (HRC) has become a strategic research field, considering the emergent need for common collaborative execution of manufacturing tasks, shared between humans and robots within the modern factories. However, the majority of the research focuses on the technological aspects and enabling technologies, mainly directing to the robotic side, and usually neglecting the human factors. This work deals with including the needs of the humans interacting with robots in the design in human-robot interaction (HRI). In particular, the paper proposes a user experience (UX)-oriented structured method to investigate the human-robot dialogue to map the interaction with robots during the execution of shared tasks, and to finally elicit the requirements for the design of valuable HRI. The research adopted the proposed method to an industrial case focused on assembly operations supported by collaborative robots and AGVs (Automated Guided Vehicles). A multidisciplinary team was created to map the HRI for the specific case with the final aim to define the requirements for the design of the system interfaces. The novelty of the proposed approach is the inclusion of typically interaction design tools focusing in the analysis of the UX into the design of the system components, without merely focusing on the technological issues. Experimental results highlighted the validity of the proposed method to identify the interaction needs and to drive the interface design.
•The multiobjective optimization model of dynamic flexible job shop scheduling problem with insufficient transportation resources (DFJSP-ITR) is established to minimize the makespan and total energy ...consumption.•A hybrid deep Q network (HDQN) is developed for DFJSP-ITR to make agent learn to select the appropriate rule according to the production state at each decision point, which has three extensions to deep Q network: double Q-learning, prioritized replay and soft target network update policy.•To implement the DRL-based scheduling, the shop floor state model is established at first, and then the decision point, 26 generic state features, genetic-programming-based action space and reward function are designed. Based on the above contents, the training method using HDQN and the strategy for facing new job insertions and machine breakdowns are proposed.•Experimental results show that HDQN has superiority and generality compared with current optimization-based approaches, and can effectively deal with disturbance events and unseen situations through learning.
With the extensive application of automated guided vehicles in manufacturing system, production scheduling considering limited transportation resources becomes a difficult problem. At the same time, the real manufacturing system is prone to various disturbance events, which increase the complexity and uncertainty of shop floor. To this end, this paper addresses the dynamic flexible job shop scheduling problem with insufficient transportation resources (DFJSP-ITR) to minimize the makespan and total energy consumption. As a sequential decision-making problem, DFJSP-ITR can be modeled as a Markov decision process where the agent should determine the scheduling object and allocation of resources at each decision point. So this paper adopts deep reinforcement learning to solve DFJSP-ITR. In this paper, the multiobjective optimization model of DFJSP-ITR is established. Then, in order to make agent learn to choose the appropriate rule based on the production state at each decision point, a hybrid deep Q network (HDQN) is developed for this problem, which combines deep Q network with three extensions. Moreover, the shop floor state model is established at first, and then the decision point, generic state features, genetic-programming-based action space and reward function are designed. Based on these contents, the training method using HDQN and the strategy for facing new job insertions and machine breakdowns are proposed. Finally, comprehensive experiments are conducted, and the results show that HDQN has superiority and generality compared with current optimization-based approaches, and can effectively deal with disturbance events and unseen situations through learning.
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