Energy-efficient path planning for a multi-load automated guided vehicle executing multiple transport tasks in a manufacturing workshop environment.

Flexible time–space network formulation and hybrid metaheuristic for conflict-free and energy-efficient path planning of automated guided vehicles

Aiming at the problems of poor flexibility, complicated path maintenance and poor positioning performance in the current guidance technology of AGV, this paper designs and implements a new navigation system of automated guided vehicle (AGV) based on AprilTags2 auxiliary positioning. The system uses the Robot Operating System (ROS) as a platform to develop AGV navigation functions. The navigation system comprises two parts: the hardware and software layers. Firstly, hardware selection is performed after considering the actual requirements, performance, cost, and other factors in the hardware layer. Simultaneously, the AGV chassis and single-steering wheel walking mechanism are built to provide a stable and flexible operating platform for the software layer. Secondly, the software layer design includes two parts, namely the ROS navigation planning end and AprilTags2 detection. The ROS planning navigation end performs the design of four functional modules (i.e., AGV map construction, autonomous positioning, path planning, and path tracking), while the AprilTags2 detection part obtains the visual positioning pose of AGV by setting the AprilTags2 on each site and using the Kinect1 camera to detect. A more accurate AGV pose can be obtained by merging the former pose with the kinematic estimated pose. Finally, the two groups of positioning errors of the system before and after adding the AprilTags2 auxiliary positioning are tested and compared. The experimental results show that the navigation accuracy of the proposed system with AprilTags2 auxiliary positioning is significantly improved.

In order to solve the path planning problem of Automated Guided Vehicle (AGV) transporting goods and packages in warehouse logistics, this paper studies the Algorithm of the path planning problem, an improved adaptive genetic algorithm is presented to solve the path optimization problem. In order to avoid falling into local optimum, an improved self-adaptive crossover method is proposed. In order to avoid the conflict of AGV in the process of path planning, the concept of Congestion Coefficient is introduced into the design of fitness function, reduce the AGV in the path optimization process of conflict. The mathematical model of AGV to accomplish multi-task is established, and the comparative experiment is set up. The experimental results show that the improved adaptive genetic algorithm can improve the optimization performance by 4.2% in solving the optimal path problem, and improve the convergence speed by 4.2%, the improved algorithm has obvious advantages.

Aiming at navigation safety and energy consumption, a novel energy-efficient path planning and path following control approach in a complex sea environment is proposed based on particle swarm optimization algorithm. Firstly, the mathematical model of the USV movement and the mathematical model of the marine environment are established. Secondly, the electronic chart information and environmental information are extracted and converted into the data required for path planning, and then the data for the path planning is modeled. Thirdly, in terms of static obstacles in the navigation area, combined with the sea environmental conditions, the particle swarm optimization algorithm is used to obtain an energy-efficient path through proper objective function and constraints. The route is re-planed and adjusted in real time with the weather conditions change. Fourthly, the generated energy-efficient path is followed by the PID control method with the optimal control parameter by using particle swarm optimization. Finally, the effectiveness of the proposed algorithm is proved to improve the safety and energy economy of the USV through a series of simulation experiment.

Optimization configuration model and application of product service system based on low-carbon design

In order to solve the problem that the traditional automated guided vehicle (AGV) path planning algorithm has slow convergence speed and is easy to fall into local optimal solution, an improved D*lite algorithm is being presented by combining Dijkstra algorithm and D*lite algorithm. Firstly, the topological map is generated with grid graph. Then, AGV path planning is divided into two stages: pre-planning and real-time planning. In the pre-planning stage, the local shortest path can be achieved through forward searching with Dijkstra algorithm from start vertex to the goal vertex. In the real-time planning stage, the total shortest path can be computed through backward searching with improved D*lite algorithm which searches from the goal vertex to the start vertex and uses heuristics to focus the search, and uses similar ways to minimize having to reorder the priority queue. Finally, the simulation results indicate that the convergence speed and path distance of AGV are optimized by using the improved D*lite algorithm.KeywordsAGVImproved D*lite algorithmPath planningSimulation

This paper addresses the problem of mapping and localization for optically guided Automated Guided Vehicles (AGV) that are widely needed in the warehousing industry. It describes an approach to build a floor plan of the optical path traversed by the AGV, while keeping the process cost-effective by using the AGVs internal sensors and the optical sensor used to keep track of the path. The idea is to create a distributed system where each AGV knows its location in the warehouse and is capable of calculating an optimal path when given a destination without having to map the environment prior. This is done through fusion of maps from multiple AGV. Each AGV creates its own map and communicates it to the mapping module which collects maps from all the AGV in the system and creates a fused map. Such a map needs to be robust against errors and noise from multiple AGV.

Intelligent logistics is an important part of intelligent manufacturing, and Automated Guided vehicle (AGV), as a part of seamless enterprise production system and storage system, is an important technical equipment to achieve intelligent logistics, and has been widely used in factories, warehousing and logistics environment such as automatic handling scenarios. Since a single AGV in the same site can no longer meet the trend of automated factory, it is important to effectively schedule and plan paths for multiple AGVs in limited space. Aiming at multi-AGV system, this paper analyzes and organizes the common path planning algorithms in the market, in order to improve the effectiveness and safety of AGVs. The A* algorithm and the ant colony algorithm's principles will be analyzed in the first part of the paper. Then describe the research status of the two respectively and summarize the advantages and defects respectively. Finally, prospect the development progress of AGV coordinated scheduling and path planning.

Agent technology has been considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. The conflict resolution of automated guided vehicles (AGVs) in a flexible manufacturing systems (FMS) environment is one such problem that comes under this category. This paper describe a multi-agent approach to the operational control of AGVs by integration of path generation, enumerating time-windows, searching interruptions, adjusting waiting time and taking decisions on the selection of routes. It presents an efficient algorithm and rules for finding a conflict-free shortest-time path for AGVs, which is applicable to a bi- and unidirectional flow path network. The concept of loop formation in a flow path network is introduced to deal with the parking of idle vehicles, without obstructing the path of moveable AGVs. The concept of loop formation at nodes reduces the timing-taking task of finding the dynamic positioning of idle AGVs in the network.

Efficient path planning for Automated Guided Vehicles (AGVs) in warehouse automation is crucial yet challenging, particularly in environments with irregular structures and constrained spaces. This study addresses these challenges by focusing on AGVs without rotary platforms, which require the rotation of the entire robot-rack assembly for directional changes, demanding additional space and complex path planning. We have developed dedicated algorithms that integrate robotics and optimization principles to tackle these issues. Our methods take into account the spatial requirements for rack rotation, navigating through limited inter-rack clearance, and adapting to irregular warehouse layouts. Extensive simulations and real-world applications validate the proposed solutions, demonstrating significant improvements in traversal efficiency and collision risk mitigation. The results indicate that our algorithms effectively enhance the operational efficiency and reliability of AGV systems in complex warehouse environments. This research adapts AGV path planning by providing robust strategies to optimize navigation in challenging settings, ultimately improving warehouse productivity.

Path planning is one of the most important fields of research in the area of robotics. In this paper, a path planning method for a certain type of wheeled mobile robots called automated guided vehicles (AGVs) is proposed. The proposed model applies fuzzy control techniques to navigate the AGV in an unknown environment to reach a certain destination. In addition, a new method for escaping local minimums, which may be very critical issue faced by an AGV in an unknown environment, is introduced. Simulation results show satisfactory performance of the proposed method

To address the issues of insufficient safety distance and unsmooth paths in AGV path planning for narrow road scenarios, this paper proposes a method that integrates Voronoi-skeleton-based custom layers with traditional cost maps. First, key nodes of the Voronoi skeleton are extracted to generate a custom layer, which is then combined with static, obstacle, and expansion layers to form a new four-layer network cost map. This approach accurately distinguishes obstacle influences and enhances algorithm robustness. The A* algorithm based on this new map guides the automated guided vehicle (AGV) to travel safely along the road center. Second, an improved A* algorithm is employed for global planning to ensure safe navigation. Finally, B-spline smoothing is applied to the global path to enhance the AGV's efficiency and stability in complex environments. The experimental results show that in narrow road scenarios, the proposed algorithm improves AGV path planning safety by 82%, reduces the number of spatial turning points by 55.85%, and shortens planning time by 48.98%. Overall, this algorithm significantly enhances the robustness and real-time performance of path planning in narrow roads, ensuring the AGV moves safely in an optimal manner.

The most fundamental requirements for achieving accurate navigation of mobile robots are localization, map building and path planning. Currently, the mainstream methods include magnetic peg navigation, laser navigation, and 2D code navigation, etc. SLAM technology has the advantages of automation, intelligence, environmental adaptability, and low cost, and AGVs using this algorithm have been widely used in indoor applications and logistics transportation. Therefore, the SLAM (Simultaneous Localization and Mapping) algorithm based Automated Guided Vehicle (AGV) has become a hot research topic at home and abroad. In this paper, we investigate the SLAM algorithm in AGV system, and build indoor and logistics simulation environment maps based on 2D laser SLAM technology of Hector, Cartographer, Gmapping and Karto algorithms, and compare them by building accuracy index. The results show that the Hector algorithm has faster map building speed and accuracy, and is more suitable for indoor and logistics AGV systems.

Automated Guided Vehicles (AGV) with magnetic navigation and electromagnetic navigation have lower path flexibility due to the need to lay the path ahead of time. The Simultaneous Localization and Mapping (SLAM) is mature in technology theory, so that this technique is applied to the AGV navigation. The laser sensor is used to acquire the information in the location environment for immediate location and mapping, so that carry out the path planning and complete the AGV autonomous navigation. Extended Kalman Filter (EKF) is the most common solution for laser SLAM. Firstly, this paper analyzes the research results of EKF-SLAM at this stage and its limitations. Secondly, it discusses the application of EKF-SLAM in AGV and the development direction in the next stage. By comparative analysis shows that laser EKF-SLAM can meet the requirements of AGV navigation. The development of EKF-SLAM algorithm in positioning, mapping accuracy and the ability to solve interferences in dynamic environment has an important reference for the reliability of AGV navigation.

Manual order picking remains a labour- and cost-intensive warehouse process. Automation technologies have gained traction, with automated guided vehicles (AGVs) assisting human pickers by handling transport tasks and optimising workflows. However, due to high cost levels and infrastructural constraints, AGVs are often introduced incrementally, resulting in mixed-technology scenarios in which workers with a prior-generation technology and workers with the new (AGV) generation technology operate side by side, for example, in the same warehouse aisle. One challenge in such settings is picker blocking, which occurs when the actions of one picker impede or delay the movements of another. This study empirically examines pick-column blocking and within-aisle blocking with respect to their impact on task performance time. Using a mixed-effects regression model, we analyse 490,398 pick events from a German retailer during 2023. This approach accounts for repeated observations and individual heterogeneity in picker performance over time. Our results show that both types of blocking significantly increase task performance time. AGV assistance mitigates these negative effects, reducing the additional delay by 3.4% for pick-column blocking and 2.1% for within-aisle blocking. Our results demonstrate that AGVs do more than provide transport assistance, reducing congestion and ensuring smoother, more efficient movement. The study provides valuable theoretical and management insights to optimise warehouse operations and contributes to the growing literature on human–AGV collaboration in logistics.