AGV Path Planning in Unknown Environment Using Fuzzy Inference Systems

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.

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.

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.

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.

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.

With the booming development of logistics, manufacturing and warehousing fields, the autonomous navigation and intelligent obstacle avoidance technology of automated guided vehicles (AGVs) has become the focus of scientific research. In this paper, an enhanced deep reinforcement learning (DRL) framework is proposed, aiming to empower AGVs with the ability of autonomous navigation and obstacle avoidance in the unknown and variable complex environment. To address the problems of time‐consuming training and limited generalisation ability of traditional DRL, we refine the twin delayed deep deterministic policy gradient algorithm by integrating adaptive noise attenuation and dynamic delayed updating, optimising both training efficiency and model robustness. In order to further strengthen the AGV's ability to perceive and respond to changes of a dynamic environment, we introduce a distance‐based obstacle penalty term in the designed composite reward function, which ensures that the AGV is capable of predicting and avoiding obstacles effectively in dynamic scenarios. Experiments indicate that the AGV model trained by this algorithm presents excellent autonomous navigation capability in both static and dynamic environments, with a high task completion rate, stable and reliable operation, which fully proves the high efficiency and robustness of this method and its practical value.

본 논문은 아날로그형 자기위치 장치(magnetic positioning system)의 개발과 퍼지 추론 시스템(FIS: fuzzy inference system)을 통한 정밀도 향상에 관한 것이다. 자기위치 장치는 무인운반차(AGV: automatic guided vehicle)의 자기-자이로 유도장치(magnet-gyro guidance system)에 사용되는 장치로, 바닥에 매설된 자석의 위치를 계측하는 장치이다. 기존의 판매되고 있는 자기-자이로 유도 장치는 외국에서 독점 판매되고 있어, 국내에서는 가격이 매우 비싸다. 또한, 자기위치 장치에 디지털 타입의 단극성 홀센서를 이용하기 때문에 위치측정 정밀도가 낮다. 이에, 본 논문에서는 자기위치 장치를 직접 개발하였고 퍼지 추론 시스템을 통해 자기위치 장치의 정밀도 향상시켰다. 실험은 직접 개발한 아날로그형 자기위치 장치를 이용하였으며, 기존의 위치측정 방법과 제안된 방법의 성능을 비교하였다. 실험 결과, 제안된 방법이 자기위치 장치의 정밀도를 향상시킴을 확인하였다. This paper presents a development of an analog type magnetic positioning system and its positioning accuracy improvement using fuzzy inference system. As the magnetic positioning system used on a magnet-gyro guidance system for AGV(automatic guided vehicle), it measures a position of magnet embedded in floor of the work place. The existing product of the magnetic positioning system is very expensive in Korea because it is being sold in a foreign country exclusively. Moreover, the positioning accuracy of the product is low because it uses digital type unipolar hall sensors. Hence, we developed the magnetic positioning system by ourselves and improved the positioning accuracy of the developed magnetic positioning system using fuzzy inference system. For experiment, we used the analog type magnetic positioning system which we have developed, and compared the performance of the proposed method with the performance of the existing positioning method for the magnetic positioning system. In experimental results, we verified that the proposed method improved the positioning accuracy of the magnetic positioning system.

In this paper, an adaptive fuzzy control (AFC) system is applied to velocity and direction angle control of a certain type of wheeled mobile robots called automated guided vehicles (AGVs). The fuzzy control system includes an adaptive model identifier and controller. The gains of fuzzy controller are obtained by using the fuzzy identifier model which is defined by real system outputs and control inputs. The parameters of fuzzy identifier model are adjusted online by using recursive least square algorithm. A PI controller is also applied to AGV to show the robustness of the AFC system. Experimental results prove that the AFC shows better tracking performance than the PI controller in terms of robustness, smoothness and fast dynamics. Results are given for complex references, sudden disturbance and extra load conditions.

In the case of knowing the environmental information of static obstacles, in order to improve the path of AGV (automatic guided vehicle) in logistics system, the paper uses a method of path planning which based on modified potential field. The potential field is more convenient and efficient than the intelligent method, and it's one of many path planning methods. The calculated amount of improved algorithm is little, it's easier to meet real-time requirement and relatively simple in mathematical description, resulting in relatively smooth and safe path. Improved potential field improves the operation efficiency and safety of the system in actual system applications. The analysis of the typical examples verifies the validity of this method.

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.

Intelligent warehouse becomes a key component of logistics process automation, which essentially promotes the productivity and cost reduction. This paper presents a novel design solution of an Automated Guided Vehicles (AGVs) system for intelligent warehouse. An improved version of classical Dijkstra shortest-path algorithm is proposed for efficient global path planning. In the case of multi-AGV, the time windows method is used to address the issue of conflict and deadlock. In addition, the local path planning and auto-localization is addressed by using a heuristics-based algorithm and Monte Carlo Localization algorithm respectively. Extensive numerical experiments based on Player/Stage simulator are carried out to assess the suggested algorithms, for a range of scenarios and the result well validates its effectiveness. Currently the proposed design solution is adopted in developing the prototype of AGVs to be deployed in practice.

The Automated Guided Vehicle (AGV) is a device that uses various sensor technologies, automation, and multiple control systems. AGVs have many navigation methods, of which the magnetic stripe navigation system is well developed. The magnetic stripe navigation AGV positioning is carried out precisely and at a lower cost level. However, the magnetic stripe is prone to breakage, and the AGV can only travel according to the magnetic stripe and cannot be intelligently avoided or the task changed in real-time by the control system. This paper draws on magnetic stripe navigation tracking principles, using grey-scale sensors for stable path tracking; distance sensors are combined for timely obstacle avoidance; Bluetooth is used to obtain approximate indoor locations for path planning. The navigation system simultaneously combines multi-sensor information for navigation, ultimately enabling the AGV to be quickly positioned accurately along the prescribed path and return to the route in time to avoid obstacles.

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

With the rapid development of computer technology and automation technology, Flexible Manufacturing System (FMS) as a modern advanced manufacturing mode is getting more and more attention by enterprises. Automated Guided Vehicle (AGV) is the main loading and unloading, and transportation instrument with its advantages of intelligent and flexible in the system. AGV has played an important role in the realization of logistics informatization, reduce logistics cost, improve the efficiency of the enterprise. Scheduling and route planning of these tasks from the system were made based on the proposed path planning methods in the empowerment directed acyclic graph. In this paper, an algebraic algorithm is proposed for a simple weighted path planning problem. Based on algebraic algorithm, the shortest path and its minimum iteration steps are calculated through direct distance matrix. The experimental results show that using this algorithm, the shortest path and its length can be obtained quickly. At the same time, compared with the traditional Dijkstra algorithm, the performance of the proposed algorithm is improved.