Path planning method and system for agv carrier of silver electrolysis process workshop

Abstract

The application relates to the technical field of path planning, and discloses a path planning method and system for an AGV carrier in a silver electrolysis process workshop. The method comprises the following steps: collecting environment features of the silver electrolysis process workshop and constructing an electronic map to obtain an electronic map of the silver electrolysis process workshop; calculating an initial optimal path of the AGV carrier from a current position to a target workstation; detecting actual driving resistance during execution of the initial optimal path to obtain path correction parameters; performing local adjustment to obtain an energy-saving and safe path; and monitoring a moving obstacle in front and an electrolyte leakage area in real time during execution of the energy-saving and safe path by the AGV carrier, and generating a temporary bypass path. The application makes the path planning more suitable for the actual environment features of the silver electrolysis workshop, can timely respond to sudden situations in the silver electrolysis workshop, guarantees the safety of equipment and the continuity of production, reduces the empty load rate of the AGV carrier, and improves the overall logistics efficiency of the silver electrolysis workshop.

Description

Technical Field

[0001] This application relates to the field of path planning technology, and in particular to a path planning method and system for AGV transport vehicles used in silver electrolysis process workshops. Background Technology

[0002] Silver electrolysis, a key process in non-ferrous metal smelting, is characterized by a highly corrosive production environment, densely packed equipment, and complex processes. Traditional manual handling methods are not only inefficient but also pose significant safety hazards in this environment, while conventional automated guided vehicles (AGVs) are ill-suited to the unique environmental requirements of silver electrolysis workshops. The densely packed electrolytic cells in silver electrolysis workshops necessitate the regular replacement and handling of materials such as cathode and anode plates. Furthermore, the complex and variable floor conditions and uneven distribution of corrosive gases make conventional path planning methods inadequate for practical application needs.

[0003] Existing AGV (Automated Guided Vehicle) path planning methods are mostly designed for standard industrial environments, failing to consider the unique corrosive environment, irregular equipment layout, and dynamically changing ground conditions of silver electrolysis workshops. When applied in silver electrolysis workshops, these methods often result in problems such as unreasonable path selection, frequent detours, and excessive energy consumption. Particularly when workshop ground conditions and workloads change, the accuracy and stability of path planning significantly decrease, leading to low handling efficiency. Furthermore, conventional path planning algorithms cannot effectively handle unexpected situations such as electrolyte leaks, increasing the risk of equipment damage and production interruptions. When multiple AGVs work collaboratively in a silver electrolysis workshop, existing technologies struggle to achieve efficient task allocation and path coordination, especially when cathode and anode plate handling tasks are performed simultaneously, easily leading to path conflicts and resource contention. Traditional centralized scheduling systems are slow to respond and struggle to cope with dynamic obstacles and unexpected situations within the workshop, while distributed systems lack overall coordination capabilities and struggle to optimize global resource allocation. In addition, existing technologies do not fully consider the special process flows and handling task priorities in silver electrolysis, reducing overall production efficiency and affecting process stability. Summary of the Invention

[0004] This application provides a path planning method and system for AGV transport vehicles in a silver electrolysis process workshop. This application makes the path planning more in line with the actual environmental characteristics of the silver electrolysis workshop, can respond to emergencies in the silver electrolysis workshop in a timely manner, ensure equipment safety and production continuity, reduce the empty load rate of AGV transport vehicles, and improve the overall logistics efficiency of the silver electrolysis workshop.

[0005] In a first aspect, this application provides a path planning method for AGV transport vehicles used in a silver electrolysis process workshop, the path planning method for AGV transport vehicles used in a silver electrolysis process workshop comprising:

[0006] Environmental features were collected and an electronic map was constructed for the silver electrolysis process workshop, resulting in an electronic map of the silver electrolysis process workshop;

[0007] The initial optimal path for the AGV transport vehicle from its current location to the target workstation is calculated based on the electronic map of the silver electrolysis process workshop.

[0008] The actual driving resistance during the execution of the initial optimal path is detected. When the abnormal value of the path segment resistance exceeds the momentum residual threshold, the actual friction coefficient and obstacle conditions of the path segment are recorded to obtain the path correction parameters.

[0009] Based on the path correction parameters, the initial optimal path is locally adjusted by bypassing high-corrosion and high-friction coefficient regions to obtain an energy-saving and safe path.

[0010] During the execution of the energy-saving and safe path by the AGV transport vehicle, the moving obstacles and electrolyte leakage areas ahead are monitored in real time, and the temporary obstacle avoidance direction and speed are calculated to generate a temporary detour path.

[0011] Secondly, this application provides a path planning system for AGV transport vehicles in a silver electrolysis process workshop, the path planning system for AGV transport vehicles in a silver electrolysis process workshop comprising:

[0012] The module is used to collect environmental features and build an electronic map of the silver electrolysis process workshop, resulting in an electronic map of the silver electrolysis process workshop.

[0013] The calculation module is used to calculate the initial optimal path for the AGV transport vehicle from its current position to the target workstation based on the electronic map of the silver electrolysis process workshop.

[0014] The detection module is used to detect the actual driving resistance during the execution of the initial optimal path. When the abnormal value of the path segment resistance exceeds the momentum residual threshold, the actual friction coefficient and obstacle conditions of the path segment are recorded to obtain the path correction parameters.

[0015] The local adjustment module is used to locally adjust the initial optimal path according to the path correction parameters by bypassing high corrosion areas and high friction coefficient areas to obtain an energy-saving and safe path.

[0016] The real-time monitoring module is used to monitor moving obstacles and electrolyte leakage areas in real time during the process of the AGV transport vehicle executing the energy-saving and safe path, and to calculate the temporary obstacle avoidance direction and speed, and generate a temporary detour path.

[0017] The technical solution provided in this application accurately distinguishes between electrolytic cell areas, corrosive gas areas, and densely populated equipment areas by collecting environmental characteristics of the silver electrolysis workshop and constructing a refined electronic map. This provides detailed environmental information for AGV transport vehicles, making path planning more closely aligned with the actual environmental characteristics of the silver electrolysis workshop. A comprehensive scoring mechanism is used to calculate the initial optimal path, considering path length, corrosive environment exposure, and equipment collision risk. This reduces the time AGV transport vehicles spend in highly corrosive areas, extends equipment lifespan, and lowers maintenance costs. Dynamic detection of actual path travel resistance and recording of correction parameters enable real-time adaptation to changes in the ground conditions of the silver electrolysis workshop, making the path planning scheme more suitable for the complex and ever-changing environment. Local adjustments are made based on path correction parameters to bypass highly corrosive and high-friction areas, reducing energy consumption and improving the operating efficiency and safety of AGV transport vehicles in the silver electrolysis workshop. Real-time monitoring of moving obstacles and electrolyte leakage areas ahead calculates temporary obstacle avoidance directions and speeds, enabling AGV transport vehicles to respond promptly to emergencies in the silver electrolysis workshop, ensuring equipment safety and production continuity. By using a binary task structure and a leader-follower architecture, multiple AGV transport vehicles can work collaboratively, optimizing the handling process of cathode and anode plates, reducing the empty load rate of AGV transport vehicles, and improving the overall logistics efficiency of the silver electrolysis workshop. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an embodiment of the path planning method for AGV transport vehicles used in a silver electrolysis process workshop according to the present application.

[0020] Figure 2 This is a schematic diagram of an embodiment of the path planning system for an AGV transport vehicle used in a silver electrolysis process workshop, as described in this application. Detailed Implementation

[0021] This application provides a path planning method and system for AGV (Automated Guided Vehicle) transport vehicles in a silver electrolysis process workshop. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the path planning method for AGV transport vehicles in a silver electrolysis process workshop, as described in this application, includes:

[0023] Step S101: Collect environmental features and construct an electronic map of the silver electrolysis process workshop to obtain an electronic map of the silver electrolysis process workshop;

[0024] It is understood that the executing entity of this application can be a path planning system for AGV transport vehicles used in silver electrolysis process workshops, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiments use a server as an example for illustration.

[0025] Specifically, data is collected based on the physical layout of the silver electrolysis workshop, including static obstacle location information, equipment layout information, process pipeline location information, and the distribution of work areas. Multiple high-precision positioning camera systems are installed within the workshop, combined with LiDAR scanning and a visual recognition system, to acquire spatial structure data, including important information such as equipment installation areas, passageway distribution, electrolytic cell locations, and storage areas. This also covers the layout of the pipeline network, which poses potential obstacles to the AGV's movement path. The raw environmental data of the workshop is input into the system and rasterized, dividing the entire space of the silver electrolysis workshop into equal-sized basic cells, each representing a physical area. The cell size is set at 10 cm × 10 cm to ensure sufficient spatial resolution during path planning. Each cell is assigned different attribute labels, distinguished according to different functional areas of the workshop, including free areas, static obstacles, electrolytic cell areas, cathode plate storage areas, anode plate storage areas, short-circuit plate storage areas, passageway areas, and areas with high concentrations of corrosive gases. Free zones indicate areas where AGVs can freely pass, while static obstacle zones indicate areas that are inaccessible or contain immovable obstacles. Electrolytic cell areas are critical process areas in the workshop and are marked as special zones to prevent accidental entry and potential hazards. Cathode and anode plate storage areas are key stations for AGV operations; path planning in these areas requires priority optimization, and short-circuit plate storage areas and other storage areas also need clear labeling. Areas with high concentrations of corrosive gases are specially marked so that path planning can proactively avoid these areas, thereby extending the lifespan of AGVs and reducing the risk of equipment damage. The marked grid data is converted into a workshop topology map. The topology map is the core form of an electronic map, where each grid cell is mapped to a node in the topology, representing passable locations connected by edges. Edges are established based on the physical positional relationships between grid cells; connecting edges are created between adjacent cells, and these edges are assigned appropriate weights according to the characteristics of different areas. The edge weight allocation depends on several factors, including the actual distance of the path, the degree of corrosivity of the area, and the friction coefficient of the ground. The generated weighted edge set provides path information for subsequent path planning algorithms, enabling the system to select the optimal path based on the characteristics of different regions. All workstations are marked based on the weighted edge set to construct an electronic map of the silver electrolysis process workshop. The marked workstations include electrolytic cell plate removal stations, cathode plate storage stations, and anode plate storage stations.

[0026] Step S102: Calculate the initial optimal path for the AGV transport vehicle from its current location to the target workstation based on the electronic map of the silver electrolysis process workshop;

[0027] Specifically, the starting position and target workstation of the AGV transport vehicle are determined based on its real-time location and task requirements in the silver electrolysis process workshop. This process involves analyzing the current task status, including determining whether the current transport task is to move cathode or anode plates, and obtaining the corresponding target workstation coordinates through the task management system to accurately locate the coordinates of the starting and ending points. This coordinate information forms the basis of path planning. Based on the coordinates of the starting and target positions, the start and end points of the path search are marked on the electronic map, providing precise input parameters for the subsequent path search algorithm. A priority queue is constructed on the electronic map of the silver electrolysis process workshop based on the start and end point coordinates, and the starting position node is added to the priority queue. Simultaneously, the path cost is initialized to obtain the initial state of the path search. During the path search process, the priority queue is used to store nodes to be processed. The system always prioritizes expanding the node with the lowest cost to ensure that the optimal path is considered first during the path search process. As the search progresses, the system continuously selects the node with the lowest current cost from the priority queue for expansion and calculates the path cost of its adjacent nodes, resulting in a set of candidate path nodes. Different travel cost coefficients are assigned to nodes in the candidate path node set based on their location within the corresponding area type to better reflect the travel difficulty of different areas. Due to the complex environment of the silver electrolysis workshop, travel costs vary significantly between different areas. Therefore, different travel costs are set for different areas; for example, the passageway area has the lowest travel cost coefficient, while the electrolysis cell area, due to its narrow space and higher risk, has a correspondingly higher travel cost coefficient. A turning penalty factor is introduced during the path cost calculation to adjust the path cost value, reflecting the additional consumption of the AGV transport vehicle during turning. The turning penalty factor is calculated based on the angle between adjacent path segments; the larger the angle, the higher the penalty factor, thereby minimizing unnecessary turns during path search and optimizing the overall smoothness and safety of the path. The safety of each candidate path is checked based on the corrected node cost value to ensure that all paths meet the safety requirements of the silver electrolysis process workshop. During the safety check, the minimum safe distance between the path and static obstacles is checked, and whether the path passes through highly corrosive areas and high-friction areas that may cause equipment damage are considered. Paths that meet the safety constraints are retained to form a path set. A comprehensive score is calculated for each path based on a set of paths that meet safety constraints. The scoring criteria include multiple dimensions such as path length, travel cost, energy consumption, number of turns, and safety. Each dimension is weighted and evaluated to calculate a comprehensive score for each path. The path with the highest score from the set of paths meeting safety constraints is selected as the initial optimal path and provided to the AGV (Automated Guided Vehicle) for execution.

[0028] Step S103: Detect the actual driving resistance during the execution of the initial optimal path. When the abnormal value of the path segment resistance exceeds the momentum residual threshold, record the actual friction coefficient and obstacle conditions of the path segment to obtain the path correction parameters.

[0029] Specifically, a multi-sensor system installed on the AGV (Automated Guided Vehicle) collects real-time driving data to obtain raw data on the vehicle's status during travel. These sensors include distance sensors, a vision recognition system, and torque sensors. Distance sensors detect surrounding obstacles and path conditions, the vision recognition system identifies path markers, equipment status, and area features, while torque sensors detect changes in the forces acting on the vehicle during travel. Through the coordinated operation of these sensors, driving data for each path segment is acquired in real time during the AGV's path execution, providing environmental parameters and status information for subsequent path correction. The raw data on the AGV's driving status is segmented according to path segments, dividing the driving process into multiple path segments. Each path segment corresponds to a travel interval on the initial optimal path. The segmented driving parameters include the travel time, average speed, force, and other key data for each segment. Based on the segmented driving data, the momentum residual value for each path segment is calculated. The momentum residual is used to determine whether the resistance of the path segment is abnormal by comparing the theoretical momentum with the actual momentum change. The momentum residual is calculated based on the AGV's mass, average speed, and the force-time product of the path segment. The calculated momentum residual data reflects the actual resistance changes during the AGV's movement. If the momentum residual value of a certain path segment is high, it indicates abnormal resistance in that path segment. To determine the abnormality of a path segment, the momentum residual data of that path segment is compared with a preset momentum residual threshold. When the momentum residual value exceeds the threshold, the path segment is marked as an abnormal path segment, resulting in a set of abnormal path segments. An adaptive learning rate is calculated based on the momentum residual values ​​of the abnormal path segments to obtain parameter update coefficients. The adaptive learning rate is a dynamically adjusted parameter that automatically adjusts the update step size according to the magnitude of the momentum residual value of the path segment, thereby effectively correcting the friction coefficient of the abnormal path segments. Based on the calculated parameter update coefficients, the ground friction coefficient of each path segment in the set of abnormal path segments is corrected, and the corrected friction coefficient is used as the path correction parameter.

[0030] Step S104: Based on the path correction parameters, the initial optimal path is locally adjusted by bypassing high corrosion areas and high friction coefficient areas to obtain an energy-saving and safe path;

[0031] Specifically, path correction parameters are fed back into the electronic map of the silver electrolysis process workshop to adjust the edge weights of relevant areas. These parameters are obtained after detecting abnormal resistance in path segments and calculating the actual friction coefficient. The weights of each path segment in the electronic map are dynamically adjusted using these parameters to ensure the weight data reflects the current environmental conditions of the workshop. Based on the correction parameters, the weighted edge set of the silver electrolysis process workshop is updated, appropriately increasing the edge weights of high-corrosion and high-friction areas. This ensures that path planning prioritizes avoiding these areas during subsequent adjustments, resulting in updated electronic map weight data. Based on the updated electronic map weight data and the initial optimal path, a set of path segments to be adjusted is identified. These include path segments with abnormal momentum residuals, high-corrosion areas, and high friction coefficients. These path segments affect the energy consumption and driving safety of the AGV transport vehicles and may cause abnormal vehicle stoppages or equipment damage. By analyzing the cost of the path segment set, path segments with higher weights are prioritized for adjustment. Each path segment in the set of path segments to be adjusted is smoothed to bypass highly corrosive and high-friction areas, resulting in locally optimized path segments. Path smoothing employs curve fitting or spline interpolation methods, adjusting key points of the path segments to achieve path optimization. Based on the spatial distribution characteristics of the path segments to be adjusted, multiple candidate paths are set, and the path with the lowest cost and best meeting safety requirements is selected for local replacement. This process effectively avoids the influence of highly corrosive and high-friction areas and reduces the driving resistance of the transport vehicle in complex environments, thereby achieving energy-saving optimization of the path. Control points for locally optimized path segments are selected by minimizing the energy consumption objective function to ensure that the selected path segments achieve optimal energy consumption. During the optimization of the energy consumption objective function, factors such as the length of the path segment, the friction coefficient, and the number of turns are comprehensively considered. The selection of control points ensures that the path reduces overall energy consumption while maintaining smooth driving. After completing the control point optimization, the system performs collision detection verification on the energy-optimized path segment to ensure that the new path segment will not collide with static obstacles or dynamic equipment during actual operation, resulting in safe and reliable local path segments. Safe local path segments are reconnected with unmodified path segments from the initial optimal path to form an energy-efficient and safe path. During path splicing, a path smoothing algorithm is used to process the transition points, ensuring smooth connections between path segments without significant speed abrupt changes or directional deviations. Simultaneously, the cost of the spliced ​​path is recalculated to ensure that the energy consumption and safety of the entire path remain at an ideal level.

[0032] Step S105: During the process of the AGV transport vehicle executing the energy-saving and safe path, monitor the moving obstacles and electrolyte leakage areas ahead in real time, calculate the temporary obstacle avoidance direction and speed, and generate a temporary detour path.

[0033] Specifically, a multi-sensor system installed on the AGV transporter collects real-time environmental data from the workshop. These sensors include multi-directional distance sensors, a vision recognition system, and torque sensors. The multi-directional distance sensors detect the position and distance changes of obstacles around the AGV. The vision recognition system identifies the shape, speed, and direction of moving obstacles ahead and can detect the location of electrolyte leaks. The torque sensors detect real-time torque changes caused by obstacles or ground anomalies during the AGV's movement. These sensors work together to form a real-time obstacle information acquisition mechanism. Based on the real-time obstacle information, the AGV's current position, and the characteristics of the surrounding environment, a combined potential field function is established for the silver electrolysis workshop environment. This function is a comprehensive potential field model combining gravitational and repulsive fields. The gravitational field guides the AGV to the target position, while the repulsive field prevents collisions between the AGV and obstacles or hazardous areas. The combined potential field function dynamically adjusts the potential field strength in different regions. For example, when an electrolyte leak is detected, the system generates a strong repulsive field in that region, forcing the transport vehicle away from the danger zone. Simultaneously, a repulsive field is set in front of the path of moving obstacles to guide the transport vehicle to choose a safe direction of travel. After establishing the combined potential field function, the negative gradient value of the function is calculated, and the direction of the negative gradient is used as the temporary travel direction of the AGV transport vehicle. The selection of the negative gradient direction ensures that the transport vehicle moves along the direction of minimum potential field, thereby avoiding obstacles and danger zones, forming a dynamic basic obstacle avoidance direction. After determining the temporary travel direction, a speed candidate set is constructed based on the current state and dynamic constraints of the AGV transport vehicle. The speed candidate set includes different speed combinations. The candidate speeds need to meet the safe driving requirements within the workshop and take into account the dynamic constraints of the AGV transport vehicle, such as acceleration, deceleration, and turning radius. For each speed combination, a speed score is calculated based on the current driving state and temporary travel direction. The scoring criteria include multiple factors such as obstacle avoidance efficiency, path offset, driving smoothness, and energy consumption optimization. Each speed combination in the candidate speed set is comprehensively scored, and the speed combination with the highest score is selected as the current real-time obstacle avoidance control command. This speed combination ensures that the transport vehicle maintains smooth operation during obstacle avoidance and minimizes path deviation and energy consumption, thus achieving efficient obstacle avoidance. When the system detects that the original path is no longer feasible, such as when the path is completely blocked due to obstacle movement or when electrolyte leakage makes the path area unsafe, the system replans the path in the local area, generating a temporary detour path. The temporary path planning uses a dynamic path planning algorithm to search for a new path that avoids obstacles and meets safety constraints within the limited local area, and seamlessly splices this detour path back to the original energy-saving and safe path, thereby ensuring that the transport vehicle can return to the target travel direction in the shortest possible time.The generated temporary detour path needs to take into account the characteristics of the current environment and ensure the smoothness of the path, the stability of the steering, and the smoothness of the connection with the original path, so as to ensure that the AGV transport vehicle does not produce obvious speed fluctuations or directional deviations during the driving process after obstacle avoidance.

[0034] Based on the silver electrolysis process flow, the tasks of the AGV transport vehicles are classified into cathode plate transport tasks and anode plate transport tasks. These tasks correspond to different process requirements and transportation objectives. The cathode plate transport task mainly involves removing the cathode plates from the electrolytic cell and transporting them to the cathode plate storage area, while the anode plate transport task involves moving the anode plates from the storage area to the electrolytic cell and completing their installation. A leader AGV and multiple follower AGVs are assigned to each of the cathode and anode plate transport tasks, forming a binary task structure. The leader AGV is responsible for path planning and execution guidance, while the follower AGVs coordinate their movements according to the leader AGV's path instructions, thereby achieving efficient collaborative transport among multiple AGVs within the silver electrolysis workshop. After constructing the binary task structure, collaborative control is implemented based on a leader-follower architecture control model. The leader-follower architecture achieves stable queue operation by establishing the relative position and speed relationships between the leader AGV and the follower AGVs. The system acquires the real-time position, speed, and steering information of the lead AGV, generates corresponding motion control commands for each following AGV, and dynamically adjusts the motion trajectory and speed of the following AGVs based on the real-time status of the transport tasks, forming an intra-group collaborative control strategy. During collaborative control, time delay compensation and nonlinear fuzzy adaptive control techniques are employed to ensure that the following AGVs can respond quickly to path adjustments and speed changes, thereby maintaining the stability and accuracy of the queue and avoiding congestion or collisions in the narrow areas of the electrolysis workshop. After completing the intra-group collaborative control strategy design, a task sequence is constructed for all transport tasks in the silver electrolysis process workshop. All tasks are sorted according to their urgency, priority, start and end time windows, and path complexity, and a task sequence matrix is ​​constructed. The task sequence matrix maps all transport tasks to different time windows and sorts them reasonably according to the constraints of each task. After obtaining the task sequence, the task sequence is used as input, and the Hungarian algorithm is used for task allocation. The Hungarian algorithm achieves optimal task matching by minimizing the total cost of task allocation, assigning each task to the most suitable AGV and generating an AGV execution plan. The execution plan includes the start and end times of each AGV's task, path planning results, and expected completion time, ensuring task coordination among multiple AGVs. Spatiotemporal conflict detection is performed on the execution plans of each AGV to ensure that different AGVs do not experience path intersections or time conflicts during task execution. Spatiotemporal conflict detection includes path conflict detection, task time overlap detection, and congestion area identification. Based on the AGV's path and task time window, potential conflicts are detected, and pre-processing is performed when conflicts are found. Conflicts are eliminated by adjusting the path or task time to obtain a conflict-free execution plan. By introducing dynamic constraints and a safety distance protection mechanism, it is ensured that the paths of different AGVs do not intersect at critical time points, guaranteeing the smooth execution of the transport task.An event-triggered task reassignment mechanism is designed based on a conflict-free execution scheme to dynamically respond to unexpected situations during task execution. When the system detects abnormal events such as task anomalies, AGV malfunctions, or path blockages, a local replanning mechanism is automatically triggered. By re-analyzing the current task status and path data, a new path planning scheme is generated within a local area, and task resources are reallocated to form a global collaborative path planning scheme.

[0035] In this embodiment, by collecting environmental characteristics of the silver electrolysis workshop and constructing a refined electronic map, the electrolytic cell area, corrosive gas area, and equipment-dense area are accurately distinguished, providing detailed environmental information for the AGV transport vehicle and making the path planning more closely match the actual environmental characteristics of the silver electrolysis workshop. A comprehensive scoring mechanism is used to calculate the initial optimal path, considering path length, corrosive environment exposure, and equipment collision risk, reducing the AGV transport vehicle's dwell time in highly corrosive areas, extending equipment lifespan, and reducing equipment maintenance costs. The actual travel resistance of the path is dynamically detected and correction parameters are recorded, enabling real-time adaptation to changes in the ground conditions of the silver electrolysis workshop, making the path planning scheme more suitable for the complex and ever-changing actual environment of the silver electrolysis workshop. Local adjustments are made based on the path correction parameters to bypass highly corrosive and high-friction areas, reducing energy consumption and improving the operating efficiency and safety of the AGV transport vehicle in the silver electrolysis workshop. Real-time monitoring of moving obstacles and electrolyte leakage areas ahead calculates temporary obstacle avoidance directions and speeds, enabling the AGV transport vehicle to respond promptly to emergencies in the silver electrolysis workshop, ensuring equipment safety and production continuity. By using a binary task structure and a leader-follower architecture, multiple AGV transport vehicles can work collaboratively, optimizing the handling process of cathode and anode plates, reducing the empty load rate of AGV transport vehicles, and improving the overall logistics efficiency of the silver electrolysis workshop.

[0036] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0037] Based on the physical layout of the silver electrolysis process workshop, static obstacle location information, equipment layout information, process pipeline location information, and work area distribution information are collected to obtain the original environmental data of the workshop.

[0038] The original environmental data of the workshop is rasterized to obtain a workshop raster model;

[0039] Each cell in the workshop grid model is labeled with attributes to obtain labeled grid data. The attribute labels include free areas, static obstacles, electrolytic cell areas, cathode plate storage areas, anode plate storage areas, short-circuit plate storage areas, channel areas, and areas with high concentrations of corrosive gases.

[0040] The marked raster data is converted into a workshop topology map, and each edge in the workshop topology map is assigned a weight value based on the actual distance, the corrosion coefficient and the friction coefficient of the area, to obtain a weighted edge set;

[0041] Based on the weighted edge set, all work stations are marked to obtain an electronic map of the silver electrolysis process workshop. The work stations include the electrolytic cell plate retrieval station, the cathode plate storage station, and the anode plate storage station.

[0042] Specifically, environmental information was collected from the silver electrolysis process workshop. High-precision LiDAR, a visual recognition system, and industrial cameras were deployed at key locations within the workshop, combined with panoramic scanning and 3D modeling technologies to collect spatial layout data. LiDAR scanned the entire workshop environment with millimeter-level precision, identifying the locations of static obstacles such as walls, equipment, fixtures, and process piping. The visual recognition system used image recognition technology to confirm the equipment layout within the workshop, including the boundaries and relative positions of key working areas such as electrolytic cells, cathode plate storage areas, anode plate storage areas, and short-circuit plate storage areas. Simultaneously, the layout information of process piping was identified through a combination of laser scanning and visual recognition, ensuring the capture of the spatial orientation and connections of the piping. Information on the distribution of working areas within the workshop, including the location, boundaries, and environmental characteristics of different functional areas, was collected using multi-sensor fusion technology. After preliminary processing, this data formed the workshop's raw environmental data. The original environmental data of the workshop was rasterized, dividing the entire silver electrolysis workshop into a regular two-dimensional raster model. The basic cell size was set to 10 cm × 10 cm to ensure sufficient spatial resolution for path planning. The core of the rasterization process is to transform the continuous physical space of the workshop into a discrete two-dimensional grid model. Each cell corresponds to an actual spatial area in the workshop, thereby transforming complex environmental information into structured data that can be processed by computers. The attributes of each cell are determined by the environmental data and are accomplished through attribute labeling. Attribute labeling is a key step in classifying the raster model according to the characteristics of different areas within the workshop. Based on the actual environmental information, each cell is assigned different attribute labels, including labels for free areas, static obstacles, electrolytic cell areas, cathode plate storage areas, anode plate storage areas, short-circuit plate storage areas, passage areas, and areas with high concentrations of corrosive gases. Free areas represent areas where AGV transport vehicles can pass normally. Static obstacle areas are marked as impassable areas. Electrolytic cell areas require separate marking due to their involvement in critical process operations. Cathode and anode plate storage areas are key locations for transport vehicles to retrieve and place plates, and path planning for these areas requires special optimization. Short-circuit plate storage areas are areas that need attention in path planning, while passageways are the main travel paths for AGV transport vehicles, and their unobstructed flow must be ensured. Areas with high concentrations of corrosive gases are specially marked by the system, and transport vehicles should avoid these areas as much as possible during subsequent path planning to extend equipment lifespan and ensure operational safety. The marked grid data is converted into a workshop topology map. By mapping the passable area nodes in the grid data to a set of vertices in the topology map, and mapping the passage paths between adjacent nodes to a set of edges, a weighted directed graph is formed.During the generation of the topology map, the weight of each edge is precisely assigned based on a combination of factors, including the actual distance of the path segment, the corrosion coefficient of the area, and the friction coefficient. The actual distance of the path segment depends on the Euclidean distance between adjacent nodes. The corrosion coefficient is assigned based on the corrosion level of the area where the path is located; areas with high corrosion are given higher weights to prioritize avoiding them during path planning. The friction coefficient is allocated based on the ground material and the characteristics of the path segment; for example, the weight is further increased in slippery areas or areas with high friction coefficients, guiding the transport vehicle to choose the path with the lowest energy consumption. This weighting mechanism dynamically adjusts the path weights during subsequent path search, selecting the optimal path based on the characteristics of different areas. Based on the weighted edge set, all workstations are marked to form an electronic map of the silver electrolysis process workshop. Workstations include electrolytic cell plate retrieval stations, cathode plate storage stations, and anode plate storage stations. Electrolytic cell plate retrieval stations are the starting points for AGV transport vehicles to retrieve cathode or anode plates from the electrolytic cell. The locations of these stations need to be precisely marked to ensure the transport vehicles can accurately perform their retrieval tasks. Cathode plate storage stations are the target locations for the transport vehicles to complete cathode plate transportation, and are also clearly marked on the electronic map. Anode plate storage stations are critical stations used for storing and transporting anode plates, and are prioritized for identification in path planning. By mapping these work stations to the vertex set of the topology graph, work station nodes are formed, ensuring that the start and end points are quickly located based on the task type during path planning.

[0043] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0044] Based on the real-time location and task requirements of the AGV transport vehicle in the silver electrolysis process workshop, the starting position and target workstation of the transport vehicle are determined, and the coordinates of the starting and ending points of the path planning are obtained.

[0045] Based on the coordinates of the start and end points, a priority queue is constructed on the electronic map of the silver electrolysis process workshop. The starting position node is added to the queue and the path cost is initialized to obtain the initial state of the path search.

[0046] The node with the lowest replacement value is selected from the priority queue for expansion. The path cost of adjacent nodes is calculated to obtain a set of candidate nodes for the path.

[0047] Different travel cost coefficients are assigned to nodes in the path candidate node set according to the type of the region they are in, and a turning penalty factor is introduced to adjust the path cost, so as to obtain the corrected node cost.

[0048] The safety of each candidate path is checked based on the corrected node cost value to obtain a set of paths that meet the safety constraints. A comprehensive score is then calculated based on the set of paths that meet the safety constraints, and the path with the highest score is selected as the initial optimal path.

[0049] Specifically, the AGV (Automated Guided Vehicle) positioning system, combined with the task management module, obtains the current position of the AGV and determines its target position based on the current task requirements. The positioning system employs a multi-sensor fusion positioning method combining LiDAR, a visual recognition system, and an IMU (Inertial Measurement Unit). By matching the positioning with the electronic map of the silver electrolysis workshop, the real-time coordinates of the AGV are determined. Simultaneously, the task management module automatically assigns task targets based on the current task type (e.g., transporting cathode or anode plates), using the coordinates of the target workstation as the endpoint for path planning. The coordinates of the starting and target positions, after being parsed by the system, serve as input to the path planning algorithm, providing start and end point coordinates for subsequent path searching. Based on these coordinates, a priority queue is constructed on the electronic map of the silver electrolysis workshop. This priority queue is a data structure used to store nodes to be processed during path searching. Its core function is to sort nodes according to their path cost, ensuring that nodes with the lowest cost are processed first. The starting position node is added to the priority queue, and its path cost is initialized to zero, representing that the cost from the starting point to itself is zero, thus obtaining the initial state of the path search. During path search, a priority queue automatically sorts nodes to be processed according to their path cost, ensuring that the node with the lowest cost is selected first in each expansion, thus achieving optimal path search. Specifically, the node with the lowest cost is selected from the priority queue for expansion; that is, the node with the lowest current path cost is chosen as the expansion node. The path costs of its neighboring nodes are calculated, generating a set of candidate nodes. Candidate nodes are the neighbors of the current node, representing all possible path points reachable through a single step expansion. The system calculates the cost of candidate nodes based on the current node's location, the locations of neighboring nodes, and path characteristics, and adds them to the candidate node set. For each candidate node, its path cost is evaluated to ensure that the best path is prioritized during path search, thereby continuously approaching the target location. Different travel cost coefficients are assigned to nodes in the candidate node set based on their location type. These coefficients reflect the difficulty and energy consumption of travel in different areas. For example, the travel cost coefficient is lower in passageways, while it is higher in electrolytic cell areas or cathode plate storage areas, to avoid frequent travel by transport vehicles in these complex areas. A turning penalty factor is introduced to adjust the path cost. When a turn occurs during path expansion, the turning penalty factor is calculated based on the turning angle; the larger the turning angle, the higher the penalty factor, encouraging the path planning algorithm to select smoother paths and avoid unnecessary sharp turns. After path cost correction, the corrected node cost value is obtained, reflecting the actual cost of the candidate path. The safety of each candidate path is then checked based on the corrected node cost value to ensure that the path complies with the safety constraints of the silver electrolysis process workshop.Safety verification encompasses multiple factors, such as the minimum safe distance between the detection path and static obstacles, whether the detection path passes through areas with high concentrations of corrosive gases, and the existence of other potential safety risk areas. Candidate paths are screened based on safety constraints, and paths meeting the safety requirements are retained, resulting in a set of paths that satisfy the safety constraints. A comprehensive score is then calculated based on this set of paths, using criteria including path length, path cost, driving smoothness, obstacle avoidance success rate, and energy consumption optimization. By weighted summing of the scores from different dimensions, a comprehensive score is calculated for each candidate path, and the path with the highest score is selected as the initial optimal path.

[0050] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0051] Real-time driving data is collected by the sensor group installed on the AGV transport vehicle to obtain the raw data of the AGV transport vehicle's driving status. The sensor group includes distance sensor data, vision recognition system data and torque sensor data.

[0052] The raw data of the AGV transport vehicle's driving status is segmented according to the path segment to obtain segmented driving parameters;

[0053] The momentum residual value of each path segment is calculated based on the segmented driving parameters to obtain the momentum residual data of the path segment. The momentum residual value is equal to the absolute value of the difference between the mass of the AGV transport vehicle multiplied by the average speed and the product of the applied force and time.

[0054] The momentum residual data of the path segment is compared with the preset momentum residual threshold. If the momentum residual value exceeds the momentum residual threshold, the path segment is marked as an abnormal path segment, and a set of abnormal path segments is obtained.

[0055] The adaptive learning rate is calculated based on the momentum residual value of each path segment in the abnormal path segment set to obtain the parameter update coefficient. Then, the ground friction coefficient of each path segment in the abnormal path segment set is corrected based on the parameter update coefficient to obtain the path correction parameter.

[0056] Specifically, the AGV transporter integrates multiple types of high-precision sensors, including distance sensors, a vision recognition system, and torque sensors. These sensors are used to collect real-time information about the workshop environment, path travel data, and dynamic state data during the transporter's transport tasks. The distance sensors detect the positions of obstacles around the transporter, path boundaries, and changes in distance to moving objects. This data helps the system determine the spatial accessibility of the current path segment. The vision recognition system identifies the positions of obstacles, electrolytic cells, cathode plates, anode plates, and pipelines in the area ahead by acquiring and analyzing real-time images of the workshop environment, and determines whether any abnormalities have occurred in the current path segment. Simultaneously, the torque sensors detect real-time changes in friction and resistance between the transporter and the ground, as well as torque changes during acceleration or deceleration, acquiring force state data of the transporter during travel. All this data is integrated through a multi-sensor data fusion system and, under the coordination of a time synchronization module, forms the raw data of the AGV transporter's travel status. The system segments the raw data of the AGV transporter's travel status according to path segments, precisely dividing the data of the transporter's travel on different path segments. Each path segment corresponds to an independent spatial area or path interval traversed by the transporter within the silver electrolysis process workshop. Segmentation is a key step in path analysis. The path segment identification module divides the travel data of different areas. The data for each path segment includes parameters such as travel time, average speed, friction changes, and torque changes. This segmented data provides input for subsequent momentum residual calculation. Based on the topology information and start and end point coordinates of the path segments, the system splits the raw travel data into multiple independent path segments and stores the parameters of each path segment in a path segment data set for subsequent calculations. After the path segment data is segmented, the momentum residual value of each path segment is calculated based on the segmented travel parameters. The momentum residual is a key indicator for measuring the degree of abnormality in the force state of the transporter during travel. The calculation method is to multiply the mass of the AGV transporter by the average speed of the path segment, and then compare it with the force-time product of the path segment, calculating the absolute value of the difference between the two. The momentum residual value reflects the deviation between the actual and theoretical resistance of the transport vehicle on a path segment. A large momentum residual value indicates a significant resistance variation on the path segment. The momentum residual data of the path segment is compared with a preset momentum residual threshold, which is set based on the environmental characteristics of the silver electrolysis workshop, the dynamic characteristics of the transport vehicle, and the momentum variation range under normal operating conditions. When the system detects that the momentum residual value of a certain path segment exceeds the preset threshold, the path segment is marked as an abnormal path segment and recorded in the abnormal path segment set. The causes of the abnormality are analyzed based on the abnormal path segment set, and the friction coefficient of the path segment is dynamically adjusted. Abnormal path segments are concentrated in high-friction areas, path blockage points, or corrosive areas, which can easily lead to increased energy consumption and decreased driving stability during transport vehicle operation.An adaptive learning rate is calculated based on the momentum residual values ​​of each path segment in the abnormal path segment set. This adaptive learning rate is a dynamically adjusted parameter that adjusts the friction coefficient correction step size of the path segment according to the magnitude of the momentum residual. The calculation of the learning rate follows an exponential decay mechanism: when the momentum residual value is large, the learning rate is high, thus accelerating the correction speed of the path segment's friction coefficient; conversely, when the momentum residual is small, the learning rate gradually decreases to ensure the stability of the path correction. By adjusting the adaptive learning rate, the correction speed of the path segment's friction coefficient is ensured to remain consistent with the change in momentum residual, thereby improving the accuracy and efficiency of path optimization. The ground friction coefficient of each path segment in the abnormal path segment set is corrected based on the parameter update coefficient. By dynamically updating the friction coefficient, the influence of abnormal path segment resistance is eliminated, thereby optimizing the path planning effect. Based on the magnitude of the momentum residual of the abnormal path segment and the driving parameters of the path segment, the friction coefficient is adaptively updated. Through repeated iterative corrections, the friction coefficient of the path segment tends to stabilize, forming the path correction parameters. These corrective parameters are fed back into the electronic map of the silver electrolysis workshop to update the path weights of the corresponding areas. This guides the AGV transport vehicles to choose better paths and avoid high-friction or abnormally resistant areas during subsequent path planning.

[0057] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0058] The path correction parameters are fed back to the electronic map of the silver electrolysis process workshop, and the edge weights of the corresponding areas are corrected to obtain the updated electronic map weight data.

[0059] Based on the updated electronic map weight data and the initial optimal path, the set of path segments to be adjusted is identified;

[0060] Each path segment in the set of path segments to be adjusted is smoothed to bypass areas with high corrosion and high friction coefficients, resulting in locally optimized path segments.

[0061] By minimizing the energy consumption objective function, control points of the local optimization path segment are selected to obtain the local path with optimal energy consumption. Collision detection is then performed on the local path with optimal energy consumption to verify it and obtain a safe local path segment.

[0062] By reconnecting the safe local path segments with the unmodified path segments in the initial optimal path, an energy-saving and safe path is obtained.

[0063] Specifically, the path correction parameters identified by the momentum residual detection module are fed back to the electronic map system in the silver electrolysis process workshop, and the weighted edge set in the electronic map is dynamically updated. The path correction parameters include the friction coefficient, regional corrosion coefficient, and abnormal path resistance information for each path segment. These parameters directly affect the calculation of edge weights in path planning; therefore, the weights of the corresponding path segments are corrected based on the path correction parameters. The core of weight correction is to appropriately increase the weight values ​​of high-friction areas, high-corrosion areas, and areas with abnormal path resistance, thereby guiding AGV transport vehicles to prioritize avoiding these unfavorable areas during path planning. The updated electronic map weight data reflects the latest workshop environment status. Based on the updated electronic map weight data and the initial optimal path, the set of path segments to be adjusted is identified. The initial optimal path is a path generated based on the original electronic map. Due to changes in the path correction parameters, some path segments are no longer the most energy-efficient or safest paths; therefore, these path segments are re-identified. The updated path cost is calculated by the path segment analysis module, and path segments with abnormal costs are marked as needing adjustment. These path segments are located in highly corrosive areas, areas with varying friction coefficients, or areas with abnormal resistance. The path cost increases significantly in these areas, directly affecting the operating efficiency and safety of the AGV transporter. Each path segment in the set of path segments to be adjusted is smoothed to ensure the smoothness and safety of the transporter when avoiding highly corrosive and high-friction areas. Path smoothing uses B-spline curves or Bezier curves, and optimizes the path curve by introducing control point adjustments to achieve smooth transitions between path segments. The core objective of path smoothing is to ensure the continuity and smoothness of the path, avoiding sharp turns or path fluctuations in highly corrosive or high-friction areas, reducing energy consumption, and improving the stability of the transporter during path execution. During the path smoothing process, multiple candidate path schemes are generated based on the spatial distribution characteristics of the path segments. The candidate path evaluation module comprehensively compares the smoothness, turning angle, and energy consumption levels of different candidate paths, selecting the optimal path smoothing scheme to obtain the locally optimized path segment. By minimizing the energy consumption objective function, control points are selected for locally optimized path segments, ensuring that the path achieves optimal energy consumption. Minimizing the energy consumption objective function is the core constraint of path optimization. Its optimization goal is to reduce the path length, decrease frictional resistance, and minimize turning penalties by adjusting the control point positions of path segments, thereby minimizing the energy consumption of the transport vehicle. During the optimization process, the system iteratively adjusts the control points of candidate path segments and calculates the path energy consumption under different control point configurations using a path cost evaluation model, selecting the locally optimal path segment. Collision detection is then performed on the locally optimal path to ensure that the path segment will not collide with obstacles or equipment in a real-world environment.Collision detection employs a time-based dynamic window method combined with a static obstacle detection algorithm. During detection, dynamic simulation is performed based on the motion model of the transport vehicle, the control points of the path segment, and the positions of surrounding obstacles to ensure that the transport vehicle does not collide with static or dynamic obstacles while traveling along the optimized path segment. Simultaneously, the minimum safe distance of the path segment is checked to ensure that a safe distance is always maintained between the transport vehicle and obstacles. When a potential collision risk is detected in a path segment, the system automatically makes local adjustments to the path segment and re-performs collision detection until a safe local path segment is obtained. This safe local path segment is then reconnected with the unmodified path segments in the initial optimal path to form an energy-efficient and safe path. The path segment reconnection module seamlessly stitches the path segments according to their spatial location and time sequence, ensuring a smooth transition between connection points and eliminating speed abrupt changes or directional shifts during the stitching process. The total path length, travel time, and estimated energy consumption are recalculated based on the stitched path segments to ensure that the stitched energy-efficient and safe path achieves optimal overall performance.

[0064] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0065] Based on the multi-directional distance sensors, vision recognition system and torque sensor on the AGV transport vehicle, real-time environmental data is collected to identify the position and speed of moving obstacles ahead and the location of electrolyte leakage areas, thereby obtaining real-time obstacle information;

[0066] Based on real-time obstacle information and the current position of the AGV transport vehicle, the combined potential field function in the silver electrolysis workshop environment is established;

[0067] The negative gradient value is calculated for the resultant potential field function, and the direction of the negative gradient is used as the temporary travel direction of the AGV transport vehicle to obtain the basic obstacle avoidance direction.

[0068] Based on the current state and dynamic constraints of the AGV transport vehicle, a speed candidate set is constructed, and speed score data is calculated for each speed combination in the speed candidate set;

[0069] Based on the speed score data, the speed combination with the highest score is selected to obtain real-time obstacle avoidance control instructions. When the original path is no longer feasible, the path is replanned in the local area to generate a temporary detour path.

[0070] Specifically, relying on a multi-sensor data fusion system, multi-directional distance sensors integrated on the AGV transport vehicle monitor the distance, shape, and positional changes of obstacles in front of and around the vehicle in real time. Simultaneously, a vision recognition system uses image processing algorithms to classify and track obstacles, identifying their position, speed, and direction of movement. Torque sensors detect torque fluctuations caused by road surface changes or abnormal resistance during the transport vehicle's movement, thus aiding in determining the location of electrolyte leak areas. Through the synergistic effect of multi-sensor data, real-time obstacle information is acquired, including the spatial coordinates, speed, trajectory of obstacles, and the extent and location of leak areas. Based on the real-time obstacle information, the current position of the AGV transport vehicle, and the characteristics of the workshop environment, a resultant potential field function is constructed. This function consists of both an attractive field and a repulsive field. The attractive field, with the target location as the attraction source, generates an attractive force that guides the transport vehicle towards the target direction, while the repulsive field generates a repulsive force around obstacles and hazardous areas, forcing the transport vehicle away from these areas. For electrolyte leak areas, a stronger repulsive field is set to ensure that the transport vehicle prioritizes avoiding these areas during path adjustments. The establishment of the combined potential field fully considers the distance between the transport vehicle and obstacles, the moving speed of the obstacles, and the complexity of the path. Simultaneously, it dynamically adjusts the potential field strength according to the characteristics of different areas within the workshop, enabling the system to generate reasonable obstacle avoidance paths in real time in complex environments. As the position of the transport vehicle changes, the potential field function is continuously updated to ensure the real-time nature and accuracy of obstacle avoidance decisions. After constructing the combined potential field function, the negative gradient value of the function is calculated, and the direction of the negative gradient is used as the temporary travel direction of the AGV transport vehicle, generating the basic obstacle avoidance direction. The negative gradient direction refers to the direction of the fastest descent along the potential field function. This means that in a real-world environment, this direction can effectively avoid obstacles and hazardous areas while maintaining guidance for the target position. By calculating the negative gradient value in real time, the travel direction of the transport vehicle is quickly adjusted, ensuring a stable and reasonable path throughout the obstacle avoidance process. Even when facing dynamic obstacles or sudden path changes, the calculation of the negative gradient direction still provides rapid travel guidance, helping the transport vehicle make obstacle avoidance decisions in the shortest possible time. Based on the determined basic obstacle avoidance direction, the current state of the transport vehicle and dynamic constraints are considered to construct a candidate speed set. The status of the transport vehicle includes information such as its current position, speed, acceleration, and steering angle, while dynamic constraints involve parameters such as the vehicle's maximum speed, acceleration limits, and minimum turning radius. Under these constraints, the system generates multiple possible speed combinations, each consisting of linear velocity and angular velocity, forming a candidate speed set. By considering the impact of different speed combinations on the transport vehicle's stability and obstacle avoidance capabilities, the system provides the transport vehicle with greater degrees of freedom while maintaining safety, enabling flexible adjustments during obstacle avoidance.After generating a candidate speed set, a speed score is calculated for each speed combination within the set. The scoring criteria include multiple dimensions such as obstacle avoidance efficiency, path deviation, driving smoothness, and energy consumption optimization. Obstacle avoidance efficiency primarily measures the success rate of the transport vehicle avoiding obstacles after selecting a specific speed combination; path deviation assesses the degree of deviation of the transport vehicle from the original path during obstacle avoidance; driving smoothness considers the smoothness of the transport vehicle during turning, acceleration, and deceleration; and energy consumption optimization focuses on the energy consumption performance of the transport vehicle under different speed combinations. The system performs a weighted summation of the score data for all speed combinations to calculate a comprehensive score for each speed combination. Based on the speed score data, the speed combination with the highest score is selected as the current real-time obstacle avoidance control command. This command includes specific linear and angular velocity control values ​​and is sent to the transport vehicle's control system for execution. During the execution of the obstacle avoidance control command, the system monitors the position changes of obstacles and the driving status of the transport vehicle in real time to ensure the effectiveness and safety of the obstacle avoidance path. When the transport vehicle avoids the obstacle and returns to the normal path, the system automatically stops the obstacle avoidance mode and switches back to the original path planning command. If the system detects that the original path is no longer feasible due to obstacles or sudden environmental changes, it triggers a local path replanning module to replan the path within a local area, generating a temporary detour path. The goal of local path replanning is to find a safe path for the transport vehicle to avoid obstacles in the shortest possible time, while minimizing path length and travel time. Based on the current electronic map and real-time obstacle information, the system reconstructs the path search space and uses dynamic programming or A* algorithms for path searching. To ensure the rationality of the temporary detour path, the path is smoothed to avoid sharp turns and discontinuous paths. Furthermore, collision detection and safety verification are performed to ensure that the generated temporary path does not conflict with other obstacles.

[0071] In one specific embodiment, the path planning method for AGV transport vehicles used in a silver electrolysis process workshop further includes the following steps:

[0072] Based on the silver electrolysis process, the AGV handling task is divided into cathode plate handling task and anode plate handling task. One leader AGV and multiple follower AGVs are assigned to the cathode plate handling task and the anode plate handling task respectively, resulting in a binary task structure.

[0073] A leader-follower architecture control model is constructed based on a binary task structure, and intra-group collaborative control strategies are generated based on the leader-follower architecture control model.

[0074] A task sequence is constructed for all handling tasks in the silver electrolysis process workshop, and the task sequence is input into the Hungarian algorithm for task allocation to obtain the AGV execution plan;

[0075] Spatiotemporal conflict detection is performed on the execution plans of each AGV to obtain conflict-free execution schemes;

[0076] Based on a conflict-free execution scheme, an event-triggered task reassignment mechanism is designed. When a task anomaly, AGV failure, or path blockage is detected, local replanning is performed to obtain a global collaborative path planning scheme.

[0077] Specifically, the handling tasks are divided according to the production task characteristics of the silver electrolysis process. The cathode plate handling task mainly involves removing the cathode plates from the electrolytic cell and transporting them to the cathode plate storage area, while the anode plate handling task includes delivering new anode plates to the electrolytic cell and replacing them. These two types of tasks have different process requirements and handling paths; therefore, a binary task structure is constructed based on the task type. In this binary task structure, each type of task is assigned a leader AGV and multiple follower AGVs to jointly complete the task. The leader AGV is responsible for path planning, target identification, and task status updates, while the follower AGVs perform motion control based on the path and speed commands of the leader AGV, achieving efficient coordination of the multi-vehicle queue. After constructing the binary task structure, multi-AGV collaborative control is achieved based on the leader-follower architecture control model. The leader-follower architecture ensures the stability and consistency of queue operation by establishing relative position information, speed matching, and path synchronization mechanisms between the leader AGV and follower AGVs. The leader AGV obtains its own position information, speed status, and task progress in real time and sends this data as control signals to the follower AGVs. The following AGVs adaptively adjust based on the distance deviation, speed difference, and path angle difference with the leader AGV, ensuring a tight and stable collaborative state for the entire queue. The system uses a queue control algorithm to adjust the distance between the following and leader AGVs, ensuring no queue separation or collisions occur during path turns, acceleration, and deceleration. A time delay compensation mechanism addresses control lag caused by communication delays, generating an intra-group collaborative control strategy to guarantee the stability and efficiency of multi-vehicle task execution. A task sequence is constructed for all handling tasks in the silver electrolysis workshop. The task sequence generation is based on multiple factors, including task priority, task completion time window, path length, energy consumption estimation, and equipment status. The system sorts all handling tasks according to these factors and constructs a task sequence matrix. This matrix maps the priority, execution time, and resource requirements of each task to provide reference data for task allocation. The task sequences are then input into the Hungarian algorithm for task allocation. The Hungarian algorithm is an optimal allocation algorithm that achieves optimal matching in complex environments with multiple tasks and resources. By taking the task allocation matrix as input, the Hungarian algorithm solves for the optimal task allocation based on task execution cost, time constraints, and resource load, generating an AGV execution plan. The execution plan includes the task start point, target location, execution order, and estimated completion time for each AGV, ensuring all tasks are completed on time and that workshop resources are fully utilized. Spatiotemporal conflict detection is performed on each AGV execution plan to ensure that resource contention caused by path conflicts or time overlaps does not occur during multi-vehicle collaborative operations. Spatiotemporal conflict detection includes path conflict detection, task time overlap detection, and dynamic obstacle prediction, among other aspects.The system cross-checks the execution path of each AGV with the paths of other AGVs and calculates the time difference between intersecting path segments. If the time difference is lower than a set safety threshold, a conflict is considered to exist. Furthermore, the system performs overlap detection on the start and end times of each task to ensure that the same area or equipment is not occupied by different AGVs at the same time, resulting in a conflict-free execution plan. After the spatiotemporal conflict detection is completed, path adjustments or task time fine-tuning are made based on the detection results to ensure that all AGVs maintain spatial and temporal independence during execution, improving the overall coordination of the handling tasks. An event-triggered task redistribution mechanism is designed based on the conflict-free execution plan. This mechanism is used to handle unexpected situations during task execution, such as task anomalies, AGV malfunctions, or path blockages. The event-triggered mechanism continuously monitors the operating status of AGVs through a real-time monitoring module. When the system detects an anomaly in a task or a malfunction in an AGV, it automatically triggers the task redistribution mechanism. The task redistribution mechanism reassesses the current task status and remaining resources, analyzes the priority of affected tasks, incomplete paths, and the idle level of available AGVs, and re-invokes the Hungarian algorithm for local task redistribution based on the new task allocation matrix. During local task redistribution, abnormal tasks are prioritized for assignment to the nearest backup AGV to ensure task resumption in the shortest possible time. When path congestion is detected, the system initiates a local path replanning module to dynamically search the affected path segments and generate a new temporary detour path, ensuring the AGV can bypass obstacles and continue completing the task. Local path replanning employs either the A* algorithm or Dijkstra's algorithm for path searching, combined with a dynamic obstacle avoidance mechanism to ensure a balance between obstacle avoidance and driving efficiency in the new path. After task redistribution and path replanning are completed, a global collaborative path planning scheme is generated. This scheme includes the redistribution results of all tasks, as well as the new execution plan after path adjustment and conflict detection results. By reintegrating all task status and path information, the global collaborative path planning scheme ensures efficient collaborative operation of multiple AGVs in complex workshop environments. Even in the event of task anomalies or path congestion, it can quickly make emergency adjustments, maintaining the continuity and reliability of task execution.

[0078] The path planning method for AGV transport vehicles in a silver electrolysis process workshop, as described above in the embodiments of this application, is now described below. Please refer to the path planning system for AGV transport vehicles in a silver electrolysis process workshop, as described below. Figure 2 One embodiment of the path planning system for AGV transport vehicles in a silver electrolysis process workshop, as described in this application, includes:

[0079] Module 201 is used to collect environmental features and construct an electronic map of the silver electrolysis process workshop to obtain an electronic map of the silver electrolysis process workshop.

[0080] Calculation module 202 is used to calculate the initial optimal path of the AGV transport vehicle from its current position to the target workstation based on the electronic map of the silver electrolysis process workshop;

[0081] The detection module 203 is used to detect the actual driving resistance during the execution of the initial optimal path. When the abnormal value of the path segment resistance exceeds the momentum residual threshold, the actual friction coefficient and obstacle conditions of the path segment are recorded to obtain the path correction parameters.

[0082] The local adjustment module 204 is used to locally adjust the initial optimal path according to the path correction parameters by bypassing high corrosion areas and high friction coefficient areas, so as to obtain an energy-saving and safe path.

[0083] The real-time monitoring module 205 is used to monitor moving obstacles and electrolyte leakage areas in real time during the AGV transport vehicle's execution of an energy-saving and safe path, and to calculate the temporary obstacle avoidance direction and speed to generate a temporary detour path.

[0084] Through the collaborative efforts of the aforementioned components, and by collecting environmental characteristics of the silver electrolysis workshop and constructing a refined electronic map, the system accurately distinguishes between electrolytic cell areas, corrosive gas areas, and densely populated equipment areas. This provides AGV transport vehicles with detailed environmental information, making path planning more aligned with the actual environmental characteristics of the silver electrolysis workshop. A comprehensive scoring mechanism is employed to calculate the initial optimal path, considering path length, corrosive environment exposure, and equipment collision risk. This reduces the AGV transport vehicles' dwell time in highly corrosive areas, extends equipment lifespan, and lowers maintenance costs. Dynamic detection of actual path travel resistance and recording of correction parameters enable real-time adaptation to changes in the silver electrolysis workshop's ground conditions, making the path planning scheme more suitable for the complex and ever-changing environment. Local adjustments are made based on path correction parameters to bypass highly corrosive and high-friction areas, reducing energy consumption and improving the AGV transport vehicles' operating efficiency and safety in the silver electrolysis workshop. Real-time monitoring of moving obstacles and electrolyte leakage areas ahead calculates temporary obstacle avoidance directions and speeds, enabling the AGV transport vehicles to respond promptly to emergencies in the silver electrolysis workshop, ensuring equipment safety and production continuity. By using a binary task structure and a leader-follower architecture, multiple AGV transport vehicles can work collaboratively, optimizing the handling process of cathode and anode plates, reducing the empty load rate of AGV transport vehicles, and improving the overall logistics efficiency of the silver electrolysis workshop.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0086] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a path planning device (which can be a personal computer, server, or network device, etc.) for an AGV transport vehicle used in a silver electrolysis process workshop to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A path planning method for AGV transport vehicles used in a silver electrolysis process workshop, characterized in that, include: Environmental features were collected and an electronic map was constructed for the silver electrolysis process workshop, resulting in an electronic map of the silver electrolysis process workshop; The initial optimal path for the AGV transport vehicle from its current location to the target workstation is calculated based on the electronic map of the silver electrolysis process workshop. The actual driving resistance during the execution of the initial optimal path is detected. When the abnormal value of the path segment resistance exceeds the momentum residual threshold, the actual friction coefficient and obstacle conditions of the path segment are recorded to obtain path correction parameters. This includes: collecting real-time driving data through a sensor group installed on the AGV transport vehicle to obtain raw data of the AGV transport vehicle's driving status; the sensor group includes distance sensor data, vision recognition system data, and torque sensor data; segmenting the raw data of the AGV transport vehicle's driving status according to the path segment to obtain segmented driving parameters; and calculating the momentum residual of each path segment based on the segmented driving parameters. The difference is used to obtain the momentum residual data of the path segment, where the momentum residual value is equal to the absolute value of the difference between the mass of the AGV transport vehicle multiplied by the average speed and the product of the force and time. The momentum residual data of the path segment is compared with a preset momentum residual threshold. If the momentum residual value exceeds the momentum residual threshold, the path segment is marked as an abnormal path segment, and an abnormal path segment set is obtained. The adaptive learning rate is calculated based on the momentum residual values ​​of each path segment in the abnormal path segment set to obtain the parameter update coefficient. Based on the parameter update coefficient, the ground friction coefficient of each path segment in the abnormal path segment set is corrected to obtain the path correction parameter. Based on the path correction parameters, the initial optimal path is locally adjusted by bypassing high-corrosion and high-friction coefficient regions to obtain an energy-saving and safe path. During the execution of the energy-saving and safe path by the AGV transport vehicle, the moving obstacles and electrolyte leakage areas ahead are monitored in real time, and the temporary obstacle avoidance direction and speed are calculated to generate a temporary detour path.

2. The path planning method for AGV transport vehicles in a silver electrolysis process workshop according to claim 1, characterized in that, The process of collecting environmental features and constructing an electronic map of the silver electrolysis process workshop, resulting in an electronic map of the silver electrolysis process workshop, includes: Based on the physical layout of the silver electrolysis process workshop, static obstacle location information, equipment layout information, process pipeline location information, and work area distribution information are collected to obtain the original environmental data of the workshop. The original environmental data of the workshop is rasterized to obtain a workshop raster model; each cell in the workshop raster model is labeled with attributes to obtain labeled raster data. The attribute labels include free areas, static obstacles, electrolytic cell areas, cathode plate storage areas, anode plate storage areas, short-circuit plate storage areas, channel areas, and areas with high concentrations of corrosive gases. The marked raster data is converted into a workshop topology diagram, and each edge in the workshop topology diagram is assigned a weight value based on the actual distance, the corrosion coefficient of the area, and the friction coefficient to obtain a weighted edge set; Based on the weighted edge set, all work stations are marked to obtain an electronic map of the silver electrolysis process workshop. The work stations include electrolytic cell plate retrieval stations, cathode plate storage stations, and anode plate storage stations.

3. The path planning method for AGV transport vehicles in a silver electrolysis process workshop according to claim 1, characterized in that, The calculation of the initial optimal path for the AGV transport vehicle from its current location to the target workstation based on the electronic map of the silver electrolysis process workshop includes: Based on the real-time location and task requirements of the AGV transport vehicle in the silver electrolysis process workshop, the starting position and target workstation of the transport vehicle are determined, and the coordinates of the starting and ending points of the path planning are obtained. Based on the starting and ending point coordinates, a priority queue is constructed on the electronic map of the silver electrolysis process workshop. The starting position node is added to the queue and the path cost is initialized to obtain the initial state of path search. The node with the lowest replacement value is selected from the priority queue for expansion, and the path cost of adjacent nodes is calculated to obtain a set of candidate nodes for the path. Different travel cost coefficients are assigned to nodes in the path candidate node set according to the type of the region they are in, and a turning penalty factor is introduced to adjust the path cost, so as to obtain the corrected node cost. The safety of each candidate path is checked based on the corrected node cost value to obtain a set of paths that meet the safety constraints. A comprehensive score is then calculated based on the set of paths that meet the safety constraints, and the path with the highest score is selected as the initial optimal path.

4. The path planning method for AGV transport vehicles in a silver electrolysis process workshop according to claim 1, characterized in that, The step of locally adjusting the initial optimal path based on the path correction parameters, by bypassing high-corrosion and high-friction coefficient regions, to obtain an energy-saving and safe path includes: The path correction parameters are fed back to the electronic map of the silver electrolysis process workshop to correct the edge weights of the corresponding areas, thereby obtaining updated electronic map weight data; based on the updated electronic map weight data and the initial optimal path, a set of path segments to be adjusted is identified. Each path segment in the set of path segments to be adjusted is smoothed to bypass areas with high corrosion and high friction coefficient, resulting in locally optimized path segments. By minimizing the energy consumption objective function, the control points of the local optimized path segment are selected to obtain the local path with the optimal energy consumption. Collision detection is then performed on the local path with the optimal energy consumption to verify it and obtain a safe local path segment. The safe local path segment is reconnected with the unmodified path segment in the initial optimal path to obtain an energy-saving and safe path.

5. The path planning method for AGV transport vehicles in a silver electrolysis process workshop according to claim 1, characterized in that, During the process of the AGV transport vehicle executing the energy-saving and safe path, the system monitors moving obstacles and electrolyte leakage areas ahead in real time, calculates temporary obstacle avoidance direction and speed, and generates a temporary detour path, including: Based on the multi-directional distance sensor, vision recognition system and torque sensor on the AGV transport vehicle, real-time environmental data is collected to identify the position and speed of moving obstacles ahead and the location of electrolyte leakage areas, thereby obtaining real-time obstacle information. Based on the real-time obstacle information and the current position of the AGV transport vehicle, the combined potential field function in the silver electrolysis workshop environment is established; The negative gradient value is calculated for the combined potential field function, and the direction of the negative gradient is used as the temporary travel direction of the AGV transport vehicle to obtain the basic obstacle avoidance direction. Based on the current state and dynamic constraints of the AGV transport vehicle, a speed candidate set is constructed, and speed score data is calculated for each speed combination in the speed candidate set. Based on the speed score data, the speed combination with the highest score is selected to obtain real-time obstacle avoidance control instructions. When the original path is no longer feasible, path planning is re-performed in the local area to generate a temporary detour path.

6. The path planning method for AGV transport vehicles in a silver electrolysis process workshop according to claim 1, characterized in that, The path planning method for AGV transport vehicles used in silver electrolysis process workshops also includes: Based on the silver electrolysis process, the AGV handling task is divided into cathode plate handling task and anode plate handling task. One leader AGV and multiple follower AGVs are assigned to the cathode plate handling task and the anode plate handling task respectively, resulting in a binary task structure. A leader-follower architecture control model is constructed based on the aforementioned binary task structure, and an intra-group collaborative control strategy is generated based on the leader-follower architecture control model. A task sequence is constructed for all handling tasks in the silver electrolysis process workshop, and the task sequence is input into the Hungarian algorithm for task allocation to obtain the AGV execution plan; Spatiotemporal conflict detection is performed on the execution plans of each AGV to obtain conflict-free execution schemes; Based on the conflict-free execution scheme, an event-triggered task reassignment mechanism is designed. When a task abnormality, AGV failure, or path blockage is detected, local replanning is performed to obtain a global collaborative path planning scheme.

7. A path planning system for AGV (Automated Guided Vehicle) transport vehicles used in a silver electrolysis process workshop, characterized in that, For implementing the path planning method for AGV transport vehicles in a silver electrolysis process workshop as described in any one of claims 1 to 6, the path planning system for AGV transport vehicles in a silver electrolysis process workshop comprises: The module is used to collect environmental features and build an electronic map of the silver electrolysis process workshop, resulting in an electronic map of the silver electrolysis process workshop. The calculation module is used to calculate the initial optimal path for the AGV transport vehicle from its current position to the target workstation based on the electronic map of the silver electrolysis process workshop. The detection module is used to detect the actual driving resistance during the execution of the initial optimal path. When the abnormal value of the path segment resistance exceeds the momentum residual threshold, the actual friction coefficient and obstacle conditions of the path segment are recorded to obtain the path correction parameters. The local adjustment module is used to locally adjust the initial optimal path according to the path correction parameters by bypassing high corrosion areas and high friction coefficient areas to obtain an energy-saving and safe path. The real-time monitoring module is used to monitor moving obstacles and electrolyte leakage areas in real time during the process of the AGV transport vehicle executing the energy-saving and safe path, and to calculate the temporary obstacle avoidance direction and speed, and generate a temporary detour path.

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