A safety cooperative control method for same-track RGV multi-vehicle dense scheduling

Abstract

The present application relates to the technical field of cooperative control, in particular to a safety cooperative control method for same-track RGV multi-vehicle intensive scheduling, comprising: acquiring the track distance of each RGV from the entrance of a target avoidance area, the planning time window of each RGV in the target avoidance area, and the total number of RGVs; outputting the feasible path of each RGV by using an improved priority search algorithm, and performing safety cooperative control on each RGV based on the feasible path. The present application solves the problem of low continuity and reliability in the scheduling process.

Description

Technical Field

[0001] This invention relates to the field of collaborative control technology. More specifically, this invention relates to a safe collaborative control method for dense scheduling of multiple RGVs on the same track. Background Technology

[0002] With the rapid development of intelligent manufacturing and automated logistics systems, rail-mounted material handling equipment is increasingly used in warehousing and sorting, flexible production lines, and smart factories. Especially in space-constrained and task-intensive operating scenarios, multi-vehicle shared-rail operation is gradually becoming the mainstream configuration. In this type of system, multiple vehicles move back and forth along the same track, using several avoidance zones to achieve passing, overtaking, and task switching, thereby completing high-frequency, high-density material handling operations. However, due to the inherent exclusivity of track resources and the limited number of avoidance zones, when multiple vehicles enter the same area in a short period, path conflicts, resource competition, and localized congestion are easily generated, thus affecting overall scheduling efficiency and system operational safety.

[0003] In existing technologies, priority-based search scheduling strategies are commonly used to address multi-vehicle path planning and scheduling problems. These strategies assign priorities to each vehicle and plan routes sequentially to reduce the probability of conflicts. While these methods can achieve a degree of orderliness in multi-vehicle operation, they largely rely on pre-set initial priority values, which are typically manually set based on task urgency or simple rules and remain unchanged during scheduling. Although simple to implement, this approach ignores dynamically changing environmental factors during actual operation, such as local congestion levels, inter-vehicle interactions, and spatial distribution differences. This means that priorities cannot accurately reflect the current operating state of the system, thus limiting the adaptability and flexibility of the scheduling strategy.

[0004] Furthermore, in scenarios with multiple vehicles operating densely on the same track, a fixed initial priority value can easily lead to an imbalance in resource allocation. When some vehicles are in high-conflict-risk areas or close to critical avoidance zones, they may still be unable to obtain scheduling resources in a timely manner due to their low initial priority, thereby exacerbating local congestion or even triggering a chain reaction of blockages. At the same time, for vehicles that have moved away from conflict areas or have a relatively relaxed operating environment, an excessively high fixed priority may result in unreasonable resource utilization, reduce the overall system's operating efficiency, and lead to problems with low continuity and reliability during the scheduling process. Summary of the Invention

[0005] To address the issues of low continuity and reliability in the scheduling process mentioned in the background art, the present invention provides the following solution.

[0006] This invention provides a safe and coordinated control method for dense scheduling of multiple RGVs on the same track, comprising: obtaining the track distance of each RGV from the entrance of the target avoidance zone, the planning time window of each RGV in the target avoidance zone, and the total number of RGVs; outputting feasible paths for each RGV using an improved priority search algorithm, and performing safe and coordinated control on each RGV based on the feasible paths; wherein, the improved priority search algorithm includes a priority score, which is the sum of an initial value and an adjustment amount, and the adjustment amount is positively correlated with the congestion risk of each RGV and the ratio between the track distance of each RGV from the entrance of the target avoidance zone and a preset maximum scheduling distance; the congestion risk is positively correlated with the maximum value of the competition intensity corresponding to all avoidance zones at any given time and the total number of RGVs in the avoidance zone corresponding to the maximum competition intensity, and negatively correlated with the total number of RGVs in all avoidance zones; the competition intensity of the target avoidance zone at any given time is positively correlated with the overlap duration of the planning time windows of any two RGVs in the target avoidance zone, and negatively correlated with the preset planning duration and the total number of RGVs in the target avoidance zone within the preset planning duration.

[0007] The aforementioned technical solution comprehensively characterizes the temporal overlap, spatial location, and local congestion levels among vehicles, and incorporates the resulting conflict risks into the path planning decision-making process. This allows priorities to be dynamically adjusted according to changes in the operating environment, thereby enabling priority scheduling of critical areas and high-risk vehicles. This effectively reduces the probability of conflict and congestion among multiple vehicles on the same track and in the avoidance area. Simultaneously, by progressively transforming path planning results into subsequent scheduling constraints, the overall operation becomes more orderly and coherent, reducing redundant avoidances and ineffective waiting, significantly improving system throughput and resource utilization. Furthermore, this method enhances the foresight and adaptability of scheduling decisions in complex and dense operating scenarios, enabling the system to maintain high stability and safety even when facing load fluctuations or local congestion, thus improving the overall reliability and continuity of multi-vehicle collaborative operation.

[0008] Furthermore, the first RGV in Adjustment amount at any time for: , For the first RGV in The risk of congestion at any time For the first RGV in The track distance from the entrance to the target avoidance zone at any given time. The preset maximum scheduling distance.

[0009] The aforementioned technical solution couples the congestion risk of a vehicle's environment with its spatial proximity to critical areas, enabling priority adjustments to simultaneously reflect the severity of conflict and the urgency of location. This allows vehicles closer to critical areas and in high-risk states to receive greater adjustment ranges, increasing their response priority in scheduling. Consequently, the scheduling system can more accurately focus on potential conflict sources, proactively optimize routes and allocate resources for high-risk vehicles, reduce their lingering and mutual interference near critical areas, and lower the likelihood of further congestion. At the same time, it avoids excessive intervention in vehicles far from critical areas, thereby improving the overall targeting and balance of scheduling strategies and enhancing the efficiency and stability of system operation.

[0010] Furthermore, the first RGV in Current blocking risks for: , for The maximum value of the competition intensity corresponding to all avoidance zones at any given time. The total number of RGVs within the avoidance zone corresponding to the maximum competition intensity. This represents the total number of RGVs in all avoidance zones.

[0011] The aforementioned technical solution extracts the areas with the highest concentration of conflicts in the system and combines the degree of vehicle aggregation within these areas with the overall vehicle distribution to comprehensively characterize the operational risks faced by individual vehicles. This allows the risk assessment to sensitively reflect both localized high congestion and global load levels, thus providing a more accurate basis for scheduling decisions. Based on this risk metric, key areas and vehicles that may cause congestion can be identified in advance, prompting scheduling strategies to prioritize intervention and resource adjustments for high-risk situations. This effectively suppresses the spread of localized congestion to a wider area, reduces waiting times and conflict-induced stagnation between vehicles, and improves the continuity, stability, and overall traffic efficiency of the system.

[0012] Furthermore, the first A buffer zone Current competitive intensity for: , for At this moment Vehicles and the first RGV in the The overlap of planning time windows for each avoidance zone To preset the planning duration, For the first time within the preset planning period The total number of RGVs within the avoidance zone For the first A buffer zone The total number of RGVs at any given time.

[0013] The aforementioned technical solution systematically quantifies the overlap of multiple vehicles within the same avoidance area over time and normalizes it by combining the overall planning period and the vehicle scale within the area. This allows the obtained indicators to accurately reflect the congestion and conflict intensity of the area under current operating conditions, thus providing the scheduling system with a stable and comparable basis for congestion assessment. Based on this assessment result, potential high-risk areas and high-conflict periods can be identified more accurately, prompting targeted optimization of scheduling strategies in path allocation and timing arrangements. This effectively reduces mutual interference and waiting phenomena among vehicles in key areas, lowers the probability of conflict, and improves the utilization efficiency of avoidance resources and the continuity and stability of the overall system operation.

[0014] Furthermore, the initial value is set according to the task urgency of each RGV, and the task urgency is set manually.

[0015] Furthermore, the planning time window is obtained by calculating the expected arrival time and expected departure time of each RGV.

[0016] Furthermore, the overlap duration of the planning time window is obtained by calculating the intersection of the entry and exit times of any two RGVs in the target avoidance zone.

[0017] The aforementioned technical solution transforms the entry and exit times of vehicles within the target area into continuous time intervals and quantifies the degree of time overlap between vehicles based on the intersection of these intervals. This allows potential conflicts between different vehicles on the same spatial resource to be characterized in a unified and intuitive way, thereby achieving accurate identification and assessment of temporal conflicts. Compared to discrete-time judgment, this method offers higher continuity in time representation and computational consistency, more accurately reflecting the actual occupancy relationship of multiple vehicles within the same area and reducing misjudgments or omissions caused by imprecise time division. Simultaneously, precise calculation of overlap duration provides a more reliable basis for subsequent scheduling strategies, helping to optimize vehicle entry order and traffic flow in advance, reducing the probability of vehicles occupying the area simultaneously, and thus improving the overall coordination, safety, and operational efficiency of scheduling.

[0018] Furthermore, it also includes: when the congestion risk exceeds a preset threshold, triggering a congestion warning for the corresponding avoidance zone.

[0019] The aforementioned technical solution triggers an early warning mechanism when the risk level reaches a certain threshold, enabling the dispatching system to detect potential operational bottlenecks before congestion escalates further, thus shifting from reactive response to proactive intervention. Based on this mechanism, the system can implement control measures such as route adjustments, staggered vehicle operation, or resource reallocation in high-risk areas to effectively suppress the continuous accumulation and spread of local congestion, reduce prolonged vehicle dwell time and mutual interference in key areas, and mitigate the impact of sudden blockages on the overall dispatching order. This early warning and rapid response approach significantly improves the safety and stability of multi-vehicle collaborative operation and enhances the system's anti-congestion capability and operational continuity under high load conditions.

[0020] Furthermore, the step of using the improved priority search algorithm to output feasible paths for each RGV specifically includes: sorting the RGVs based on their priority scores and planning paths in descending order of priority.

[0021] The aforementioned technical solution, by uniformly sorting vehicles and sequentially planning routes according to priority, allows limited track and obstacle avoidance resources to be allocated preferentially to more critical or urgent operational tasks. This creates an orderly and hierarchical scheduling process, significantly reducing the conflicts and uncertainties caused by simultaneous decision-making by multiple vehicles. Simultaneously, the route planning results completed in advance constrain subsequent vehicles, enabling them to proactively avoid already occupied time and space resources, reducing the additional costs of repeated route adjustments and conflict corrections, thereby improving the stability and consistency of the planning results. This top-down, step-by-step decision-making approach also effectively reduces the risk of local congestion escalating into global blockage, minimizing waiting and stagnation, and further enhancing the overall continuity, safety, and traffic efficiency of the system.

[0022] Furthermore, it also includes smoothing the competition intensity.

[0023] The beneficial effects of this invention are as follows: This invention systematically models the temporal overlap, spatial distribution, and local congestion levels during multi-vehicle operation, and incorporates the resulting conflict risks into priority dynamic adjustment and path planning decisions. This enables the scheduling process to adaptively optimize according to changes in operating status, thereby prioritizing responses to critical areas and high-risk vehicles, effectively reducing the probability of conflicts and congestion during co-track operation. Simultaneously, by orderly allocating path planning results and passing constraints at each level, the overall operation process becomes more coordinated and consistent, reducing redundant avoidance and ineffective waiting, and improving the utilization efficiency of track and avoidance resources. Furthermore, by combining congestion early warning and smoothing mechanisms, the ability to perceive congestion trends in advance can be enhanced, and the interference of instantaneous fluctuations on decision-making can be suppressed. This allows the system to maintain good stability and continuity even in high-density operating environments, comprehensively improving the safety, reliability, and operational efficiency of multi-vehicle collaborative scheduling. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a safe collaborative control method for dense scheduling of multiple RGVs on the same track according to an embodiment of the present invention; Figure 2 This diagram schematically illustrates the RGV scheduling results before the priority search algorithm improvement of a safety collaborative control method for dense scheduling of multiple RGVs on the same track according to an embodiment of the present invention. Figure 3 This diagram schematically illustrates the RGV scheduling results after the priority search algorithm is improved in a safety collaborative control method for dense scheduling of multiple RGVs on the same track, according to an embodiment of the present invention. Detailed Implementation

[0025] An embodiment of a safe collaborative control method for dense scheduling of multiple RGVs on the same track.

[0026] like Figure 1 As shown, a flowchart of a safe collaborative control method for dense scheduling of multiple RGVs on the same track according to an embodiment of the present invention is presented, including the following steps: S1: Obtain the track distance of each RGV from the entrance of the target avoidance zone, the planning time window of each RGV in the target avoidance zone, and the total number of RGVs.

[0027] In a preferred embodiment, the track distance between each RGV and the entrance to the target avoidance zone can be measured and calculated in real time by a track encoder, a position sensor, or a positioning system based on a track coordinate system to ensure the continuity and accuracy of the distance information. Simultaneously, based on the route planning information of each RGV, the estimated arrival time and departure time to the entrance of the target avoidance zone are comprehensively predicted and estimated. This allows for the construction of a planning time window for each RGV within the target avoidance zone using the time difference between the estimated departure and arrival times. Furthermore, the total number of RGVs currently participating in the scheduling is statistically analyzed to form a complete description of the multi-vehicle operating status. This approach not only enables precise characterization of the spatiotemporal relationships of each RGV but also allows for the early identification of potential temporal and spatial conflicts during scheduling, providing a reliable basis for subsequent avoidance strategies and effectively improving the collaborative operation efficiency and safety of the multi-RGV system. Moreover, refined modeling of the time windows enhances the foresight and dynamic adaptability of scheduling decisions, reducing congestion and waiting caused by information lag or inaccurate estimations, further improving the overall transportation system's throughput and operational stability.

[0028] S2: Utilize the improved priority search algorithm to output feasible paths for each RGV.

[0029] like Figure 2 As shown, this is a diagram illustrating the RGV scheduling results before the improvement of the priority search algorithm in a safety collaborative control method for dense scheduling of multiple RGVs on the same track, according to an embodiment of the present invention.

[0030] like Figure 3 As shown, this is a diagram illustrating the RGV scheduling results after the improvement of the priority search algorithm in a safety collaborative control method for dense scheduling of multiple RGVs on the same track, according to an embodiment of the present invention.

[0031] In a preferred embodiment, the improved priority search algorithm includes a priority score, which is the sum of an initial value and an adjustment amount, wherein the priority score is... RGV in Adjustment amount at any time for: , For the first RGV in The risk of congestion at any time For the first RGV in The track distance from the entrance to the target avoidance zone at any given time. The initial value is set as the preset maximum scheduling distance. This initial value is set based on the task urgency of each RGV, and the task urgency is manually set.

[0032] A dynamically adjustable priority evaluation mechanism is introduced into the search decision-making process, integrating the urgency of the task itself with the real-time status during operation. The logic is to use the manually set task urgency as the basic ranking criterion, while simultaneously considering the congestion risk in the vehicle's environment and its spatial proximity to key areas. This adaptively adjusts the priority, ensuring that vehicles closer to key areas and in high-congestion-risk environments receive higher scheduling attention. This forms a comprehensive ranking system that considers both static task requirements and dynamic operational status. This approach avoids the scheduling rigidity caused by relying solely on fixed priorities, allowing the scheduling strategy to flexibly adjust according to changes in system congestion and spatial distribution. It effectively improves response speed to critical conflict areas, reduces the possibility of further deterioration of local congestion, and decreases vehicle waiting time in high-risk areas, thereby improving overall operational efficiency and system coordination and stability.

[0033] No. RGV in Current blocking risks for: , for The maximum value of the competition intensity corresponding to all avoidance zones at any given time. The total number of RGVs within the avoidance zone corresponding to the maximum competition intensity. This refers to the total RGV of all avoidance zones. It also includes triggering a congestion warning for the corresponding avoidance zone when the congestion risk exceeds a preset threshold.

[0034] Using the most competitive avoidance zone as the core reference for risk perception, the system extracts the area with the highest conflict level at the current moment to characterize the extreme congestion state in the overall operating environment. This is then combined with a comprehensive normalized mapping of the vehicle aggregation scale within this area and the overall vehicle distribution to construct a risk metric that reflects the degree to which a single vehicle is affected by the global congestion situation. The logic lies in coupling the strongest local conflict with the global scale distribution, making the risk assessment sensitive to key bottleneck areas while also reflecting the overall system load level. Based on this, a threshold judgment mechanism is introduced to trigger a congestion warning in a timely manner when the risk level reaches a set limit, thereby achieving a shift from passive response to proactive prevention. Through this approach, potential congestion trends can be identified in advance during complex multi-vehicle collaborative operations, prompting the scheduling system to prioritize intervention and resource reallocation in high-risk areas. This effectively reduces the probability of local congestion spreading globally, decreases vehicle waiting and conflict-induced stagnation, and improves the continuity, stability, and overall traffic efficiency of the system.

[0035] No. A buffer zone Current competitive intensity for: , for At this moment Vehicles and the first RGV in the The overlap of planning time windows for each avoidance zone To preset the planning duration, For the first time within the preset planning period The total number of RGVs within the avoidance zone For the first A buffer zone The total number of RGVs at any given time. The overlap duration of the planning time window is obtained by calculating the intersection of the entry and exit times of any two RGVs in the target avoidance zone. It also includes smoothing the competition intensity.

[0036] By uniformly modeling the time intervals of each vehicle within the avoidance zone and quantifying the time overlap between any two vehicles within the zone, this method comprehensively characterizes the resource competition level of the same avoidance zone within a certain planning period. The intersection of time intervals reflects the potential conflict relationships between vehicles, and by cumulatively normalizing the overlap between all pairs of vehicles, the resulting competition intensity reflects both the density of local conflicts and comparability across different scale scenarios. Furthermore, a smoothing mechanism is introduced to continuously correct the competition intensity over time, thereby reducing the interference of instantaneous fluctuations or abnormal scheduling behavior on the evaluation results and making the trend of competition intensity more stable and reliable. This allows for a more accurate characterization of the congestion status and conflict risk in the avoidance zone during operation, providing a more forward-looking decision-making basis for the scheduling system. It helps to adjust routes or stagger times in advance, thereby reducing the probability of conflicts during multi-vehicle interactions, reducing waiting and congestion, and improving overall operational efficiency and system stability.

[0037] S3: Perform safe and coordinated control of each RGV based on the feasible path.

[0038] In a preferred embodiment, the step of using the improved priority search algorithm to output feasible paths for each RGV specifically includes: First, globally sorting all RGVs participating in scheduling according to their priority scores at the current time, and constructing an ordered scheduling sequence from high to low priority; On this basis, performing path planning operations on each RGV in sequence according to the ordered scheduling sequence. When searching for paths for RGVs with higher priority, prioritizing the allocation of avoidance zone travel time and track resources for them, and passing their planning results as constraints for subsequent RGV path planning, so that subsequent RGVs with lower priority can actively avoid occupied key sections and time intervals during the path search process, thereby gradually forming a set of globally feasible paths that meet the multi-vehicle coordination constraints; The above method enables hierarchical decision-making and step-by-step constraint propagation from high priority to low priority, allowing critical or high-risk vehicles to obtain better path resources first. This effectively reduces the probability of conflict, decreases the computational overhead caused by repeated path adjustments, and improves the stability and consistency of path planning results. Furthermore, by transforming planned paths into constraints for subsequent decisions, this method helps enhance the overall coordination in multi-vehicle scheduling, avoids global conflicts caused by local optima, and thus significantly improves the system's traffic efficiency and operational reliability under complex conditions.

[0039] The present invention provides a unified model of the temporal overlap, spatial proximity, and overall system load level among vehicles in key areas. It further combines local congestion peaks and vehicle density to characterize vehicle operational risks and regional competition at multiple levels, enabling scheduling decisions to simultaneously reflect both local conflict intensity and global operational status. This enhances the comprehensive perception capability for complex multi-vehicle interaction scenarios. Furthermore, a priority adjustment mechanism driven by dynamic status and task urgency is introduced, allowing the path planning process to adaptively respond to real-time congestion changes, prioritizing the passage needs of high-risk, high-conflict vehicles. Through hierarchical sorting and sequential planning, generated paths are transformed into subsequent decision constraints, enhancing the consistency and coordination of overall scheduling. Simultaneously, by smoothing conflict indicators and providing early warnings for areas exceeding risk limits, the system can identify potential congestion trends and intervene in advance, effectively reducing the probability of local congestion spreading into global blockage, minimizing vehicle waiting and repeated path adjustments, and significantly improving the safety, stability, and overall traffic efficiency of multi-vehicle collaborative operation.

[0040] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0041] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A safe and coordinated control method for dense scheduling of multiple RGVs on the same track, characterized in that, include: Obtain the track distance of each RGV from the entrance of the target avoidance zone, the planning time window of each RGV in the target avoidance zone, and the total number of RGVs; An improved priority search algorithm is used to output feasible paths for each RGV, and safe collaborative control is performed on each RGV based on the feasible paths. The improved priority search algorithm includes a priority score, which is the sum of the initial value and the adjustment amount. The adjustment amount is negatively correlated with the blocking risk of each RGV, the ratio between the track distance of each RGV from the entrance of the target avoidance zone and the preset maximum scheduling distance. The blocking risk is positively correlated with the maximum value of the competition intensity corresponding to all avoidance zones at any given time and the total number of RGVs in the avoidance zone corresponding to the maximum value of the competition intensity, and negatively correlated with the total number of RGVs in all avoidance zones. The competition intensity of the target avoidance zone at any given time is positively correlated with the overlap duration of the planning time window of any two RGVs in the target avoidance zone, and negatively correlated with the preset planning duration and the total number of RGVs in the target avoidance zone within the preset planning duration. No. RGV in Current blocking risks for: , for The maximum value of the competition intensity corresponding to all avoidance zones at any given time. The total number of RGVs within the avoidance zone corresponding to the maximum competition intensity. This represents the total number of RGVs in all avoidance zones.

2. The safe collaborative control method for dense scheduling of multiple RGVs on the same track according to claim 1, characterized in that, No. RGV in Adjustment amount at any time for: , For the first RGV in Current risk of congestion For the first RGV in The track distance from the entrance to the target avoidance zone at any given time. The preset maximum scheduling distance.

3. The safe collaborative control method for dense scheduling of multiple RGVs on the same track according to claim 1, characterized in that, No. A buffer zone Current competitive intensity for: , for At this moment Vehicles and the first RGV in the The overlap of planning time windows for each avoidance zone To preset the planning duration, For the first time within the preset planning period The total number of RGVs within the avoidance zone For the first A buffer zone The total number of RGVs at any given time.

4. The safe collaborative control method for dense scheduling of multiple RGVs on the same track according to claim 1, characterized in that, The initial value is set according to the urgency of each RGV's task, and the urgency of the task is set manually.

5. A safe and coordinated control method for dense scheduling of multiple RGVs on the same track, as described in claim 1, is characterized in that... The planning time window is calculated by taking the estimated arrival time and estimated departure time of each RGV.

6. The safe collaborative control method for dense scheduling of multiple RGVs on the same track according to claim 1, characterized in that, The overlap duration of the planned time window is obtained by calculating the intersection of the entry and exit times of any two RGVs in the target avoidance zone.

7. A safe and coordinated control method for dense scheduling of multiple RGVs on the same track, as described in claim 1, is characterized in that... Also includes: When the congestion risk exceeds a preset threshold, a congestion warning is triggered for the corresponding avoidance zone.

8. A safe and coordinated control method for dense scheduling of multiple RGVs on the same track, as described in claim 1, is characterized in that... The method of using an improved priority search algorithm to output feasible paths for each RGV specifically includes: sorting RGVs based on their priority scores and planning paths in descending order of priority.

9. A safe and coordinated control method for dense scheduling of multiple RGVs on the same track, as described in claim 1, is characterized in that... It also includes smoothing the competition intensity.

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