Multi-underwater-robot cooperative path planning method and system and storage medium

Abstract

The invention discloses a multi-underwater robot cooperative path planning method and system and a storage medium, and relates to the technical field of underwater robos.The method comprises the steps that S1, an underwater distribution electronic graph is obtained in advance, a plurality of sets of underwater charging station areas are locked, and meanwhile serial number marking is conducted on the charging station areas and the robots; accurate positioning of the robots and the charging stations in the path planning process is further ensured, the initial precision of path planning is improved through the process, and a reliable data basis is provided for subsequent steps. In the steps S2 and S3, electric energy state data information of a plurality of groups of robots is monitored in real time, an electric energy set is generated, a to-be-charged robot is determined, a task state set is constructed according to related task processing data information, and a relative task urgency coefficient Rhxs is generated, so that the electric energy state and the task urgency degree of the robots are comprehensively considered; and the reasonable arrangement of the charging demand in the path planning process is further ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater robots, and in particular to a method, system and storage medium for collaborative path planning of multiple underwater robots. Background Art

[0002] In the field of robotics, underwater robots are widely used in tasks such as ocean exploration, resource exploration, and environmental monitoring. Their efficient collaboration is crucial to the successful execution of tasks. These tasks usually require multiple underwater robots to work together in a complex marine environment to improve task efficiency and completion quality. In these tasks, underwater charging path planning becomes an important issue because the endurance of underwater robots directly affects the sustainability and success rate of the mission.

[0003] In a Chinese invention patent with application publication number CN107168309B, a behavior-based multi-underwater robot path planning method is disclosed, including: first, defining basic behaviors to add constraints to the navigation path of the AUV, the basic behaviors are energy-saving behavior, collaborative behavior and safety behavior; then, establishing a behavior objective function corresponding to the basic behavior, combining the time variables and space variables related to the AUV; finally, establishing a global objective function to achieve the behavior fusion of the three basic behaviors, and using a particle swarm optimization algorithm with inertia weights to solve the global objective function, and the output optimal solution can generate a collision-free and shortest path.

[0004] In the technical solution recorded in the above application, the inertia weight determination method of first increasing and then decreasing the inertia weight has faster convergence ability and better local convergence speed than the general particle swarm optimization algorithm. However, in combination with the prior art and the above application, the existing multi-underwater robot collaborative path planning method often faces problems such as unreasonable charging path planning, uneven allocation of charging station resources, and low task execution efficiency. Specifically, the current method lacks comprehensive consideration of the robot's power status and the urgency of the task, which makes it easy for the robot to run out of power during the path planning process and cannot reach the charging station smoothly. At the same time, the existing method lacks flexibility in charging station selection and queuing charging mode, and cannot be dynamically adjusted to adapt to real-time changing environments and task requirements. These shortcomings significantly affect the overall efficiency and task completion quality of the multi-underwater robot system. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method, system and storage medium for collaborative path planning of multiple underwater robots, which solve the problems in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for collaborative path planning of multiple underwater robots, comprising: S1. Obtain an underwater distribution electronic map in advance, lock several groups of underwater charging station areas, and mark the several groups of underwater charging station areas and several groups of robots with serial numbers respectively; S2. Real-time monitoring of power status data information of several groups of robots to generate a power set, determining a robot to be charged from the several groups of robots according to the power set, locating the robot to be charged, and preliminarily determining the nearest charging station; S3. According to the several groups of robots to be charged obtained in step S2, the relevant task processing data information of the several groups of robots to be charged is monitored, a task state set is constructed, and a relative task urgency coefficient Rhxs of the robot to be charged with a sequence number i is generated according to the task state set. Based on the result of the relative task urgency coefficient Rhxs, the robot to be charged with a sequence number i is substituted into a queuing charging mode, and the relevant power consumption data information of the corresponding robot to be charged is combined to predict the power consumption evaluation value Nhz, and it is judged whether the robot preliminarily determined to be to be charged can successfully reach the preliminarily determined charging station; S4. Meanwhile, the relevant business status data information of the nearest charging station is monitored and recorded, and then it is determined whether the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined; when the robot to be charged with serial number i fails to successfully match the nearest charging station preliminarily determined, the next nearest charging station is determined again, and the secondary power consumption evaluation value Nhzc is obtained, and then the secondary power consumption evaluation value Nhzc is compared with the existing power Xnz, and finally it is determined whether the robot to be charged with serial number i needs to enter the path waiting mode.

[0007] Preferably, the power state data information of several groups of robots underwater is monitored and recorded in real time, and a power set is generated, wherein the power set includes the existing power Xnz, power usage time and power safety values ​​Dqz set for the several groups of robots; At the same time, the relationship between the existing electric energy Xnz and the electric power safety value Dqz of several groups of robots is judged to determine the robots that need to interrupt the current task and go to the charging station for charging from several groups of underwater robots. The specific contents are as follows: When the existing electric energy Xnz≤the electric energy safety value Dqz, the corresponding robot is determined as a robot to be charged and marked; When the existing electric energy Xnz>the electric energy safety value Dqz, the corresponding robot is determined as a robot to be charged.

[0008] Preferably, according to the several groups of robots to be charged obtained in step S2, the relevant task processing data information of the several groups of robots to be charged is monitored to construct a task state set, wherein the task state set includes the task deadlines Rpz and travel distances Yxj of the several groups of robots to be charged; The relative task urgency coefficient Rhxs of the robot to be charged with sequence number i is generated according to the task state set. The task urgency coefficient Rhxs of sequence number i is obtained by the following formula: ; The meaning of this formula is to evaluate the difference in task urgency between the robot to be charged with serial number i and the robot to be charged with serial number n; It is represented as the task deadline of the robot to be charged with serial number i, It is represented as the task deadline of the robot to be charged with sequence number n, It is represented by the distance that the robot to be charged with serial number i should travel. It is represented by the distance that the robot to be charged with sequence number n should travel. and The weight coefficients of the corresponding task deadline difference and the corresponding distance difference are respectively, Expressed as the first correction constant.

[0009] Preferably, the charging order of the robot to be charged with the serial number i is determined in combination with the result of the urgency coefficient Rhxs of the task with the serial number i, and the specific contents are as follows; When the urgency coefficient Rhxs of the task with sequence number i is negative, the queue charging mode is turned on. At this time, the robot with sequence number i to be charged will be charged before the robot with sequence number n to be charged. When the urgency coefficient Rhxs of the task with sequence number i is zero, the queue charging mode is turned on. At this time, the robot with sequence number i and the robot with sequence number n can be charged at the same time. When the urgency coefficient Rhxs of the task with sequence number i is a positive number, the queue charging mode is turned on. At this time, the robot to be charged with the current sequence number i will be charged after the robot to be charged with the sequence number n.

[0010] Preferably, relevant power consumption data information of several groups of robots to be charged are monitored and recorded at the same time, wherein the relevant power consumption data information includes an average energy consumption rate Pchz of several groups of robots to be charged and a distance Jk of the corresponding robots to be charged from the initially determined nearest charging station; According to the relevant power consumption data information, the average energy consumption rate Pchz of the corresponding robot to be charged and the distance Jk of the corresponding robot to be charged from the initially determined nearest charging station are correlated to calculate and obtain the corresponding power consumption evaluation value Nhz, which is obtained by the following formula: ; The significance of this formula is to predict whether the power of the robot to be charged can reach the preliminarily determined charging station smoothly. C is the correction constant. It is expressed as the average energy consumption rate of the jth robot to be charged, It is represented as the distance between the jth robot to be charged and the initially determined nearest charging station.

[0011] Preferably, according to the power consumption evaluation value Nhz, combined with the existing power Xnz, it is determined again whether the robot preliminarily determined to be to be charged can successfully reach the preliminarily determined charging station, the specific contents are as follows: If the power consumption evaluation value Nhz> the existing power Xnz, it means that the robots preliminarily determined to be to be charged cannot successfully reach the preliminarily determined charging station. At this time, the group of robots preliminarily determined to be to be charged will reach the nearest preliminarily determined charging station by means of transportation; If the electric energy consumption evaluation value Nhz≤the existing electric energy Xnz, it is preliminarily determined that the robot to be charged can successfully reach the preliminarily determined charging station.

[0012] Preferably, according to the initially determined nearest charging station, relevant business status data information in the initially determined nearest charging station is monitored and obtained, wherein the relevant business status data information includes the total number of charging points Zcds, the number of occupied charging points Yce, the number of free charging points, and the number of robots on the way to the initially determined nearest charging station Jqc; According to the relevant business status data information, the number of remaining charging points Scs of the nearest charging station is calculated and obtained. The number of remaining charging points Scs is obtained by the following formula: ; Where Zcds represents the total number of charging points, Yce represents the number of occupied charging points, and Jqc represents the number of robots on the way to the nearest charging station preliminarily determined; Based on the charging sequence of the robot to be charged with the serial number i and the number of remaining charging points Scs, it is further determined whether the robot to be charged with the serial number i can be successfully matched to the nearest charging station initially determined; When the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined, the robot to be charged with serial number i will start the instruction to go to the preliminarily determined charging station for charging.

[0013] Preferably, when the robot to be charged with serial number i fails to successfully match the nearest charging station initially determined, the next nearest charging station of the robot to be charged with serial number i will be determined again to obtain the distance Jt between the next nearest charging station and the robot to be charged with serial number i, and finally determine whether the robot to be charged with serial number i needs to enter the path waiting mode, the specific contents are as follows: First, calculate the secondary power consumption assessment value Nhzc, the specific formula is: In the formula, It is expressed as the average energy consumption rate of the i-th robot to be charged; Then compare the secondary power consumption evaluation value Nhzc with the existing power Xnz of the robot to be charged with serial number i: When the secondary power consumption evaluation value Nhzc> the current power Xnz of the robot to be charged with the serial number i, it is determined that the robot to be charged with the serial number i needs to enter the path waiting mode; When the secondary power consumption evaluation value Nhzc≤the existing power Xnz of the robot to be charged with the serial number i, it is determined that the robot to be charged with the serial number i does not need to enter the path waiting mode, and will enter the queuing charging mode of the next nearest charging station.

[0014] A multi-underwater robot collaborative path planning system, comprising a preparation module, a charging judgment module, a task analysis module, a sequence division module, an execution consumption module and a conclusion output module; The preparation module obtains the underwater distribution electronic map in advance, locks several groups of underwater charging station areas, and marks the several groups of underwater charging station areas and several groups of robots with serial numbers respectively; The to-be-charged judgment module monitors the power status data information of several groups of robots in real time to generate a power set, determines the to-be-charged robot from the several groups of robots according to the power set, locates the to-be-charged robot, and preliminarily determines the nearest charging station; The task analysis module monitors the relevant task processing data information of the plurality of groups of robots to be charged according to the plurality of groups of robots to be charged, constructs a task state set, and generates a relative task urgency coefficient Rhxs of the robot to be charged with a sequence number i according to the task state set; The sequence division module, based on the result of the relative task urgency coefficient Rhxs, puts the robot to be charged with sequence number i into a queuing charging mode; The execution consumption module predicts the power consumption evaluation value Nhz in combination with the relevant power consumption data information of the corresponding robot to be charged, and determines whether the robot preliminarily determined to be charged can successfully reach the preliminarily determined charging station; The conclusion output module will monitor and record the relevant business status data information of the nearest charging station, and then determine whether the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined; when the robot to be charged with serial number i fails to successfully match the nearest charging station preliminarily determined, the next nearest charging station is determined again, and the secondary power consumption evaluation value Nhzc is obtained, and then the secondary power consumption evaluation value Nhzc is compared with the existing power Xnz, and finally it is determined whether the robot to be charged with serial number i needs to enter the path waiting mode.

[0015] A storage medium for collaborative path planning of multiple underwater robots stores a computer program, which includes the following steps. The computer program stores computer instructions, and when the instructions are executed by a processor, the method for collaborative path planning of multiple underwater robots is implemented.

[0016] The present invention provides a method, system and storage medium for collaborative path planning of multiple underwater robots, which have the following beneficial effects: (1) In step S1, by obtaining the underwater distribution electronic map in advance and locking several groups of underwater charging station areas, and marking the charging station areas and robots with serial numbers, the precise positioning of each robot and charging station during the path planning process is further ensured. This process improves the initial accuracy of path planning and provides a reliable data basis for subsequent steps. In steps S2 and S3, by real-time monitoring of the power status data information of several groups of robots, a power set is generated, the robot to be charged is determined, and a task status set is constructed based on the relevant task processing data information to generate the relative task urgency coefficient Rhxs. This method comprehensively considers the power status of the robot and the urgency of the task, further ensuring the reasonable arrangement of the charging demand during the path planning process, and reducing the situation where robots with relatively urgent tasks cannot reach the charging station smoothly due to insufficient power. In step S4, by monitoring and recording the relevant business status data information of the nearest charging station, the next nearest charging station is determined again, the secondary power consumption evaluation value Nhzc is obtained, and it is compared with the existing power Xnz, and finally it is determined whether the robot needs to enter the path waiting mode. This process ensures the reasonable allocation of charging station resources and the dynamic adjustment of the charging path, and improves the flexibility and adaptability of the system. In short, this method realizes the accurate prediction and reasonable arrangement of charging demand in the path planning process, further reduces the possibility of delays in more intense tasks due to insufficient power, and the robots to be charged can queue for charging according to the importance of the tasks, ensuring the continuity and efficient execution of the tasks.

[0017] (2) By calculating the energy consumption evaluation value Nhz, the energy consumption of the robot to be charged can be accurately predicted and evaluated. According to the relationship between the energy consumption evaluation value Nhz and the existing power Xnz, it is intelligently determined whether the robot can reach the nearest charging station, so that corresponding measures can be taken in time to ensure the smooth progress of the charging process. This intelligent scheduling charging method has high practical value and practicability, and can better adapt to the complex and changeable underwater environment and task requirements.

[0018] (3) The order of charging is determined by the robot's task urgency coefficient Rhxs, making the system more flexible and adaptable, and able to cope with complex and changing underwater environments and task requirements. By arranging the charging sequence reasonably, the resources of the charging station can be used more evenly and effectively. Prioritizing charging services for robots with negative task urgency coefficients ensures the smooth execution of key tasks. At the same time, the charging station can make full use of the charging station resources and improve resource utilization.

[0019] (4) By utilizing the operating status information of the charging station and combining it with the charging sequence of the robot to be charged, it is intelligently determined whether the robot can be successfully matched to the charging station, thereby optimizing the charging scheduling process. This intelligent charging scheduling method has high practical value, can improve charging efficiency, reduce the robot's charging waiting time, and achieve efficient execution of tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the steps of a method for collaborative path planning of multiple underwater robots according to the present invention; Figure 2 The present invention is a schematic diagram of the process of a multi-underwater robot collaborative path planning system. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1 See also Figure 1 The present invention provides a method for collaborative path planning of multiple underwater robots, comprising: S1. Obtain an underwater distribution electronic map in advance, lock several groups of underwater charging station areas, and mark the several groups of underwater charging station areas and several groups of robots with serial numbers respectively; S2. Real-time monitoring of power status data information of several groups of robots to generate a power set, determining a robot to be charged from the several groups of robots according to the power set, locating the robot to be charged, and preliminarily determining the nearest charging station; S3. According to the several groups of robots to be charged obtained in step S2, the relevant task processing data information of the several groups of robots to be charged is monitored, a task state set is constructed, and a relative task urgency coefficient Rhxs of the robot to be charged with a sequence number i is generated according to the task state set. Based on the result of the relative task urgency coefficient Rhxs, the robot to be charged with a sequence number i is substituted into a queuing charging mode, and the relevant power consumption data information of the corresponding robot to be charged is combined to predict the power consumption evaluation value Nhz, and it is judged whether the robot preliminarily determined to be to be charged can successfully reach the preliminarily determined charging station; S4. Meanwhile, the relevant business status data information of the nearest charging station is monitored and recorded, and then it is determined whether the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined; when the robot to be charged with serial number i fails to successfully match the nearest charging station preliminarily determined, the next nearest charging station is determined again, and the secondary power consumption evaluation value Nhzc is obtained, and then the secondary power consumption evaluation value Nhzc is compared with the existing power Xnz, and finally it is determined whether the robot to be charged with serial number i needs to enter the path waiting mode.

[0023] In this embodiment, by real-time monitoring of the robot's power status data, determining the robot to be charged and preliminarily determining the nearest charging station, it is helpful to manage the robot's charging priority, so as to ensure that the robot with low power can be charged in time, and further improve the continuity and stability of task execution. According to the task processing data information of the monitored robot to be charged, a task state set is generated, and the relative task urgency coefficient Rhxs is calculated, which helps the system to reasonably arrange the charging order of the robot according to the urgency of the task and ensure the timely completion of important tasks. By monitoring and recording the relevant business status data information of the nearest charging station, and determining the next nearest charging station again according to the secondary power consumption evaluation value Nhzc, the function of dynamically selecting the charging station is realized. This dynamic selection of the charging station can adjust the selection of the charging station according to the real-time situation and improve the charging efficiency of the robot. Finally, it is determined whether the robot to be charged needs to enter the path waiting mode, and based on the comparison between the secondary power consumption evaluation value Nhzc and the existing power Xnz, the intelligent charging decision is realized, which helps the system to reasonably dispatch the charging behavior of the robot according to the power state and charging demand of the robot, and improve the overall efficiency and stability of the system.

[0024] Traditional underwater robot path planning methods often consider the robot's motion path, thus ignoring the robot's power status and charging needs. In contrast, this method achieves more intelligent and comprehensive path planning by comprehensively considering factors such as the robot's power status, mission urgency, and the operating status of the charging station, and can better adapt to complex underwater environments and mission requirements.

[0025] Example 2 Please refer to Figure 1 Specifically: real-time monitoring and recording of power status data information of several groups of robots underwater, and generating a power set, wherein the power set includes the existing power Xnz, power usage time and power safety values ​​Dqz set for the several groups of robots; At the same time, the relationship between the existing electric energy Xnz and the electric power safety value Dqz of several groups of robots is judged to determine the robots that need to interrupt the current task and go to the charging station for charging from several groups of underwater robots. The specific contents are as follows: When the existing electric energy Xnz≤the electric energy safety value Dqz, the corresponding robot is determined as a robot to be charged and marked; When the existing electric energy Xnz>the electric energy safety value Dqz, the corresponding robot is determined as a robot to be charged.

[0026] In this embodiment, by real-time monitoring and recording of power status data information of several groups of robots, the system can dynamically obtain the current power level of the robot. The generation of the power set includes the existing power Xnz, the power usage time and the power safety value Dqz, which provides comprehensive and accurate data support for judging whether the robot needs to be charged. This real-time monitoring mechanism ensures the timely update and accuracy of the power information and improves the reliability of decision-making. By setting the power safety value Dqz and comparing it with the current power Xnz of the robot, when the existing power Xnz≤power safety value Dqz, the robot is marked as a robot to be charged and the current task is interrupted in time, further ensuring that the robot goes to the charging station for charging before the power is about to run out. This mechanism effectively prevents the robot from unexpectedly shutting down due to power exhaustion, and ensures the safety of the robot's operation and the continuity of the task. When the existing power Xnz>power safety value Dqz, the robot is not determined as a robot to be charged, avoiding unnecessary charging operations, so that the charging station resources are reasonably allocated, and the excessive use and queuing of the charging station are avoided. In short, through a reasonable charging demand judgment mechanism, the charging path planning is optimized and the overall efficiency and resource utilization of the system are improved. Real-time monitoring and judgment of the robot's power status ensures that the task is interrupted for charging only when it is really necessary, and the number of task interruptions is minimized, thereby improving the quality and efficiency of task completion. This strategy ensures the efficient collaboration of robots when performing tasks and improves the overall task completion rate and quality of the system.

[0027] Example 3 Please refer to Figure 1 Specifically: according to the several groups of robots to be charged obtained in step S2, the relevant task processing data information of the several groups of robots to be charged is monitored to construct a task state set, wherein the task state set includes the task deadlines Rpz and travel distances Yxj of the several groups of robots to be charged; The relative task urgency coefficient Rhxs of the robot to be charged with sequence number i is generated according to the task state set. The task urgency coefficient Rhxs of sequence number i is obtained by the following formula: ; The meaning of this formula is to evaluate the difference in task urgency between the robot to be charged with serial number i and the robot to be charged with serial number n; It is represented as the task deadline of the robot to be charged with serial number i, It is represented as the task deadline of the robot to be charged with sequence number n, It is represented by the distance that the robot to be charged with serial number i should travel. It is represented by the distance that the robot to be charged with sequence number n should travel. and The weight coefficients of the corresponding task deadline difference and the corresponding distance difference, 0< ≤1,0< ≤1, and =1, Expressed as the first correction constant.

[0028] The task deadlines Rpz of the above-mentioned groups of robots to be charged can be monitored and obtained through a timestamp recorder; The required distance Yxj refers to the distance that several groups of robots to be charged need to travel to complete the task, which is monitored and obtained by a laser rangefinder.

[0029] in, It is represented as the task deadline difference between the corresponding robots to be charged, It is expressed as the travel distance difference between the corresponding robots to be charged; Combined with the result of the urgency coefficient Rhxs of the task with sequence number i, the charging order of the robot to be charged with sequence number i is determined, and the specific contents are as follows; When the urgency coefficient Rhxs of the task with sequence number i is negative, the queue charging mode is turned on. At this time, the robot with sequence number i to be charged will be charged before the robot with sequence number n to be charged. When the urgency coefficient Rhxs of the task with sequence number i is zero, the queue charging mode is turned on. At this time, the robot with sequence number i and the robot with sequence number n can be charged at the same time. When the urgency coefficient Rhxs of the task with sequence number i is a positive number, the queue charging mode is turned on. At this time, the robot to be charged with the current sequence number i will be charged after the robot to be charged with the sequence number n.

[0030] In this embodiment, by monitoring the task deadline Rpz and the travel distance Yxj of the robot to be charged, constructing a task state set and calculating the relative task urgency coefficient Rhxs, the urgency of each task of the robot to be charged is effectively evaluated. This method ensures that the charging is arranged first for the robot with urgent tasks and a long travel distance, further improving the task completion efficiency of the entire system; according to the result of the relative task urgency coefficient Rhxs, the charging order of the robot to be charged is dynamically determined. Such a dynamic adjustment mechanism of the charging order improves the utilization rate of charging resources and further avoids the waste of charging station resources; through reasonable task priority and charging order arrangement, the waiting time of high-priority task robots is reduced, ensuring that key tasks are handled in a timely manner. This mechanism significantly improves the overall efficiency of the system and the timeliness of task completion, and avoids task failures caused by charging delays. When evaluating and comparing the task urgency of multiple robots to be charged, the weights of the task deadline and the travel distance are considered to ensure that resource allocation is more reasonable and fair. This method of optimizing resource allocation helps to balance the task burden of each robot and improve the stability and task completion rate of the system. By dynamically evaluating and adjusting the charging order, the system can flexibly respond to different task requirements and environmental changes.

[0031] Example 4 Please refer to Figure 1 Specifically: simultaneously monitoring and recording relevant power consumption data information of several groups of robots to be charged, wherein the relevant power consumption data information includes an average energy consumption rate Pchz of several groups of robots to be charged and a distance Jk of the corresponding robots to be charged from the nearest charging station preliminarily determined; According to the relevant power consumption data information, the average energy consumption rate Pchz of the corresponding robot to be charged and the distance Jk of the corresponding robot to be charged from the initially determined nearest charging station are correlated to calculate and obtain the corresponding power consumption evaluation value Nhz, which is obtained by the following formula: ; The significance of this formula is to predict whether the power of the robot to be charged can reach the preliminarily determined charging station smoothly. C is the correction constant. It is expressed as the average energy consumption rate of the jth robot to be charged, It is represented as the distance between the jth robot to be charged and the initially determined nearest charging station.

[0032] According to the power consumption evaluation value Nhz, combined with the existing power Xnz, it is again determined whether the robot preliminarily determined to be to be charged can successfully reach the preliminarily determined charging station. The specific contents are as follows: If the power consumption evaluation value Nhz> the existing power Xnz, it means that the robots preliminarily determined to be to be charged cannot successfully reach the preliminarily determined charging station. At this time, the group of robots preliminarily determined to be to be charged will reach the nearest preliminarily determined charging station by means of transportation; If the electric energy consumption evaluation value Nhz≤the existing electric energy Xnz, it is preliminarily determined that the robot to be charged can successfully reach the preliminarily determined charging station.

[0033] In this embodiment, by monitoring and recording the average energy consumption rate Pchz of the robot to be charged and the distance Jk to the nearest charging station to be initially determined, and other power consumption data information, the system can accurately predict the power consumption of the robot to be charged in the process of going to the charging station. According to the size relationship of the power consumption evaluation value Nhz, it is timely judged whether the robot to be charged can successfully reach the charging station, which provides an important reference basis for the charging process. By comparing the power consumption evaluation value Nhz with the existing power Xnz, the charging demand of the robot to be charged is judged. When the power consumption evaluation value Nhz is greater than the existing power Xnz, it means that the robot cannot reach the charging station smoothly. At this time, the robot is transported to the charging station by means of transportation, which makes full use of the resources in the charging station and improves the efficiency of resource utilization. According to the power consumption of different robots, flexible means of transportation or direct arrival at the charging station are adopted to maximize the charging efficiency and the overall efficiency of the system.

[0034] Example 5 Please refer to Figure 1 Specifically: according to the nearest charging station initially determined, monitor and obtain relevant business status data information in the nearest charging station initially determined, wherein the relevant business status data information includes the total number of charging points Zcds, the number of occupied charging points Yce, the number of idle charging points, and the number of robots on the way to the nearest charging station initially determined Jqc; According to the relevant business status data information, the number of remaining charging points Scs of the nearest charging station is calculated and obtained. The number of remaining charging points Scs is obtained by the following formula: ; Where Zcds represents the total number of charging points, Yce represents the number of occupied charging points, and Jqc represents the number of robots on the way to the nearest charging station preliminarily determined; The number of robots on the way to the nearest charging station is monitored by marking. Based on the charging sequence of the robot to be charged with the serial number i and the number of remaining charging points Scs, it is further determined whether the robot to be charged with the serial number i can be successfully matched to the nearest charging station initially determined; When the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined, the robot to be charged with serial number i will start the instruction to go to the preliminarily determined charging station for charging.

[0035] In this embodiment, through multi-faceted business status data information, the system can understand the resource utilization of the charging station in real time, calculate the number of remaining charging points Scs, which is helpful to dynamically manage the resources of the charging station, adjust the operation strategy of the charging station in time, and further improve the utilization efficiency and service level of the charging station. According to the charging order of the robot to be charged and the number of remaining charging points Scs, the system can intelligently determine whether the robot to be charged can be successfully matched to the charging station. In the case of tight charging point resources, the system can give priority to providing charging services to robots with more intense tasks to ensure the smooth progress of the task; this method effectively manages charging resources by intelligently matching robots and charging stations, which helps to reduce charging waiting time, further avoids waste of charging station resources and idleness of robots while waiting for charging, which can ensure the stable operation of the system and improve the overall efficiency and reliability of the collaborative work of multiple underwater robots.

[0036] Example 6 Please refer to Figure 1 Specifically: when the robot to be charged with serial number i fails to successfully match the nearest charging station initially determined, the next nearest charging station of the robot to be charged with serial number i will be determined again to obtain the distance Jt between the next nearest charging station and the robot to be charged with serial number i, and finally determine whether the robot to be charged with serial number i needs to enter the path waiting mode. The specific contents are as follows: First, calculate the secondary power consumption assessment value Nhzc, the specific formula is: In the formula, It is expressed as the average energy consumption rate of the i-th robot to be charged; Then compare the secondary power consumption evaluation value Nhzc with the existing power Xnz of the robot to be charged with serial number i: When the secondary power consumption evaluation value Nhzc> the current power Xnz of the robot to be charged with the serial number i, it is determined that the robot to be charged with the serial number i needs to enter the path waiting mode; When the secondary power consumption evaluation value Nhzc≤the existing power Xnz of the robot to be charged with the serial number i, it is determined that the robot to be charged with the serial number i does not need to enter the path waiting mode, and will enter the queuing charging mode of the next nearest charging station.

[0037] Among them, re-determining the next nearest charging station of the robot to be charged with serial number i means determining the charging station location closest to the robot to be charged with serial number i on the premise of excluding the charging station initially determined. Real-time positioning can be performed through the positioning system to determine the nearest charging station.

[0038] In this embodiment, by re-determining the next nearest charging station of the robot to be charged and calculating the distance Jt between the charging station and the robot, the system can dynamically adjust the charging path of the robot. This dynamic path adjustment method helps to optimize the charging path and improve the efficiency and success rate of the robot charging. According to the secondary power consumption evaluation value Nhzc, the system can accurately determine whether the robot needs to enter the path waiting mode. By evaluating the power consumption of the robot, timely measures can be taken to avoid the situation where the robot cannot reach the charging station smoothly due to insufficient power, thereby ensuring the smooth execution of the task. This method intelligently determines whether the robot needs to enter the path waiting mode based on the power status and charging requirements of the robot. Through intelligent path planning, the system can more effectively manage the charging behavior of the robot, improve the charging efficiency, and ensure the smooth execution of the task.

[0039] Example 7 Please refer to Figure 1 and Figure 2 ,Specifically: A multi-underwater robot collaborative path planning system includes a preparation module, a charging judgment module, a task analysis module, a sequence division module, an execution consumption module and a conclusion output module; The preparation module obtains the underwater distribution electronic map in advance, locks several groups of underwater charging station areas, and marks the several groups of underwater charging station areas and several groups of robots with serial numbers respectively; The to-be-charged judgment module monitors the power status data information of several groups of robots in real time to generate a power set, determines the to-be-charged robot from the several groups of robots according to the power set, locates the to-be-charged robot, and preliminarily determines the nearest charging station; The task analysis module monitors the relevant task processing data information of the plurality of groups of robots to be charged according to the plurality of groups of robots to be charged, constructs a task state set, and generates a relative task urgency coefficient Rhxs of the robot to be charged with a sequence number i according to the task state set; The sequence division module, based on the result of the relative task urgency coefficient Rhxs, puts the robot to be charged with sequence number i into a queuing charging mode; The execution consumption module predicts the power consumption evaluation value Nhz in combination with the relevant power consumption data information of the corresponding robot to be charged, and determines whether the robot preliminarily determined to be charged can successfully reach the preliminarily determined charging station; The conclusion output module will monitor and record the relevant business status data information of the nearest charging station, and then determine whether the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined; when the robot to be charged with serial number i fails to successfully match the nearest charging station preliminarily determined, the next nearest charging station is determined again, and the secondary power consumption evaluation value Nhzc is obtained, and then the secondary power consumption evaluation value Nhzc is compared with the existing power Xnz, and finally it is determined whether the robot to be charged with serial number i needs to enter the path waiting mode.

[0040] A storage medium for collaborative path planning of multiple underwater robots stores a computer program, including the following steps. The computer program stores computer instructions, and when the instructions are executed by a processor, a method for collaborative path planning of multiple underwater robots as described above is implemented.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for collaborative path planning of multiple underwater robots, characterized in that: include, S1. Obtain an underwater distribution electronic map in advance, lock several groups of underwater charging station areas, and mark the several groups of underwater charging station areas and several groups of robots with serial numbers respectively; S2. Real-time monitoring of power status data information of several groups of robots to generate a power set, determining a robot to be charged from the several groups of robots according to the power set, locating the robot to be charged, and preliminarily determining the nearest charging station; S3. According to the several groups of robots to be charged obtained in step S2, the relevant task processing data information of the several groups of robots to be charged is monitored, a task state set is constructed, and a relative task urgency coefficient Rhxs of the robot to be charged with a sequence number i is generated according to the task state set. Based on the result of the relative task urgency coefficient Rhxs, the robot to be charged with a sequence number i is substituted into a queuing charging mode, and the relevant power consumption data information of the corresponding robot to be charged is combined to predict the power consumption evaluation value Nhz, and it is judged whether the robot preliminarily determined to be to be charged can successfully reach the preliminarily determined charging station; S4. Meanwhile, the relevant business status data information of the nearest charging station is monitored and recorded, and then it is determined whether the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined; when the robot to be charged with serial number i fails to successfully match the nearest charging station preliminarily determined, the next nearest charging station is determined again, and the secondary power consumption evaluation value Nhzc is obtained, and then the secondary power consumption evaluation value Nhzc is compared with the existing power Xnz, and finally it is determined whether the robot to be charged with serial number i needs to enter the path waiting mode.

2. A method for collaborative path planning of multiple underwater robots according to claim 1, characterized in that: Real-time monitoring and recording of power status data information of several groups of robots underwater, and generating a power set, wherein the power set includes the corresponding existing power Xnz of the several groups of robots, the power usage time, and the power safety value Dqz set for the several groups of robots; At the same time, the relationship between the existing electric energy Xnz and the electric power safety value Dqz of several groups of robots is judged to determine the robots that need to interrupt the current task and go to the charging station for charging from several groups of underwater robots. The specific contents are as follows: When the existing electric energy Xnz≤the electric energy safety value Dqz, the corresponding robot is determined as a robot to be charged and marked; When the existing electric energy Xnz>the electric energy safety value Dqz, the corresponding robot is determined as a robot to be charged.

3. A method for collaborative path planning of multiple underwater robots according to claim 2, characterized in that: According to the several groups of robots to be charged obtained in step S2, the relevant task processing data information of the several groups of robots to be charged is monitored to construct a task state set, wherein the task state set includes the task deadlines Rpz and travel distances Yxj of the several groups of robots to be charged; The relative task urgency coefficient Rhxs of the robot to be charged with sequence number i is generated according to the task state set. The task urgency coefficient Rhxs of sequence number i is obtained by the following formula: ； The meaning of this formula is to evaluate the difference in task urgency between the robot to be charged with serial number i and the robot to be charged with serial number n; It is represented as the task deadline of the robot to be charged with serial number i, It is represented as the task deadline of the robot to be charged with sequence number n, It is represented by the distance that the robot to be charged with serial number i should travel. It is represented by the distance that the robot to be charged with sequence number n should travel. and The weight coefficients of the corresponding task deadline difference and the corresponding distance difference are respectively, Expressed as the first correction constant.

4. A method for collaborative path planning of multiple underwater robots according to claim 3, characterized in that: Combined with the result of the urgency coefficient Rhxs of the task with sequence number i, the charging order of the robot to be charged with sequence number i is determined, and the specific contents are as follows; When the urgency coefficient Rhxs of the task with sequence number i is negative, the queue charging mode is turned on. At this time, the robot with sequence number i to be charged will be charged before the robot with sequence number n to be charged. When the urgency coefficient Rhxs of the task with sequence number i is zero, the queue charging mode is turned on. At this time, the robot with sequence number i and the robot with sequence number n can be charged at the same time. When the urgency coefficient Rhxs of the task with sequence number i is a positive number, the queue charging mode is turned on. At this time, the robot to be charged with the current sequence number i will be charged after the robot to be charged with the sequence number n.

5. The method for collaborative path planning of multiple underwater robots according to claim 1, characterized in that: At the same time, the relevant power consumption data information of several groups of robots to be charged is monitored and recorded, wherein the relevant power consumption data information includes the average energy consumption rate Pchz of several groups of robots to be charged and the distance Jk of the corresponding robots to be charged from the initially determined nearest charging station; According to the relevant power consumption data information, the average energy consumption rate Pchz of the corresponding robot to be charged and the distance Jk of the corresponding robot to be charged from the initially determined nearest charging station are correlated to calculate and obtain the corresponding power consumption evaluation value Nhz, which is obtained by the following formula: ； The significance of this formula is to predict whether the power of the robot to be charged can reach the preliminarily determined charging station smoothly. C is the correction constant. It is expressed as the average energy consumption rate of the jth robot to be charged, It is represented as the distance between the jth robot to be charged and the initially determined nearest charging station.

6. A method for collaborative path planning of multiple underwater robots according to claim 5, characterized in that: According to the power consumption evaluation value Nhz, combined with the existing power Xnz, it is again determined whether the robot preliminarily determined to be to be charged can successfully reach the preliminarily determined charging station. The specific contents are as follows: If the power consumption evaluation value Nhz> the existing power Xnz, it means that the robots preliminarily determined to be to be charged cannot successfully reach the preliminarily determined charging station. At this time, the group of robots preliminarily determined to be to be charged will reach the nearest preliminarily determined charging station by means of transportation; If the electric energy consumption evaluation value Nhz≤the existing electric energy Xnz, it is preliminarily determined that the robot to be charged can successfully reach the preliminarily determined charging station.

7. The method for collaborative path planning of multiple underwater robots according to claim 1, characterized in that: According to the initially determined nearest charging station, monitor and obtain relevant business status data information in the initially determined nearest charging station, wherein the relevant business status data information includes the total number of charging points Zcds, the number of occupied charging points Yce, the number of free charging points, and the number of robots on the way to the initially determined nearest charging station Jqc; According to the relevant business status data information, the number of remaining charging points Scs of the nearest charging station is calculated and obtained. The number of remaining charging points Scs is obtained by the following formula: ； Where Zcds represents the total number of charging points, Yce represents the number of occupied charging points, and Jqc represents the number of robots on the way to the nearest charging station preliminarily determined; Based on the charging sequence of the robot to be charged with the serial number i and the number of remaining charging points Scs, it is further determined whether the robot to be charged with the serial number i can be successfully matched to the nearest charging station initially determined; When the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined, the robot to be charged with serial number i will start the instruction to go to the preliminarily determined charging station for charging.

8. The method for collaborative path planning of multiple underwater robots according to claim 7, characterized in that: When the robot to be charged with serial number i fails to successfully match the nearest charging station initially determined, the next nearest charging station of the robot to be charged with serial number i will be determined again to obtain the distance Jt between the next nearest charging station and the robot to be charged with serial number i, and finally determine whether the robot to be charged with serial number i needs to enter the path waiting mode. The specific contents are as follows: First, calculate the secondary power consumption assessment value Nhzc, the specific formula is: In the formula, It is expressed as the average energy consumption rate of the i-th robot to be charged; Then compare the secondary power consumption evaluation value Nhzc with the existing power Xnz of the robot to be charged with serial number i: When the secondary power consumption evaluation value Nhzc> the current power Xnz of the robot to be charged with the serial number i, it is determined that the robot to be charged with the serial number i needs to enter the path waiting mode; When the secondary power consumption evaluation value Nhzc≤the existing power Xnz of the robot to be charged with the serial number i, it is determined that the robot to be charged with the serial number i does not need to enter the path waiting mode, and will enter the queuing charging mode of the next nearest charging station.

9. A multi-underwater robot collaborative path planning system, comprising a multi-underwater robot collaborative path planning method according to any one of claims 1 to 8, characterized in that: It includes a preparation module, a charging judgment module, a task analysis module, a sequence division module, an execution consumption module and a conclusion output module; The preparation module obtains the underwater distribution electronic map in advance, locks several groups of underwater charging station areas, and marks the several groups of underwater charging station areas and several groups of robots with serial numbers respectively; The to-be-charged judgment module monitors the power status data information of several groups of robots in real time to generate a power set, determines the to-be-charged robot from the several groups of robots according to the power set, locates the to-be-charged robot, and preliminarily determines the nearest charging station; The task analysis module monitors the relevant task processing data information of the plurality of groups of robots to be charged according to the plurality of groups of robots to be charged, constructs a task state set, and generates a relative task urgency coefficient Rhxs of the robot to be charged with a sequence number i according to the task state set; The sequence division module, based on the result of the relative task urgency coefficient Rhxs, puts the robot to be charged with sequence number i into a queuing charging mode; The execution consumption module predicts the power consumption evaluation value Nhz in combination with the relevant power consumption data information of the corresponding robot to be charged, and determines whether the robot preliminarily determined to be charged can successfully reach the preliminarily determined charging station; The conclusion output module will monitor and record the relevant business status data information of the nearest charging station, and then determine whether the robot to be charged with serial number i can successfully match the nearest charging station preliminarily determined; when the robot to be charged with serial number i fails to successfully match the nearest charging station preliminarily determined, the next nearest charging station is determined again, and the secondary power consumption evaluation value Nhzc is obtained, and then the secondary power consumption evaluation value Nhzc is compared with the existing power Xnz, and finally it is determined whether the robot to be charged with serial number i needs to enter the path waiting mode.

10. A storage medium for collaborative path planning of multiple underwater robots, on which a computer program is stored, characterized in that: The method comprises the following steps: the computer program stores computer instructions, and when the instructions are executed by a processor, a method for collaborative path planning of multiple underwater robots as described in any one of claims 1 to 8 is implemented.

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