Unmanned aerial vehicle cluster task and route planning method considering battery life

Abstract

The invention belongs to the technical field of unmanned aerial vehicle cluster cooperative control and trajectory planning, and relates to a battery life-considered unmanned aerial vehicle cluster task and route planning method, which comprises the following steps of: receiving task data and a battery health degree factor, and generating an initial candidate route and a basic energy consumption curve; energy supply upper limit and consumption lower limit tracks are calculated based on the health degree, and an energy state safety corridor is constructed; checking whether the energy consumption curve and the predicted residual energy meet corridor constraints or not, performing iterative adjustment on illegal route segments, and generating a final feasible route converged in a corridor; and calculating an energy margin and a full-cycle safety analysis report containing the comprehensive pressure index based on the route, packaging and broadcasting the bidding data packet, and receiving and executing a task instruction determined according to the comprehensive pressure index. The problems of flight potential safety hazards and unbalanced cluster battery life loss caused by the fact that the power boundary is not dynamically adjusted according to the battery aging characteristics in an existing planning method are solved.

Description

Technical Field

[0001] This invention belongs to the technical field of collaborative control and trajectory planning of unmanned aerial vehicle (UAV) swarms, and relates to a method for planning UAV swarm missions and routes that takes into account battery life. Background Technology

[0002] With the widespread application of drone technology in complex scenarios, multi-drone collaborative operations have become the mainstream mode for performing tasks such as regional reconnaissance and logistics transportation. The endurance and reliability of the onboard energy system are key factors restricting the success rate of missions. In actual long-term operation and maintenance, the health status of batteries of different drones in the cluster is significantly dispersed due to the number of charge-discharge cycles and the usage environment, resulting in individual differences in power output limits and energy conversion efficiency of each drone.

[0003] Existing route planning and task allocation schemes in the industry mainly rely on optimization strategies that minimize geometric distance or theoretical energy consumption, typically simplifying batteries as ideal constant voltage sources or using linear estimations based solely on the remaining percentage of charge. At the task allocation level, current technologies tend to focus on minimizing the overall swarm completion time or total energy consumption, favoring the assignment of tasks to the drone closest to the target, with less emphasis on in-depth dynamic constraint analysis of the internal electrochemical reactions of the battery.

[0004] However, existing technologies have limitations in safety and lifespan management when dealing with drone swarms with varying battery performance. Traditional static energy consumption models struggle to capture the voltage drop caused by increased internal resistance in aging batteries during high-maneuvering maneuvers such as climbs or sharp turns. This means that even when drones have theoretically sufficient battery power, they may still experience power interruptions due to momentary power shortages. Furthermore, allocation strategies that ignore the cost of battery health can force batteries with lower health to perform high-load tasks, accelerating their performance degradation and affecting the overall lifespan balance and long-term operational sustainability of the swarm. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a method for drone swarm mission and route planning that takes battery life into account.

[0006] A method for drone swarm mission and route planning that takes battery life into account includes the following steps:

[0007] S1. Receive the mission data packet and the UAV's own status data, obtain the current health factor provided by the battery management system, and generate an initial planning request;

[0008] S2. Generate initial candidate routes based on the initial planning request, and calculate the basic energy consumption curve of the initial candidate routes based on flight performance parameters;

[0009] S3. Based on the current health factor and the initial candidate routes, calculate the upper limit trajectory of energy supply and the lower limit trajectory of energy consumption respectively, and construct the energy status safety corridor.

[0010] S4. Verify whether the basic energy consumption curve and predicted remaining energy meet the constraints of the energy state safety corridor, iteratively adjust the segments with violations, and generate the final feasible route that converges within the energy state safety corridor.

[0011] S5. Calculate the average energy margin index and the full-cycle energy state security analysis report including the comprehensive pressure index based on the final feasible route, and package them to generate a collaborative planning bidding data package.

[0012] S6. Broadcast the collaborative planning bidding data packet to the cluster network, receive the task assignment instructions determined by the cluster decision-making unit based on the comprehensive pressure index, and execute the task.

[0013] A further aspect of the present invention, step S1, includes the following steps:

[0014] Parse the received task data packet to extract the task target location, priority, and task type;

[0015] Real-time location information is collected through the airborne positioning module, and the current health factor, which characterizes the overall health status of the battery pack, is obtained by querying the battery management system.

[0016] Set the real-time location information as the starting point, the target location of the task as the ending point, and map the task type to the route attribute label to generate an initial planning request.

[0017] A further aspect of the present invention, step S2, includes the following steps:

[0018] The path planning algorithm is invoked to generate a three-dimensional spatial trajectory connecting the starting point and the ending point, and the trajectory is sampled and discretized to obtain a discrete waypoint sequence;

[0019] Based on the segment type and route attribute label, associate the expected maneuver instruction with each waypoint in the discrete waypoint sequence;

[0020] Based on flight performance parameters, the cruise energy consumption, climb energy consumption, and turn energy consumption of the discrete waypoint sequence are calculated segment by segment and accumulated in chronological order to generate a basic energy consumption curve.

[0021] A further aspect of the present invention, step S3, includes the following steps:

[0022] The instantaneous power demand during the execution of the initial candidate route is simulated using a battery electrochemical model. Dynamic integration is performed by combining the continuous discharge rate and peak discharge rate corresponding to the current health factor to generate the energy supply upper limit trajectory.

[0023] Calculate the return energy required to return to the preset base from any point on the flight path, and add the margin for handling sudden disturbances to generate the energy consumption lower limit trajectory;

[0024] By combining the upper limit trajectory of energy supply and the lower limit trajectory of energy consumption, a feasible region is defined that satisfies the condition that the cumulative energy consumption is lower than the upper limit and the remaining energy is higher than the lower limit, which serves as the energy state safety corridor.

[0025] A further aspect of the present invention, step S4, includes the following steps:

[0026] Calculate the current total available energy and derive the predicted remaining energy curve based on the basic energy consumption curve;

[0027] Identify the time intervals in which the predicted remaining energy curve is below the lower limit of energy consumption or the basic energy consumption curve is above the upper limit of energy supply, and locate the corresponding illegal flight segments.

[0028] The sequential quadratic programming algorithm is invoked to adjust the waypoints within the illegal flight segment to meet the energy constraints, thereby generating a corrected flight path. Based on the corrected flight path, the steps of constructing and verifying the energy state safety corridor are repeated.

[0029] A further aspect of the present invention, step S5, includes the following steps:

[0030] Calculate the difference sequence between the predicted remaining energy trajectory of the final feasible route and the energy consumption lower limit trajectory, integrate the difference sequence over time and divide it by the total flight time to obtain the average energy margin index.

[0031] Based on the estimated energy consumption trajectory and battery thermal model, the peak discharge rate and peak battery temperature rise are estimated, and the comprehensive stress index is calculated by combining the current health factor.

[0032] The final feasible route, average energy margin index, and full-cycle energy state security analysis report including comprehensive pressure index are combined and packaged together.

[0033] In a further embodiment of the present invention, in step S6, the task assignment instruction is generated by the cluster decision unit executing the following decision logic:

[0034] Collect collaborative planning bidding data packets broadcast by each drone within the preset data collection window;

[0035] Eliminate collaborative planning bidding data packages whose overall pressure index exceeds the preset safety threshold;

[0036] Among the remaining collaborative planning bidding data packages, the scheme with the lowest comprehensive pressure index is selected first; if the comprehensive pressure index of multiple schemes differs within the preset tolerance range, the scheme with the highest average energy margin index is selected as the winning scheme.

[0037] A further aspect of this invention is that the current health factor is a normalized value calculated based on cell voltage consistency, internal resistance variation, and cumulative cycle count.

[0038] The current health factor is obtained by weighted calculation of the deviation of the extreme value of the inter-cell voltage difference from the voltage difference fault threshold, the retention rate of the current measured AC internal resistance relative to the internal resistance of the new battery, and the ratio of the number of cycles completed to the number of cycles designed for the lifespan.

[0039] In a further aspect of this invention, the comprehensive pressure index is calculated using a life stress assessment function:

[0040] The life stress assessment function uses the normalized peak discharge rate, peak battery temperature rise, and current health factor as input variables, and calculates a scalar value characterizing the potential life loss pressure of a single task through weighted summation.

[0041] A further aspect of the present invention includes methods for directional adjustment of waypoints within a violation segment, which include: based on the acquired wind field information, increasing the flight altitude to utilize the tailwind layer, reducing the cruise speed of the segment to reduce power demand, or replacing turning maneuver commands with a turning radius less than a preset radius threshold with an arc track with a turning radius greater than or equal to the preset radius threshold.

[0042] In summary, the present invention has the following beneficial technical effects:

[0043] 1. By constructing an energy state safety corridor based on a battery electrochemical model, the upper limit of energy supply and the minimum energy state warning line that change over time are dynamically derived using the current battery health factor of the UAV. This method quantifies the power decay and peak power limitation caused by battery aging into physical constraint boundaries, enabling the planning system to identify and avoid maneuvers that exceed the current physical performance of the battery during the flight path calculation stage. This reduces the risk of power interruption due to voltage drop when aging batteries perform high-power tasks, thereby improving the physical safety of flight operations.

[0044] 2. A flight path feedback contraction iterative mechanism based on sequential quadratic programming was introduced. The energy consumption characteristics of the initial candidate flight paths were mapped to a safe energy state corridor for consistency verification, and parameters were adjusted for flight segments exceeding the corridor boundary. By taking measures such as reducing flight speed or smoothing trajectory curvature for non-compliant flight segments, instantaneous power demand was controlled within the safe battery output range, improving the matching degree between the flight path planning results and the actual power performance of the UAV, and reducing energy consumption estimation errors caused by model bias.

[0045] 3. A cluster task allocation decision-making mechanism based on a comprehensive pressure index was adopted. During the task bidding phase, peak discharge rate, estimated temperature rise, and current health status were comprehensively considered to generate a full-cycle energy state safety analysis report. This mechanism encourages the cluster decision-making unit to choose solutions that cause less damage to battery health, avoiding assigning high-load tasks to drones with lower health status. This mitigates the additional heat loss caused by high-rate discharge, helps delay the overall battery performance degradation of the cluster, and improves the long-term operational stability of the multi-drone collaborative system. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings are used to provide a further understanding of the present invention.

[0047] Figure 1 A flowchart illustrating an embodiment of this application is disclosed.

[0048] Figure 2 Structural schematic diagrams of embodiments of this application are disclosed. Detailed Implementation

[0049] The following is in conjunction with the appendix Figure 1 - Figure 2 A preferred description of the present invention is provided below.

[0050] See attached document Figure 1 This invention proposes a method for drone swarm mission and route planning that takes battery life into account, comprising the following steps:

[0051] S1. Receive the mission data packet and the UAV's own status data, obtain the current health factor provided by the battery management system, and generate an initial planning request;

[0052] S2. Generate initial candidate routes based on the initial planning request, and calculate the basic energy consumption curve of the initial candidate routes based on flight performance parameters;

[0053] S3. Based on the current health factor and the initial candidate routes, calculate the upper limit trajectory of energy supply and the lower limit trajectory of energy consumption respectively, and construct the energy status safety corridor.

[0054] S4. Verify whether the basic energy consumption curve and predicted remaining energy meet the constraints of the energy state safety corridor, iteratively adjust the segments with violations, and generate the final feasible route that converges within the energy state safety corridor.

[0055] S5. Calculate the average energy margin index and the full-cycle energy state security analysis report including the comprehensive pressure index based on the final feasible route, and package them to generate a collaborative planning bidding data package.

[0056] S6. Broadcast the collaborative planning bidding data packet to the cluster network, receive the task assignment instructions determined by the cluster decision-making unit based on the comprehensive pressure index, and execute the task.

[0057] In one embodiment of the present invention, step S1 includes the following steps:

[0058] The received task data packet is parsed to extract the task target location, priority, and task type; real-time location information is collected through the airborne positioning module, and the current health factor representing the overall health status of the battery pack is obtained by querying the battery management system; the real-time location information is set as the starting point, the task target location is set as the ending point, and the task type is mapped to the route attribute label to generate an initial planning request.

[0059] Specifically, any drone in the drone swarm, acting as the executor, initiates its onboard task processing process. This process first uses its onboard wireless communication module to listen for task data packets periodically broadcast by the task system, such as a ground control station or the lead drone in the swarm, on a preset communication frequency band. It should be noted that these task data packets are typically structured data units containing the target location, priority, and task type, such as JSON or Protobuf format, and are transmitted via UDP multicast protocol to ensure real-time distribution within the drone swarm network. After receiving the task data packet, the drone's task processing process parses it, extracting the geographical coordinates of the target, the priority level represented by integers, and the task type defined as a string.

[0060] Subsequently, the UAV's flight management unit simultaneously initiates the collection of its own status data. The flight management unit queries the onboard GPS / RTK module and inertial measurement unit via a serial bus at a preset positioning sampling frequency. The specific setting of this sampling frequency needs to balance trajectory smoothness and processor load; in this embodiment, it is preferably set to 10Hz to 50Hz, with a typical value of 10Hz, meaning data is collected every 100 milliseconds to obtain the UAV's real-time position information in a preset geodetic coordinate system, such as the WGS-84 coordinate system. It should be understood that this real-time position information is obtained by the fusion calculation of the aforementioned modules and specifically includes longitude, latitude, altitude, three-dimensional velocity, and attitude angle data. Simultaneously, the flight management unit calls a preset flight performance parameter configuration file, which stores constant parameters such as the UAV's maximum horizontal speed, maximum rate of climb, and typical cruise power consumption in key-value pairs. Furthermore, the flight management unit queries the onboard battery management system via the I2C bus at a preset status monitoring frequency, typically 1Hz, to obtain the current health factor characterizing the overall health status of the battery pack. In this embodiment, the current health factor... This is a dimensionless floating-point number ranging from 0 to 1, where 1 represents brand new and 0 represents complete failure. This factor is calculated by the battery management system based on the following weighted formula:

[0061]

[0062] in, This refers to the extreme voltage difference between battery cells, specifically the difference between the cell with the highest voltage and the cell with the lowest voltage within the battery pack. This is the differential pressure fault threshold, such as 0.1V; For the internal resistance of the new battery, This represents the current measured AC internal resistance. This represents the number of iterations already completed. The design lifespan is specified in cycles, such as 500 cycles. As a weighting coefficient, based on battery aging characteristics, it is preferably set to [value] in this embodiment. , , This is to emphasize the impact of increased internal resistance on power performance.

[0063] Finally, the task processing process instantiates a planning request data structure, setting the parsed geographic coordinates of the task objective as the route endpoint and the current coordinates from the real-time location information as the route start point. Simultaneously, it uses the maximum horizontal speed and maximum rate of climb from flight performance parameters as basic speed constraints and maps the task type to the corresponding route attribute label, all of which are then populated into the planning request data structure to generate an initial planning request. This initial planning request is a data structure whose core fields include: start point coordinates, end point coordinates, maximum permissible speed constraint, and route attributes mapped from the task type.

[0064] For example, suppose the mission system is a ground control station that broadcasts a mission data packet containing the following information: the target location is 116.4074 degrees east longitude, 39.9042 degrees north latitude, and an altitude of 50 meters; the priority is 5; and the mission type is high-speed reconnaissance. A drone in the cluster receives and parses this data packet, obtaining the aforementioned information. Simultaneously, the drone uses its own sensors to obtain real-time location information: 116.3000 degrees east longitude, 39.9000 degrees north latitude, and an altitude of 100 meters. Its preset flight performance parameters include a maximum horizontal speed of 20 m / s and a maximum climb rate of 6 m / s. Its battery management system reports a current health factor of 0.85. Next, the drone's mission processing steps set its current location as the starting point, the target location as the ending point, the maximum horizontal speed of 20 m / s as the basic speed constraint, and maps the high-speed reconnaissance mission type to an attribute label requiring high flight path straightness. Finally, an initial planning request is generated. The data structure of this request includes the starting point coordinates, the ending point coordinates, the speed limit of 20 m / s, and the route attribute label with high straightness.

[0065] In one embodiment of the present invention, step S2 includes the following steps:

[0066] The path planning algorithm is invoked to generate a three-dimensional spatial trajectory connecting the starting point and the ending point, and the trajectory is sampled and discretized to obtain a discrete waypoint sequence. Based on the segment type and route attribute label, the expected maneuver command is associated with each waypoint in the discrete waypoint sequence. Based on the flight performance parameters, the cruise energy consumption, climb additional energy consumption and turn additional energy consumption of executing the discrete waypoint sequence are calculated segment by segment and accumulated in time order to generate a basic energy consumption curve.

[0067] Specifically, the UAV's path planning unit, acting as the executor, receives the initial planning request generated by the task processing process. This unit first extracts the starting and ending coordinates of the flight path, as well as basic speed constraints, from the initial planning request. Then, it calls a path planning algorithm library pre-installed in the flight control system, such as the A* algorithm based on a 3D mesh or the fast expanding random tree algorithm. Using the starting point as the root node and the ending point as the target node, it uses the maximum horizontal speed as one of the kinematic constraints and performs a search calculation after considering pre-set geofences such as no-fly zones. The algorithm outputs a continuous 3D spatial trajectory from the starting point to the ending point, i.e., the initial candidate flight path.

[0068] After generating initial candidate routes, the path planning unit activates the waypoint resolver. This resolver samples and discretizes the initial candidate routes at preset sampling intervals, generating an ordered sequence of discrete waypoints. It should be noted that the sampling interval in this embodiment is set to 5 to 10 meters, with a typical value of 5 meters. This value is chosen because excessively large intervals, such as greater than 20 meters, will ignore power spikes caused by short-term maneuvers, while excessively small intervals, such as less than 1 meter, will significantly increase the computational load of subsequent optimization algorithms. For each waypoint in the sequence, the resolver records its three-dimensional coordinates, i.e., longitude, latitude, and altitude values ​​in the WGS-84 coordinate system. Simultaneously, the resolver calculates the desired velocity vector based on the vector difference between the current waypoint and the next waypoint. This velocity vector is a three-dimensional vector, its direction defined by the unit direction vector from the current waypoint to the next waypoint, and its magnitude is set according to the segment attributes and basic speed constraints. For example, in straight cruising segments, it is typically set to 80% to 95% of the maximum horizontal speed to reserve power margin for attitude adjustment.

[0069] Furthermore, the parser associates expected maneuver instruction codes with each waypoint based on flight segment types such as straight lines, climbs, and turns, and the route attribute tags in the initial planning request. These instructions are used to direct the flight control system to perform specific actions, such as level flight, acceleration, and deceleration, when approaching the waypoint. The settings are based on changes in trajectory curvature at the waypoint and the mission type mapping in the initial planning request.

[0070] Finally, the UAV's energy consumption estimation module is invoked. This module reads the discrete waypoint sequence, the expected maneuver command sequence, and flight performance parameters obtained from the flight control system. These flight performance parameters specifically refer to those used for energy consumption estimation, including typical cruise power consumption, energy consumption coefficient per unit mass climb, and energy consumption coefficient for turns. These parameters are typically obtained through wind tunnel experiments or calibration using historical flight data. The module first calculates and accumulates the flight time segment by segment based on the three-dimensional Euclidean distance between adjacent waypoints and the speed value assigned to each segment, obtaining the estimated total flight time required to execute the initial candidate route.

[0071] Next, the module performs an estimation based on a simplified energy consumption model. The energy consumption estimation model used in this embodiment is as follows:

[0072]

[0073] in, For the quality of drones, It is the acceleration due to gravity. For air resistance, where air density, For example, a drag coefficient of 0.5. For a windward area of ​​0.1m 2 , The efficiency of the power system is typically 0.75. This refers to the static power consumption of airborne electronic equipment, such as 50W.

[0074] For each flight segment, the base energy consumption is calculated by multiplying the flight time of that segment by the typical cruise power consumption. If the segment is a climb, an additional power consumption based on the climb altitude and unit energy consumption per climb is added. If the segment is a turn, additional power consumption is added based on the turn rate and model coefficients. The energy consumption of all flight segments is accumulated in chronological order to generate a base energy consumption curve. This curve is a discrete data sequence with time on the horizontal axis and cumulative energy consumption on the vertical axis, describing the energy consumption process of executing the initial candidate route under ideal conditions, such as no wind and constant model parameters.

[0075] For example, continuing the previous example, after receiving the initial planning request, the UAV's path planning unit calls the A* algorithm for planning. Assume the generated initial candidate route is an approximate straight line connecting the starting point and the ending point. The waypoint resolver samples it at 5-meter intervals. The first discrete waypoint obtained is the starting point: The coordinates of the second waypoint may be: : The direction vector from the first point to the second point is calculated and normalized to obtain the direction cosine. Assuming this segment is a straight cruise with a set speed of 18 m / s, the velocity vector is given. The associated expected maneuver command is level flight. All subsequent waypoints are analyzed accordingly. Next, the energy consumption estimation module begins its work. First, the total flight time is calculated: assuming a total route length of 10,000 meters and an average speed of 18 m / s, the estimated total flight time is approximately 555.6 seconds. Then, the basic energy consumption curve is estimated: for the first segment, the distance is 5 meters, the flight time is approximately 0.278 seconds, and the typical cruise power consumption is 200W, so the basic energy consumption for this segment is 55.6 joules. Assuming a total altitude drop of 50 meters from the starting point to the destination, but with potential local undulations along the route, the module determines that a certain segment involves a 5-meter descent. According to the model, the energy consumption coefficient for the descent segment is 0.2, so this segment adds an additional energy consumption calculated based on the altitude difference and mass to the basic cruise energy consumption. The energy consumption curve is calculated and accumulated segment by segment, and finally a basic energy consumption curve is generated. Its starting value is 0 joules, and the ending value at the 555.6th second is the total estimated energy consumption, which is assumed to be 112,500 joules.

[0076] In one embodiment of the present invention, step S3 includes the following steps:

[0077] The instantaneous power demand during the execution of the initial candidate route is simulated using a battery electrochemical model. Dynamic integration is performed by combining the continuous discharge rate and peak discharge rate corresponding to the current health factor to generate the upper limit trajectory of energy supply. The return energy required to return to the preset base from any point in time on the route is calculated, and the margin for handling sudden disturbances is added to generate the lower limit trajectory of energy consumption. The upper limit trajectory of energy supply and the lower limit trajectory of energy consumption are combined to define a feasible region that satisfies the condition that the cumulative energy consumption is lower than the upper limit and the remaining energy is higher than the lower limit, which serves as the energy state safety corridor.

[0078] Specifically, the UAV's energy state analysis unit acts as the execution entity, initiating the process of constructing the energy state safety corridor. This unit first receives the discrete waypoint sequence output by the path planning unit and the current health factor provided by the battery management system. .

[0079] Then, it invokes preset battery electrochemical model parameters. First, it calculates the battery's equivalent internal resistance after aging based on the current health factor. The calculation formula is:

[0080]

[0081] in For the internal resistance of the new battery, such as 15 milliohms, This is the aging sensitivity factor, typically 2.0. An increase in this equivalent internal resistance will directly lead to increased voltage loss at high power output, thereby reducing effective output power.

[0082] Next, the unit enters the energy supply limit trajectory. The calculation sub-process is described below. To accurately simulate the energy boundary of an aging battery under the physical characteristics of "allowing short-term peak output and limiting continuous high load," this embodiment employs a dynamic power limiting algorithm based on virtual thermal accumulation for integral generation.

[0083] Determine the power boundary: Query the database based on the current health factor to obtain the maximum continuous discharge rate. and maximum peak discharge rate Based on the current total battery capacity With nominal voltage And introduce a safety buffer coefficient For example, 0.9, the upper limit of continuous power is calculated. and peak power limit .

[0084] Initialize state variables: Define virtual heat accumulation variables initial value Set the heat accumulation threshold. For example, 5000J, this threshold characterizes the instantaneous overload thermal capacity limit that the battery can withstand; defining the simulated heat dissipation power. .

[0085] Execution time-step integral: Discretize the entire route cycle by time stepping, i.e., step size. For each moment Calculate the maximum allowable power supply at that moment. :

[0086] Calculate the predicted virtual heat accumulation value for the previous time step: .

[0087] The decision logic is as follows:

[0088] like This indicates that the battery is within its permissible overload window; settings should be adjusted accordingly. and update .

[0089] like This indicates that the heat accumulation has reached its limit, and power must be limited to prevent overheating or voltage collapse. In this case, a forced setting is required. and update .

[0090] Generate trajectory points: Obtain the upper limit trajectory by accumulation. .

[0091] Simultaneously, the energy state analysis unit initiates the energy consumption lower limit trajectory calculation subprocess. This calculates the energy consumption lower limit trajectory from any point in time along the flight path. Return energy required to return to the preset base from the current location This value is estimated using a function based on the return distance and a conservative energy consumption model. Then, a preset contingency margin for handling sudden disturbances is added. For example, set as 10% of the energy consumption is used to generate the minimum energy state warning line, i.e., the lower limit trajectory of energy consumption:

[0092]

[0093] Finally, the energy state analysis unit will align the time-to-time energy supply upper limit trajectory. Lowest energy state warning line By combining these parameters, a dedicated energy state safety corridor is defined for the initial candidate flight paths. The feasible region of this corridor is defined as: at any given time... Cumulative energy consumption And remaining energy .

[0094] For example, continuing from the previous example, the energy state analysis unit begins to operate. The input is the discrete waypoint sequence containing hundreds of waypoints within 555.6 seconds obtained by S2, and the current health factor of 0.85 obtained in step S1.

[0095] First, the battery model parameterization subroutine is called. Assume the battery's nominal capacity... It is 10Ah, nominal voltage The voltage is 22.2V. Based on a health factor of 0.85, the database was consulted to obtain the uncorrected maximum continuous discharge rate. This corresponds to 399.6W, with a maximum peak discharge rate. This corresponds to 999W. Set a safety buffer factor. The algorithm's control boundary is calculated as follows: the upper limit of sustained power. Peak power limit Simultaneously, initialize the virtual heat accumulation model: set the heat accumulation threshold. Simulated heat dissipation power Approximately 10% of the initial heat accumulation Next, dynamic integration is performed to generate the energy supply upper limit trajectory. The system performs simulations second by second:

[0096] During the initial level flight phase of the flight path, assuming the model attempts to output maximum capacity, but is limited by thermal accumulation logic: from second 0 to second 15, the system allows the integral power to take... (899.1W), at this time Rising rapidly. When After reaching the 5000J threshold, around the 6th second, the algorithm determines thermal saturation and forcibly limits the subsequent integral power back to its original level. (359.6W), and maintained It fluctuates around the threshold. Integrating this "burst-limit" logic over the entire time period, the resulting upper limit trajectory is no longer a straight line, but a curve with a steep initial slope followed by a gradual decrease in gradient. Assume that at the endpoint, the cumulative energy upper limit of this trajectory is approximately... joule.

[0097] Next, calculate the lower bound trajectory. Assume that at the... Seconds, it takes time to return to base from this point. Take the margin for responding to sudden disturbances as... 10%. For example, if the energy required for the return journey at the destination is 30,000 joules, then the lower limit warning line is... Joules. Ultimately, the constructed dedicated energy state safety corridor is formed by these two dynamically generated curves, namely... and The defined area enclosed by the boundary.

[0098] In one embodiment of the present invention, step S4 includes the following steps:

[0099] Calculate the current total available energy and derive the predicted remaining energy curve based on the basic energy consumption curve; identify the time intervals where the predicted remaining energy curve is lower than the lower limit of energy consumption or the basic energy consumption curve is higher than the upper limit of energy supply, and locate the corresponding illegal flight segments; call the sequential quadratic programming algorithm to adjust the waypoints in the illegal flight segments to generate corrected routes with the goal of meeting energy constraints, and repeat the steps of constructing and verifying the energy state safety corridor based on the corrected routes.

[0100] Specifically, the UAV's flight path optimization unit, acting as the execution entity, initiates a feedback contraction iteration based on the energy state safety corridor. This unit first receives the energy state safety corridor generated by the energy state analysis unit and the basic energy consumption curve generated by the energy consumption prediction module. The flight path optimization unit then obtains the current total available energy of the battery. This value is defined as the total energy that can be safely released from the initial moment under the current battery health state. It then calculates the predicted remaining energy curve. It should be understood that this curve is an estimate of the trajectory of future surplus energy over time, based on the current available energy and base energy consumption curves.

[0101] Next, the unit iterates through the time series to obtain the basic energy consumption curve. Trajectory of upper limit of energy supply Compare the results and predict the remaining energy curve. Lowest energy state warning line A comparison is performed. The logic of the consistency check is: for any given time... If both conditions are met That is, it did not exceed the battery's cumulative output capacity; and If the remaining battery power is sufficient for the return trip, the trip is considered successful; otherwise, it is considered a violation. When detected... Below or Higher than At that time, the unit records that moment. Based on the time-waypoint mapping relationship of the discrete waypoint sequence, the corresponding violation segment is located, which usually contains multiple consecutive waypoints before and after the violation time.

[0102] If a violation segment exists, the route optimization unit invokes the built-in sequential quadratic programming optimization algorithm. This is a numerical method for solving optimization problems with nonlinear constraints, and in this scheme, it is used to search for the minimum modification amount to the segment under energy consumption constraints. The algorithm uses the waypoint coordinates and speed commands within the violation segment as optimization variables. Its objective function is to minimize the total adjustment range of the segment, for example, to minimize the sum of squared Euclidean distances between the waypoint positions before and after the adjustment. The constraint is set as follows: the energy consumption curve re-estimated based on the adjusted waypoint sequence must satisfy the aforementioned safety verification logic. The algorithm solves the problem numerically iteratively and outputs a directional adjustment scheme for the waypoints within the violation segment. Specific adjustment methods include: increasing flight altitude based on wind field information to utilize potential downwind layers and reduce drag; here, wind field information is acquired in real time by the UAV during flight, specifically by calculating the wind speed and direction at the current altitude layer through the vector difference between the vacuum velocity vector collected by the computer-borne airspeed tube and the ground speed vector collected by the positioning module, or by estimating based on pre-set layered meteorological data in the mission data package; reducing the cruising speed of this segment to reduce power demand; or optimizing turning maneuvers. Specifically, the system presets a turning radius threshold, for example, 30 meters. When it detects that the turning radius corresponding to a turning maneuver command in the original flight path is less than the preset radius threshold, it replaces it with an arc-shaped flight path with continuous curvature and a turning radius greater than or equal to the preset radius threshold, in order to reduce the additional power consumption caused by centrifugal acceleration.

[0103] Based on the optimization results, a revised route is generated. Subsequently, using the revised route as new input, the dedicated energy state safety corridor construction process in step S3 is re-executed, and the verification and adjustment sub-processes of this step are repeated based on the new corridor. This feedback loop continues until the corresponding energy consumption and remaining energy curves completely converge to the final feasible route within the new round of energy state safety corridors, or until a preset iteration limit is reached, such as 5 to 20 times, which is set to balance computation time and optimization effect.

[0104] For example, continuing from the previous example, the route optimization unit begins operation. The input is the dedicated energy state safety corridor generated by S3, and the upper limit trajectory endpoint value. Joules, the lower limit warning line endpoint value Joules. The endpoint value of the basic energy consumption curve generated by S2. Joules. Assuming the cell is set to a current total available energy. Joules. Calculate the terminal value of the predicted residual energy curve. Joules. Compare this value to the corridor boundary: (Satisfies the upper limit constraint), but The lower limit constraint was not met, and the remaining power was insufficient for a safe return, resulting in a consistency check failure. The unit checked the entire time series and found that the remaining energy was insufficient starting from approximately second 300. Therefore, the segment from second 300 to second 555.6 was marked as a violation segment. Next, a sequence quadratic programming algorithm was invoked. The algorithm used dozens of waypoints within this violation segment as optimization variables. Assuming that the original elevation of one of the adjusted waypoints was 150 meters, the algorithm attempted to raise it to 180 meters while simultaneously reducing the speed command from 18 m / s to 15 m / s. After multiple model evaluations and iterations, the algorithm found a set of adjustment schemes that reduced the endpoint value of the energy consumption curve estimated based on the new waypoint sequence from 112,500 joules to 95,000 joules. At this point, the new predicted endpoint value of the remaining energy curve became... The energy state safety corridor is determined to be above the lower limit of 33,000 joules. Therefore, the revised route is sent to the S3 process, where the energy state safety corridor is recalculated based on the new waypoint sequence. Assuming the new corridor parameters do not change significantly, the final feasible route is confirmed.

[0105] In one embodiment of the present invention, step S5 includes the following steps:

[0106] The difference sequence between the predicted remaining energy trajectory of the final feasible route and the energy consumption lower limit trajectory is calculated. The difference sequence is integrated over time and divided by the total flight time to obtain the average energy margin index. The peak discharge rate and peak battery temperature rise are estimated based on the estimated energy consumption trajectory and battery thermal model. The comprehensive stress index is calculated by combining the current health factor. The final feasible route, the average energy margin index, and the full-cycle energy state safety analysis report including the comprehensive stress index are combined and packaged.

[0107] Specifically, the collaborative planning and encapsulation module of the UAV acts as the execution entity, responsible for generating bid data packets for cluster decision-making. This module first receives the final feasible route output by the route optimization unit, and the estimated energy consumption trajectory re-estimated by the energy consumption estimation module based on the final feasible route. And the energy state safety corridor and its corresponding minimum energy state warning line reconstructed by the energy state analysis unit based on the final feasible route. It should be noted that the estimated energy consumption trajectory here... Although the estimation process is similar to the basic energy consumption curve, it is calculated using more accurate model parameters.

[0108] The module initiates a data encapsulation process, encoding the final feasible route, i.e., the adjusted discrete waypoint sequence and its associated maneuvering instructions, into a machine-readable standardized route description format. For example, it defines a data exchange protocol for waypoint coordinates, speed, heading, maneuvering instructions and their timestamps, and then serializes and compresses it.

[0109] Subsequently, the module calculates the average energy margin index. This indicator, measured in joules, quantifies the average redundant energy of the entire flight path relative to the safety boundary. A higher value indicates a more adequate energy safety buffer for the UAV executing this route. The calculation is performed using a numerical integration method: first, based on the current total available energy... and predicted energy consumption trajectory Calculate and predict the remaining energy trajectory Then, calculate its relationship with the minimum energy state warning line. Difference sequence between .because After feasibility convergence, the value remains above the safety warning line; the difference... Characterizes at time Energy redundancy relative to the safety baseline. Next, the module uses the trapezoidal integral method to integrate this non-negative difference sequence and divides it by the total flight time of the route. The calculation formula is: Calculation results This refers to the average energy margin index, measured in joules. It quantifies the average redundant energy that the entire flight path possesses relative to the safety boundary. For a moment The difference between the remaining energy and the warning limit. This represents the total number of time sampling points.

[0110] Next, the module generates a full-cycle energy state safety analysis report. The report generator calls upon the battery model and the estimated energy consumption trajectory. First, the peak discharge power is estimated through differential operations. The peak discharge rate was calculated based on the total battery capacity. ,in, Nominal voltage, This represents the current total capacity of the battery. This represents the current health factor. Simultaneously, based on a simplified battery thermal model, and inputting the average power and peak power duration, the peak battery temperature rise during the flight path is estimated. This refers to the highest predicted temperature rise throughout the entire process. Overheating is one of the key factors leading to battery life degradation.

[0111] Finally, the report generator calls the preset life stress assessment function. This function takes the peak discharge rate, peak battery temperature rise, and current health factor as inputs, and outputs a comprehensive stress index between 0 and 1. ,Right now This index is a scalar calculated by a multifactor regression model; a higher index indicates a greater potential pressure on battery life from operating the route. Its evaluation function... A simplified linear form can be expressed as Where Norm represents a normalization function that maps parameters with different physical dimensions to the interval [0,1]. These are the weighting coefficients fitted based on the experimental data.

[0112] Finally, the collaborative planning encapsulation module serializes the final feasible route and calculates the average energy margin index. and including peak discharge rate Battery temperature rise peak and comprehensive stress index The full-cycle energy state security analysis report is combined and packaged into a structured collaborative planning bidding data package. This data package is used to participate in cluster task allocation decisions and also contains the UAV's own unique identifier and timestamp.

[0113] For example, continuing from the previous example, the collaborative planning encapsulation module begins to work. The input is the final feasible route output by S4, whose total flight time is re-estimated. Seconds. Based on this route, the energy consumption prediction module generates a new predicted energy consumption trajectory. Its value at the end time Joules. Remaining energy. Joules. The energy state analysis unit generates a new minimum energy state warning line based on this flight path. Its value at the endpoint is assumed to be Joules. The module first serializes and encapsulates the final feasible flight path. Then, it calculates the energy margin metric. Assume the system samples the flight path over 500 seconds at 1-second intervals, resulting in 501 data points. For each sampling point... ,calculate For example in seconds, joule, Joule, then Joule. Using the trapezoidal rule for all time intervals... Integrating the difference, we get the total integral value. The average energy margin index is calculated by dividing the joule-second by the total time of 500 seconds. joule.

[0114] Then an analysis report is generated. Differentiate and calculate to get Based on the thermal model, the following was estimated: Current health factor Call the preset The function, assuming its linear form and weights are... The input variables are normalized: a preset normalization benchmark is set as the maximum allowable discharge rate of 5C and the maximum allowable temperature rise of 50℃. The normalized peak discharge rate is then calculated. Normalized temperature rise .but Finally, the module will combine the route data, Joule, and the report Package the data and attach the drone ID and timestamp to form a collaborative planning bidding data package.

[0115] In one embodiment of the present invention, in step S6, the task assignment instruction is generated by the cluster decision unit by executing the following decision logic:

[0116] Collect collaborative planning bidding data packets broadcast by each UAV within the preset data collection window; remove collaborative planning bidding data packets whose comprehensive pressure index exceeds the preset safety threshold; among the remaining collaborative planning bidding data packets, prioritize the scheme with the lowest comprehensive pressure index; if the comprehensive pressure index differences of multiple schemes are within the preset tolerance range, select the scheme with the highest average energy margin index as the winning scheme.

[0117] Specifically, the decision-making unit in the cluster acts as the executor, implementing the final task allocation. The decision-making unit is a logical entity responsible for aggregating bidding information and running decision-making algorithms. Its physical form can be a central server in a ground control station or a processing module within a lead drone elected within the cluster. First, each drone in the cluster network that has completed its internal planning periodically broadcasts its generated collaborative planning bidding data packets via its onboard wireless communication module on a pre-set bidding broadcast channel—a combination of designated wireless communication frequency bands and data link layer protocols—using a carrier sense multiple access protocol with collision avoidance. This data packet encapsulates the drone's unique identifier, final feasible flight path, average energy margin index, and a full-cycle energy state security analysis report.

[0118] The decision-making unit continuously monitors the bidding broadcast channel and initiates a data collection window of a preset duration, typically 1 to 5 seconds, depending on the cluster size. During this window, the decision-making unit receives and parses all received collaborative planning bidding data packets, caching them in a bidding list. After the window ends, the decision-making unit calls a preset decision function to evaluate all bidding schemes in the list. This decision function encapsulates specific filtering rules; its logic is not simply based on energy margin indicators for sorting, but rather employs a two-level filtering mechanism:

[0119] The first level is to eliminate bids that have a comprehensive stress index that exceeds a preset safety threshold. This threshold is determined based on battery cycle aging test data to ensure that no winning bid will put excessive stress on battery health.

[0120] The second level involves selecting the winning bid from the remaining qualified bids based on the lowest overall stress index value. If multiple bids have the same lowest overall stress index (i.e., the differences in overall stress indices among multiple bids are within the pre-approval tolerance range), then the average energy margin index is used as a secondary criterion to select the bid with the higher index.

[0121] After the decision function completes its evaluation, the decision unit generates a task assignment instruction data packet. This packet explicitly contains the unique identifier of the winning UAV and a confirmation instruction for that UAV to execute the final feasible route provided in its bid data packet. Subsequently, the decision unit sends the task assignment instruction data packet to all members of the cluster via a wireless network, using a reliable broadcast method, such as through TCP or application-layer retransmission mechanisms to ensure reception. Each UAV in the cluster receives and parses the instruction. The winning UAV confirms its role as the task executor by comparing the identifier in the instruction packet. Its flight management unit then immediately loads the final feasible route generated during the bid, converts it into a waypoint sequence and instructions executable by the flight control system, and begins executing the route. Other non-winning UAVs discard the instruction and continue waiting or processing other tasks.

[0122] For example, following the example from the previous section, suppose that in the cluster, besides example drone A, its bid data packet contains , Besides Joule, UAVs B and C also participated in the bidding for the same task. Within a 2-second data collection window, the decision unit received three bid data packets. After parsing, it was found that UAV B's bid... , Joules; in the bidding for UAV C, , Joules. The decision function first performs a first-level screening: the preset safety threshold is 0.8, and the three drones... All were within acceptable limits, therefore all were qualified. Next, the second level of screening was implemented: among the qualified bids, The lowest value is 0.20 for drone C, so drone C is initially selected. Since there are no ties, the energy margin metric is not needed as a secondary criterion. Ultimately, drone C is determined to be the successful bidder. The decision-making unit then generates a task assignment instruction: "Executor: Drone C; Instruction: Confirm execution of the route you bid for at timestamp T." This instruction is sent to the cluster network. Drones A, B, and C all receive this instruction. Drone C parses the instruction and confirms the identifier match, then retrieves the corresponding final feasible route from its internal storage and begins executing the flight mission. Drones A and B parse the instruction and confirm they did not win the bid, then enter a standby state.

[0123] See appendix Figure 2The present invention also proposes a drone swarm mission and route planning system that takes into account battery life, including the following modules:

[0124] The task status acquisition module is used to receive task data packets and the drone's own status data, obtain the current health factor provided by the battery management system, and generate an initial planning request.

[0125] The route energy consumption estimation module generates initial candidate routes based on the initial planning request and calculates the basic energy consumption curve of the initial candidate routes based on flight performance parameters.

[0126] The energy security corridor module calculates the upper limit trajectory of energy supply and the lower limit trajectory of energy consumption based on the current health factor and the initial candidate routes, and constructs an energy status security corridor.

[0127] The route contraction iteration module is used to verify whether the basic energy consumption curve and the predicted remaining energy meet the constraints of the energy state safety corridor. It iteratively adjusts the routes with violations and generates the final feasible routes that converge within the energy state safety corridor.

[0128] The collaborative bidding encapsulation module calculates the average energy margin index and a full-cycle energy state security analysis report including the comprehensive pressure index based on the final feasible route, and encapsulates and generates a collaborative planning bidding data package.

[0129] The cluster decision allocation module is used to broadcast collaborative planning bidding data packets to the cluster network, receive task assignment instructions determined by the cluster decision-making unit based on the comprehensive pressure index, and execute tasks.

[0130] Each of the modules can be implemented in whole or in part through software, hardware, or a combination thereof. It supports hardware embedded in or independent of the processor in the computer device, and also supports software stored in the memory of the computer device, so that the processor can call and execute the operations corresponding to each of the above modules.

[0131] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for planning drone swarm missions and flight paths while considering battery life, characterized in that, Includes the following steps: S1. Receive the mission data packet and the UAV's own status data, obtain the current health factor provided by the battery management system, and generate an initial planning request; S2. Generate initial candidate routes based on the initial planning request, and calculate the basic energy consumption curve of the initial candidate routes based on flight performance parameters; S3. Based on the current health factor and the initial candidate routes, calculate the upper limit trajectory of energy supply and the lower limit trajectory of energy consumption respectively, and construct the energy status safety corridor. S4. Verify whether the basic energy consumption curve and predicted remaining energy meet the constraints of the energy state safety corridor, iteratively adjust the segments with violations, and generate the final feasible route that converges within the energy state safety corridor. S5. Calculate the average energy margin index and the full-cycle energy state security analysis report including the comprehensive pressure index based on the final feasible route, and package them to generate a collaborative planning bidding data package. S6. Broadcast the collaborative planning bidding data packet to the cluster network, receive the task assignment instructions determined by the cluster decision-making unit based on the comprehensive pressure index, and execute the task.

2. The method for planning drone swarm missions and routes while considering battery life, as described in claim 1, is characterized in that... Step S1 includes the following steps: Parse the received task data packet to extract the task target location, priority, and task type; Real-time location information is collected through the airborne positioning module, and the current health factor, which characterizes the overall health status of the battery pack, is obtained by querying the battery management system. Set the real-time location information as the starting point, the target location of the task as the ending point, and map the task type to the route attribute label to generate an initial planning request.

3. The method for planning drone swarm missions and routes while considering battery life, as described in claim 1, is characterized in that... Step S2 includes the following steps: The path planning algorithm is invoked to generate a three-dimensional spatial trajectory connecting the starting point and the ending point, and the trajectory is sampled and discretized to obtain a discrete waypoint sequence; Based on the segment type and route attribute label, associate the expected maneuver instruction with each waypoint in the discrete waypoint sequence; Based on flight performance parameters, the cruise energy consumption, climb energy consumption, and turn energy consumption of the discrete waypoint sequence are calculated segment by segment and accumulated in chronological order to generate a basic energy consumption curve.

4. The method for planning drone swarm missions and routes while considering battery life, as described in claim 1, is characterized in that... Step S3 includes the following steps: The instantaneous power demand during the execution of the initial candidate route is simulated using a battery electrochemical model. Dynamic integration is performed by combining the continuous discharge rate and peak discharge rate corresponding to the current health factor to generate the energy supply upper limit trajectory. Calculate the return energy required to return to the preset base from any point on the flight path, and add the margin for handling sudden disturbances to generate the energy consumption lower limit trajectory; By combining the upper limit trajectory of energy supply and the lower limit trajectory of energy consumption, a feasible region is defined that satisfies the condition that the cumulative energy consumption is lower than the upper limit and the remaining energy is higher than the lower limit, which serves as the energy state safety corridor.

5. A method for planning drone swarm missions and routes while considering battery life, as described in claim 1, is characterized in that... Step S4 includes the following steps: Calculate the current total available energy and derive the predicted remaining energy curve based on the basic energy consumption curve; Identify the time intervals in which the predicted remaining energy curve is below the lower limit of energy consumption or the basic energy consumption curve is above the upper limit of energy supply, and locate the corresponding illegal flight segments. The sequential quadratic programming algorithm is invoked to adjust the waypoints within the illegal flight segment to meet the energy constraints, thereby generating a corrected flight path. Based on the corrected flight path, the steps of constructing and verifying the energy state safety corridor are repeated.

6. The method for planning drone swarm missions and routes while considering battery life, as described in claim 1, is characterized in that... Step S5 includes the following steps: Calculate the difference sequence between the predicted remaining energy trajectory of the final feasible route and the energy consumption lower limit trajectory, integrate the difference sequence over time and divide it by the total flight time to obtain the average energy margin index. Based on the estimated energy consumption trajectory and battery thermal model, the peak discharge rate and peak battery temperature rise are estimated, and the comprehensive stress index is calculated by combining the current health factor. The final feasible route, average energy margin index, and full-cycle energy state security analysis report including comprehensive pressure index are combined and packaged together.

7. A method for planning drone swarm missions and routes while considering battery life, as described in claim 1, is characterized in that... In step S6, the task assignment instruction is generated by the cluster decision unit executing the following decision logic: Collect collaborative planning bidding data packets broadcast by each drone within the preset data collection window; Eliminate collaborative planning bidding data packages whose overall pressure index exceeds the preset safety threshold; Among the remaining collaborative planning bidding data packages, the scheme with the lowest comprehensive pressure index is selected first; if the comprehensive pressure index of multiple schemes differs within the preset tolerance range, the scheme with the highest average energy margin index is selected as the winning scheme.

8. A method for planning drone swarm missions and routes while considering battery life, as described in claim 2, is characterized in that... The current health factor is a normalized value calculated based on cell voltage consistency, internal resistance variation, and cumulative cycle count. The current health factor is obtained by weighted calculation of the deviation of the extreme value of the inter-cell voltage difference from the voltage difference fault threshold, the retention rate of the current measured AC internal resistance relative to the internal resistance of the new battery, and the ratio of the number of cycles completed to the number of cycles designed for the lifespan.

9. A method for planning drone swarm missions and routes while considering battery life, as described in claim 6, is characterized in that... The comprehensive pressure index is calculated using the life stress assessment function: The life stress assessment function uses the normalized peak discharge rate, peak battery temperature rise, and current health factor as input variables, and calculates a scalar value characterizing the potential life loss pressure of a single task through weighted summation.

10. A method for planning drone swarm missions and routes while considering battery life, as described in claim 5, is characterized in that... Methods for directional adjustment of waypoints within the violation segment include: increasing flight altitude to utilize the downwind layer based on the acquired wind field information, reducing the cruise speed of the segment to reduce power demand, or replacing turning maneuver commands with a turning radius less than a preset radius threshold with an arc track with a turning radius greater than or equal to the preset radius threshold.

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