Multi-UAV cooperative sensing and collision avoidance method and device under limited communication conditions

Abstract

The present invention provides a method and device for collaborative perception and collision avoidance of multiple UAVs under limited communication conditions, which belongs to the field of air traffic management and UAV technology. The method uses the UAV airborne perception equipment to perform situational awareness, predict and evaluate the risk of conflict and collision with surrounding UAVs; establishes a conflict relationship weight network to identify high-risk UAVs in the network; combines the high-risk UAV prior information provided by the conflict relationship weight network to determine the optimal collision avoidance maneuvering sequence of multiple UAVs considering communication costs and maneuvering costs; maneuvers in an orderly manner according to the optimal collision avoidance maneuvering sequence, and uses the communication link to update the flight situation externally. The present invention can solve the problem of collaborative conflict-free trajectory planning of multiple UAVs with communication constraints, ensure that multiple UAVs in the airspace can achieve collision avoidance maneuvers with the help of communication collaboration under limited communication resources, and finally reach the expected target point under the premise of reducing unnecessary communications, and avoid mutual collisions during flight.

Description

Technical Field

[0001] The present invention relates to the field of air traffic management and unmanned aerial vehicle technology, and in particular to a method and device for collaborative sensing and collision avoidance of multiple unmanned aerial vehicles under limited communication conditions, which can realize high-density cooperative safe flight of unmanned aerial vehicles under limited communication bandwidth conditions and meet the requirements of continuous safety and stability of airspace under communication resource-constrained environments. Background Art

[0002] In recent years, driven by technological advances in batteries and power, the performance of unmanned aircraft systems (UAS) has been continuously improved, and the application scenarios have been greatly expanded. They have gradually penetrated into the entire industry from logistics, emergency rescue, agriculture, forestry, animal husbandry and fishery. However, the increase in the number of drone flights, the expansion of flight range, and the extension of flight time in limited airspace environments will lead to an increase in the frequency of flight conflicts between multiple drones, and the probability of continuous conflicts between multiple drones will increase significantly, further exacerbating the complexity of airspace operation. In order to ensure the safe and efficient operation of flight activities in complex airspace environments, it is urgent to solve the problem of collaborative conflict-free trajectory planning for large-scale drones.

[0003] Traditional conflict-free trajectory planning methods mostly adopt ground-based centralized conflict control methods, where controllers centrally generate feasible strategies that meet the safety interval constraints and the objective function of minimizing the maneuvering cost, which has strong advance performance. However, the flight dynamics, temporary nature, and autonomy of UAVs are strong, and the centralized interval control method will limit their flight flexibility. In addition, the computational complexity of global optimization increases exponentially with the number of aircraft, making it difficult to meet the efficient solution requirements of multi-UAV high-density airspace. In this regard, the airborne collaborative conflict control method, in which individual UAVs assume the main responsibility for conflict resolution and autonomously generate resolution maneuvering strategies, has become the key to the safe and widespread operation of UAVs in the future. However, airborne collaborative control requires the UAV airborne end to achieve multi-machine collaboration under the action of the communication link, and current research generally does not consider the impact of communication constraints. In fact, due to the small size of UAV airborne equipment, the communication equipment and communication system themselves have limited communication bandwidth and transmission power, which will cause many restrictions on communication between UAVs. Therefore, it is necessary to study the multi-UAV collaborative perception and collision avoidance method under communication constraints. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for collaborative sensing and collision avoidance of multiple UAVs under communication limited conditions, which ensures that UAVs autonomously optimize and generate conflict-free trajectories, reduces the communication burden in the collaborative process of multiple UAVs, and minimizes the maneuvering cost and collision probability, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] On the one hand, the present invention provides a multi-UAV cooperative sensing and collision avoidance method under limited communication conditions, comprising:

[0007] Use drone-mounted sensing equipment to conduct situational awareness, predict and assess the risk of conflict and collision with surrounding drones;

[0008] Establish a conflict relationship weight network to effectively identify high-risk drones in the network;

[0009] Combined with the high-risk UAV prior information provided by the conflict relationship weight network, the optimal collision avoidance maneuver sequence of multiple UAVs considering communication cost and maneuver cost is determined;

[0010] Maneuver in an orderly manner according to the optimal collision avoidance maneuver sequence, and use communication links to update the flight status externally.

[0011] Furthermore, the situation awareness is performed with the help of the drone's onboard sensing equipment to predict and evaluate the risk of conflict and collision with surrounding drones, including: calculating the flight situation information of surrounding drones in the airspace and predicting whether there is a risk of conflict and collision; if the closest distance between the two drones is less than the given safety interval constraint r safe ,Right now It is considered that there is a possibility of conflict between the two aircraft; is the relative position vector between UAV A and UAV B, is the relative velocity vector between the two machines, is the closest distance between the two aircraft, satisfying

[0012] Furthermore, a conflict relationship weight network is established to effectively identify high-risk drones in the network, including:

[0013] (1) Calculate the conflict time T between conflicting drones cpa , if the conflict occurs within the time threshold T o If it is within the range, go to step (2);

[0014] (2) Time T when a conflict occurs between multiple drones cpa,ij Normalize and quantify the conflict relationship weight w between UAV i and UAV j ij ;

[0015] (3) Construct a multi-UAV conflict relationship weight network G. If the conflict relationship weight w between UAV i and UAV j is ij > 0, then nodes i and j in G establish an edge;

[0016] (4) Calculate the risk level of drone conflict Φ i , and sort them from high to low according to the degree of conflict risk.

[0017] Furthermore, combined with the high-risk UAV prior information provided by the conflict relationship weight network, the optimal collision avoidance maneuvering sequence of multiple UAVs considering communication cost and maneuvering cost is determined, including: establishing a utility function for the multi-UAV maneuvering sequence that satisfies both safety and economy requirements; using the high-conflict risk UAVs in the conflict relationship weight network as prior information, using a genetic algorithm for parallel search, and obtaining the optimal multi-UAV maneuvering sequence that minimizes the utility function; wherein,

[0018] The utility function is:

[0019]

[0020] Among them, the utility function consists of three parts: dev i is the maneuver cost of the UAV with maneuver order i, and are the collision avoidance velocity vector and original velocity vector of the UAV respectively; conf i is the number of conflicts after the UAV maneuvers, conf i =∑ j≠i r ij , if the UAV still conflicts with the UAV with maneuver order j, then r ij =1, otherwise 0; com i The communication cost required to update the information of the UAV is You need to update the speed situation externally, com i =1, otherwise 0; c1, c2, c3 are predefined penalty factors.

[0021] Furthermore, the high-conflict-risk UAVs in the conflict relationship weight network are used as prior information, and a genetic algorithm is used for parallel search to obtain the optimal multi-aircraft maneuver sequence opt that minimizes the utility function. seq ,include:

[0022] Population initialization: According to the risk level of drone conflict Φ i Sort from high to low to generate the first generation chromosome Pop 0 (p1); To ensure population diversity, Pop 0 (p1) Initialize the population under the guidance of prior knowledge and generate other (N-1) primary chromosomes Pop 0 (p m )←f(Pop 0 (p1),s ij ), m = 2, ..., N; where sij Pop 0 (p1) The exchange function between gene position i and gene position j:

[0023]

[0024] Among them: ij is a random number in [0,1];

[0025] Genetic operation: Use roulette wheel to generate parent chromosomes with probability P crossover Select the individuals to participate in the crossover and use the two-point crossover operator to operate; with probability P mutate Select individuals to participate in the mutation, randomly select two gene sites in the individuals, and exchange genes;

[0026] Repeat iteration: Determine whether the termination condition is reached. If not, repeat the operation; otherwise, output the best chromosome.

[0027] In a second aspect, the present invention provides a multi-UAV cooperative sensing and collision avoidance device under limited communication conditions, comprising:

[0028] Situational awareness module, used to conduct situational awareness with the help of drone-mounted sensing equipment, and predict and assess the risk of conflict and collision with surrounding drones;

[0029] The network modeling module is used to establish a conflict relationship weight network to effectively identify high-risk drones in the network;

[0030] The sequential solution module is used to combine the high-risk UAV prior information provided by the conflict relationship weight network to determine the optimal collision avoidance maneuver sequence of multiple UAVs considering communication cost and maneuver cost;

[0031] The maneuver update module is used to maneuver in an orderly manner according to the optimal collision avoidance maneuver sequence and update the flight status externally using the communication link.

[0032] Furthermore, the situation awareness module includes a situation data acquisition unit and a conflict and collision risk detection unit; wherein the situation data acquisition unit is used to collect flight situation information; and the conflict and collision risk detection unit is used to predict whether the interval distance between the drones is less than the safety interval standard.

[0033] Furthermore, the network modeling module includes a conflict occurrence time calculation unit, a conflict weight quantification unit, an edge generation unit and a risk ranking unit; the conflict occurrence time calculation unit is used to obtain the closest time between conflicting drones; the conflict weight quantification unit is used to characterize the conflict weights of multiple drones; the edge generation unit is used to construct drone conflict relationship weight network edges; and the risk ranking unit is used to screen drones with high conflict risks.

[0034] Furthermore, the sequential solution module includes a utility function definition unit and a solution space search unit; the utility function definition unit is used to measure the economy and safety of the collision avoidance maneuver sequence; the solution space search unit is used to generate the optimal collision avoidance maneuver sequence in combination with the prior information of the high conflict risk UAV.

[0035] Furthermore, the maneuver update module includes an orderly collision avoidance maneuver unit and a situation update unit; the orderly collision avoidance maneuver unit is used to generate a minimum offset speed that does not conflict with the preceding UAV for the second time; the situation update unit is used to realize the situation information transmission after the UAV updates the maneuver.

[0036] The beneficial effects of the present invention are as follows: it can solve the problem of multi-UAV collaborative conflict-free trajectory planning with communication constraints, ensure that multi-UAVs in an airspace can achieve collision avoidance maneuvers with the help of communication collaboration under limited communication resources, and ultimately reach the expected target point under the premise of reducing unnecessary communications, and avoid mutual collisions during flight; multi-UAV collaborative perception and collision avoidance under limited communication conditions means ensuring that the distance between multiple UAVs always meets the safety interval constraint (i.e., no conflict) while reducing the use of communication resources required for multi-UAV collision avoidance maneuvers to update the flight status.

[0037] Additional advantages of the present invention will be more clearly presented in the following description or learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 This is a flow chart of the method for collaborative sensing and collision avoidance of multiple UAVs under limited communication conditions described in an embodiment of the present invention.

[0040] Figure 2 Schematic diagram of the detection principle of the risk of conflict and collision of drones according to an embodiment of the present invention.

[0041] Figure 3 A flowchart of multi-UAV conflict relationship weight network modeling according to an embodiment of the present invention.

[0042] Figure 4 This is a process diagram for solving the collision avoidance maneuver sequence based on prior information of a high-conflict risk UAV according to an embodiment of the present invention.

[0043] Figure 5 This is a process diagram of sequential collision avoidance maneuvers of a UAV according to an embodiment of the present invention.

[0044] Figure 6 This is a structural diagram of a multi-UAV cooperative sensing and collision avoidance device under limited communication conditions described in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0046] It should be understood by those skilled in the art that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0047] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with that in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0048] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0049] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0050] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0051] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0052] In this embodiment, a multi-UAV collaborative perception and collision avoidance device under limited communication conditions is first provided. By setting a situation awareness module, a network modeling module, a sequential solution module and a maneuver update module, the flight situation information of the UAV airborne terminal is obtained, the risk of conflict and collision with surrounding UAVs is detected, and the conflict relationship weight network is used to reflect the degree of UAV conflict risk. The prior information of high-risk UAVs in the conflict relationship weight network is used to guide the search for the optimal collision avoidance maneuver sequence of multiple UAVs. Finally, the updated situation information after maneuvering is transmitted to the surrounding UAVs in sequence, so as to realize multi-UAV collaborative collision avoidance maneuvers under limited communication resources, and maximize the continuous safety and stability of the airspace with high-density operation of multiple UAVs.

[0053] like Figure 6 As shown, the multi-UAV cooperative perception and collision avoidance device under limited communication conditions may specifically include a situation awareness module 1, a network modeling module 2, a sequence solving module 3 and a maneuvering updating module 4. Among them, the situation awareness module 1 is used to realize the acquisition of UAV flight situation information and the detection of conflict collision risks; the network modeling module 2 is used to generate a conflict relationship weight network reflecting the degree of conflict risk of UAVs; the sequence solving module 3 is used to realize the solution of the optimal collision avoidance maneuvering sequence that minimizes the communication cost and the maneuvering cost; the maneuvering updating module 4 is used to timely update the situation information after the collision avoidance maneuver to the surrounding UAVs.

[0054] The situation awareness module 1 may include a situation data acquisition module 11 and a conflict collision risk detection module 12. The situation data acquisition module 11 is used to collect flight situation information such as the position and speed of the drone, and the conflict collision risk detection module 12 is used to predict whether the interval between drones is less than the safety interval standard.

[0055] The network modeling module 2 may include a conflict occurrence time calculation module 21, a conflict weight quantification module 22, an edge generation module 23 and a risk sorting module 24. The conflict occurrence time calculation module 21 is used to obtain the closest time between conflicting drones, the conflict weight quantification module 22 is used to characterize the conflict weights of multiple drones, the edge generation module 23 is used to construct the drone conflict relationship weight network edge, and the risk sorting module 24 is used to screen drones with high conflict risks.

[0056] The sequence solving module 3 may include a utility function definition module 31 and a solution space search module 32. The utility function definition module 31 is used to measure the economy and safety of the collision avoidance maneuver sequence, and the solution space search module 32 is used to quickly generate the optimal collision avoidance maneuver sequence in combination with the prior information of the high conflict risk UAV.

[0057] The maneuver update module 4 may include an ordered collision avoidance maneuver module 41 and a situation update module 42. The ordered collision avoidance maneuver module 41 is used to generate a minimum offset speed that does not conflict with the preceding UAV for a second time, and the situation update module 42 is used to realize the situation information transmission after the UAV updates the maneuver.

[0058] In this embodiment, the above-mentioned multi-UAV cooperative perception and collision avoidance device under limited communication conditions is used to implement a multi-UAV cooperative perception and collision avoidance method under limited communication conditions. First, the situational awareness of the UAV airborne terminal is used to detect whether there is a risk of conflict and collision in the future, and then the UAV conflict relationship weight network modeling is used to quantitatively characterize the degree of conflict risk between UAVs. Furthermore, the high-conflict risk UAVs in the conflict relationship weight network are used as prior information to solve the collision avoidance maneuver sequence plan that minimizes the communication cost and maneuvering cost, and finally the communication link is used to update the situation information of the UAV after the sequential maneuvers. This can minimize the communication burden and maneuvering cost in the multi-UAV collaboration process and ensure the continuous safety and stability of the airspace.

[0059] Cooperative perception and collision avoidance of multiple UAVs under limited communication conditions refers to reducing the use of communication resources required for multiple UAVs to update flight status during collision avoidance maneuvers while ensuring that the distance between multiple UAVs always meets the safety interval constraint (i.e., no conflict). Cooperative perception and collision avoidance of multiple UAVs is an important means to ensure the autonomy and safety of aircraft operations. Under limited communication resources, conflicting UAVs update their maneuvering status information through communication links with the goal of minimizing communication costs and maneuvering costs, which can effectively achieve cooperative collision avoidance among multiple UAVs and ensure the continuous safety and stability of high-density operation airspace. The cooperative perception and collision avoidance method of multiple UAVs under limited communication conditions first uses the situational awareness of the UAV onboard terminal to detect whether there is a risk of conflict and collision in the future, and then uses the conflict relationship weight network modeling of the UAV to quantitatively characterize the degree of conflict risk between UAVs. Furthermore, the high-conflict risk UAVs in the conflict relationship weight network are used as prior information to solve the collision avoidance maneuvering sequence plan that minimizes the communication cost and maneuvering cost, and finally the communication link is used to update the situation information of the UAVs after the sequential maneuvers. In this way, the communication burden and maneuvering cost in the collaborative process of multiple UAVs can be minimized, and the continuous safety and stability of the airspace can be ensured.

[0060] like Figure 1 As shown in the figure, the multi-UAV cooperative perception and collision avoidance method under limited communication conditions specifically includes the following steps:

[0061] Step 101, UAV airborne situation awareness.

[0062] The drone receives data from optoelectronic, infrared, laser, radar and other sensors on board, calculates the flight status information such as the position and speed of surrounding drones in the airspace, and detects whether there is a risk of conflict or collision in the future. Figure 2 As shown, the specific conflict detection principle is as follows: is the relative position vector between UAV A and UAV B, is the relative velocity vector between the two machines, is the closest distance between the two aircraft, satisfying If the closest distance between the two aircraft is less than the given safety interval constraint, that is, It is considered that there is a possibility of conflict between the two machines.

[0063] Step 102 , the UAV conflict relationship weight network model is constructed to quantify the conflict weights between the UAVs according to the conflict risk levels between the conflicting UAVs.

[0064] like Figure 3 As shown, step 102 specifically includes the following steps:

[0065] (1) Calculate the conflict time T between conflicting drones cpa , if the conflict occurs within the time threshold T o (i.e. one time step), then go to step (2);

[0066]

[0067] (2) Time T when a conflict occurs between multiple drones cpa,ij Normalize and quantify the conflict relationship weight w between UAV i and UAV j ij Among them, T min is the minimum conflict occurrence time. In principle, w ij The larger it is, the earlier the conflict will occur and the higher the risk of conflict;

[0068]

[0069] (3) Construct a multi-UAV conflict relationship weight network G. If the conflict relationship weight w between UAV i and UAV j is ij > 0, then nodes i and j in G establish an edge;

[0070] (4) Calculate the risk level of drone conflict Φ i , and sort them from high to low according to the degree of conflict risk.

[0071]

[0072] Step 103, solving the optimal collision avoidance maneuver sequence for multiple UAVs, and generating a collision avoidance maneuver sequence plan that minimizes communication cost and maneuvering cost under the guidance of the prior information of UAVs with high conflict risks.

[0073] Considering the small size of UAV airborne equipment, the communication equipment and communication system have limited communication bandwidth and transmission power, it is necessary to reduce unnecessary communication times in the process of multi-UAV collaborative collision avoidance. seq The safety and economic requirements should be met at the same time, and the utility function is defined as follows:

[0074]

[0075] Among them, the utility function consists of three parts: dev i is the maneuver cost of the UAV with maneuver order i, and They are the collision avoidance velocity vectors of the UAV respectively (the calculation method of the collision avoidance velocity vector adopts the speed obstacle method, and the algorithm principle is as follows Figure 5 ) and the original velocity vector. conf i is the number of conflicts after the UAV maneuvers, conf i =∑ j≠i r ij , if the UAV still conflicts with the UAV with maneuver order j, then r ij =1, otherwise 0. i The communication cost required to update the information of the UAV if the UAV takes a collision avoidance maneuver You need to update the speed situation externally, com i =1, otherwise 0. c1, c2, c3 are predefined penalty factors.

[0076] To obtain the optimal multi-machine maneuver sequence opt that minimizes the utility function seq , using the high conflict risk drones in the conflict relationship weight network as prior information, and using the genetic algorithm for parallel search, the specific solution process is as follows Figure 4 shown.

[0077] (1) Population initialization: In principle, high-conflict risk drones have a greater impact on the situation structure of multi-drone conflict scenarios. By adjusting the situation information of a few high-conflict risk drones, the conflict situation can be quickly alleviated and the complexity of conflict resolution can be reduced. To this end, according to the drone conflict risk level Φ i Sort from high to low to generate the first generation chromosome Pop 0 (p1). To ensure population diversity, Pop 0(p1) Initialize the population under the guidance of prior knowledge and generate other (N-1) primary chromosomes Pop 0 (p m )←f(Pop 0 (p1),s ij ), m = 2, ..., N. Among them, s ij Pop 0 (p1) The exchange function between gene position i and gene position j:

[0078]

[0079] λ ij is a random number between [0,1]. In principle, the closer the distance between two gene loci is, the higher the risk of conflict is, and the higher the probability of exchanging the two gene loci is.

[0080] (2) Genetic operations: Perform genetic operations such as selection, crossover, and mutation on the initial population. Specifically, a roulette wheel is used to generate parent chromosomes with probability P crossover Select the individuals to participate in the crossover and use the two-point crossover operator to operate. mutate Select individuals to participate in the mutation, randomly select two gene loci in the individuals, and exchange genes.

[0081] (3) Repeated iteration: Determine whether the termination condition is met. If not, repeat the operation; otherwise, output the best chromosome.

[0082] Step 104, multiple UAVs maneuver in sequence and update situation information. According to a given optimal collision avoidance maneuver sequence, the UAVs perform orderly collision avoidance maneuvers and use communication links to update corresponding maneuver situation information externally.

[0083] The specific collision avoidance maneuver process is as follows: Figure 5 As shown in the figure, the minimum offset speed obtained by the speed obstacle method for the UAV with maneuver order j is Subsequently, with the help of the drone communication link, the collision avoidance maneuver situation information is updated and transmitted to the surrounding drones, thereby maximizing the coordination between drones and ensuring a conflict-free flight scenario.

[0084] In summary, the method and device for collaborative perception and collision avoidance of multiple UAVs under limited communication conditions described in the embodiments of the present invention realizes the detection of conflict and collision risks with surrounding UAVs through the situation awareness of the UAV airborne end, and then establishes a conflict relationship weight network that quantifies the degree of conflict risk of multiple UAVs according to the time when the conflict and collision between the UAVs occur. The high-conflict risk UAVs in the conflict relationship weight network are used as prior information to guide the solution of the optimal collision avoidance maneuver sequence that minimizes communication cost and maneuvering cost. The UAVs maneuver in sequence and update the maneuvering situation information to the outside in a timely manner with the help of the communication link. The present invention thus realizes the collaborative and safe flight of multiple UAVs under conditions of limited communication bandwidth, and meets the requirements for continuous safety and stability of the airspace in an environment with limited communication resources.

[0085] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0089] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative labor on the basis of the technical solution disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-UAV cooperative sensing and collision avoidance method under limited communication conditions, characterized in that: include: Use drone-mounted sensing equipment to conduct situational awareness, predict and assess the risk of conflict and collision with surrounding drones; Establish a conflict relationship weight network to effectively identify high-risk drones in the network; Combined with the high-risk UAV prior information provided by the conflict relationship weight network, the optimal collision avoidance maneuvering sequence of multiple UAVs considering communication cost and maneuvering cost is determined; including: establishing a utility function for the multi-machine maneuvering sequence to meet both safety and economic requirements; using the high-conflict risk UAVs in the conflict relationship weight network as prior information, using genetic algorithms for parallel search, and obtaining the optimal multi-machine maneuvering sequence that minimizes the utility function; where, The utility function is: Among them, the utility function consists of three parts: dev i is the maneuver cost of the UAV with maneuver order i, and are the collision avoidance velocity vector and original velocity vector of the UAV respectively; conf i is the number of conflicts after the UAV maneuvers, conf i =∑ j≠i r ij , if the UAV still conflicts with the UAV with maneuver order j, then r ij =1, otherwise 0; com i The communication cost required to update the information of the UAV is You need to update the speed situation externally, com i =1, otherwise 0; c1, c2, c3 are predefined penalty factors; Using the high-conflict risk drones in the conflict relationship weight network as prior information, a genetic algorithm is used for parallel search to obtain the optimal multi-machine maneuver sequence opt that minimizes the utility function. seq ,include: Population initialization: According to the risk level of drone conflict Φ i Sort from high to low to generate the first generation chromosome Pop 0 (p1); To ensure population diversity, Pop 0 (p1) Initialize the population under the guidance of prior knowledge and generate other N-1 primary chromosomes Pop 0 (p m )←f(Pop 0 (p1),s ij ), m = 2, ..., N; where s ij Pop 0 (p1) The exchange function between gene position i and gene position j: Among them: ij is a random number in [0,1]; Genetic operation: Use roulette wheel to generate parent chromosomes with probability P crossover Select the individuals to participate in the crossover and use the two-point crossover operator to operate; with probability P mutate Select individuals to participate in the mutation, randomly select two gene sites in the individuals, and exchange genes; Repeated iteration: determine whether the termination condition is met, if not, repeat the operation, otherwise output the best chromosome; Maneuver in an orderly manner according to the optimal collision avoidance maneuver sequence, and use communication links to update the flight status externally.

2. The method for multi-UAV cooperative sensing and collision avoidance under limited communication conditions according to claim 1 is characterized in that: The UAV airborne sensing equipment is used to perform situational awareness, predict and evaluate the risk of conflict and collision with surrounding UAVs, including: calculating the flight situation information of surrounding UAVs in the airspace and predicting whether there is a risk of conflict and collision; if the closest distance between the two UAVs is less than the given safety interval constraint r safe ,Right now It is considered that there is a possibility of conflict between the two aircraft; is the relative velocity vector between the two machines, is the closest distance between the two aircraft, satisfying 3. The method for multi-UAV cooperative sensing and collision avoidance under limited communication conditions according to claim 1 is characterized in that: Establish a conflict relationship weight network to effectively identify high-risk drones in the network, including: (1) Calculate the conflict time T between conflicting drones cpa , if the conflict occurs within the time threshold T o If it is within the range, go to step (2); (2) Time T when a conflict occurs between multiple drones cpa,ij Normalize and quantify the conflict relationship weight w between UAV i and UAV j ij ; (3) Construct a multi-UAV conflict relationship weight network G. If the conflict relationship weight w between UAV i and UAV j is ij > 0, then nodes i and j in G establish an edge; (4) Calculate the risk level of drone conflict Φ i , and sort them from high to low according to the degree of conflict risk.

4. A multi-UAV cooperative sensing and collision avoidance device under limited communication conditions, characterized in that: include: Situational awareness module, used to conduct situational awareness with the help of drone-mounted sensing equipment, and predict and assess the risk of conflict and collision with surrounding drones; The network modeling module is used to establish a conflict relationship weight network to effectively identify high-risk drones in the network; The sequential solution module is used to combine the high-risk UAV prior information provided by the conflict relationship weight network to determine the optimal collision avoidance maneuvering sequence of multiple UAVs considering communication cost and maneuvering cost; including: establishing a utility function for the multi-machine maneuvering sequence to meet both safety and economic requirements; using the high-conflict risk UAVs in the conflict relationship weight network as prior information, using genetic algorithms for parallel search, and obtaining the optimal multi-machine maneuvering sequence that minimizes the utility function; wherein, The utility function is: Among them, the utility function consists of three parts: dev i is the maneuver cost of the UAV with maneuver order i, and are the collision avoidance velocity vector and original velocity vector of the UAV respectively; conf i is the number of conflicts after the UAV maneuvers, conf i =∑ j≠i r ij , if the UAV still conflicts with the UAV with maneuver order j, then r ij =1, otherwise 0; com i The communication cost required to update the information of the UAV is You need to update the speed situation externally, com i =1, otherwise 0; c1, c2, c3 are predefined penalty factors; Using the high-conflict risk drones in the conflict relationship weight network as prior information, a genetic algorithm is used for parallel search to obtain the optimal multi-machine maneuver sequence opt that minimizes the utility function. seq ,include: Population initialization: According to the risk level of drone conflict Φ i Sort from high to low to generate the first generation chromosome Pop 0 (p1); To ensure population diversity, Pop 0 (p1) Initialize the population under the guidance of prior knowledge and generate other N-1 primary chromosomes Pop 0 (p m )←f(Pop 0 (p1),s ij ), m = 2, ..., N; where s ij Pop 0 (p1) The exchange function between gene position i and gene position j: Among them: ij is a random number in [0,1]; Genetic operation: Use roulette wheel to generate parent chromosomes with probability P crossover Select the individuals to participate in the crossover and use the two-point crossover operator to operate; with probability P mutate Select individuals to participate in the mutation, randomly select two gene sites in the individuals, and exchange genes; Repeated iteration: determine whether the termination condition is met, if not, repeat the operation, otherwise output the best chromosome; The maneuver update module is used to maneuver in an orderly manner according to the optimal collision avoidance maneuver sequence and update the flight status externally using the communication link.

5. The multi-UAV cooperative sensing and collision avoidance device under limited communication conditions according to claim 4 is characterized in that: The situation awareness module includes a situation data acquisition unit and a conflict and collision risk detection unit; wherein the situation data acquisition unit is used to collect flight situation information; and the conflict and collision risk detection unit is used to predict whether the interval distance between drones is less than the safety interval standard.

6. The multi-UAV cooperative sensing and collision avoidance device under limited communication conditions according to claim 4 is characterized in that: The network modeling module includes a conflict occurrence time calculation unit, a conflict weight quantification unit, an edge generation unit and a risk ranking unit; The conflict occurrence time calculation unit is used to obtain the closest approach time between conflicting UAVs; The conflict weight quantification unit is used to characterize the conflict weights of multiple UAVs; The edge generation unit is used to construct the weighted network edges of the UAV conflict relations; The risk ranking unit is used to screen drones with high conflict risk.

7. The multi-UAV cooperative sensing and collision avoidance device under limited communication conditions according to claim 4 is characterized in that: The sequential solution module includes a utility function definition unit and a solution space search unit; the utility function definition unit is used to measure the economy and safety of the collision avoidance maneuver sequence; the solution space search unit is used to generate the optimal collision avoidance maneuver sequence in combination with the prior information of the high conflict risk UAV.

8. The multi-UAV cooperative sensing and collision avoidance device under limited communication conditions according to claim 4 is characterized in that: The maneuver update module includes an orderly collision avoidance maneuver unit and a situation update unit; the orderly collision avoidance maneuver unit is used to generate the minimum offset speed without secondary conflict with the preceding UAV; the situation update unit is used to realize the situation information transmission after the UAV updates the maneuver.

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