Unmanned aerial vehicle anti-collision method and system based on LoRa communication

Abstract

The invention discloses an unmanned aerial vehicle anti-collision method and system based on LoRa communication, and belongs to the technical field of risk avoidance of multiple unmanned aerial vehicles. The method comprises the following steps: constructing an ad hoc network communication architecture to realize flight state broadcast and synchronization; fusing the multi-source data by adopting a space-time alignment algorithm, and constructing a dynamic airspace situation map taking a local machine as a center; calculating a time window and space overlapping probability of the predicted intersection; a multi-priority dynamic negotiation protocol is considered, and autonomous decision making of the unmanned aerial vehicle cluster in a scene without a central node is supported; and dynamically adjusting the flight path, speed and height through a collaborative obstacle avoidance algorithm. Through a spread spectrum modulation technology and an adaptive power control mechanism, over-the-horizon communication coverage of more than 10km level is realized; through fusion of a kinematics prediction model and a risk quantification algorithm, millisecond identification and hierarchical response of collision threats are realized, a global optimal avoidance strategy is achieved, communication delay and calculation bottleneck brought by centralized decision are effectively avoided, and the real-time collaborative obstacle avoidance capability in a complex airspace is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-UAV risk avoidance, and in particular relates to a UAV anti-collision method and system based on LoRa communication. Background Art

[0002] Traditional collision avoidance systems primarily rely on the single-machine perception capabilities of onboard sensors (such as vision, radar, and GPS). Their effective range is limited by the physical characteristics of the sensors, making them incapable of meeting the requirements for multi-machine collaborative obstacle avoidance in beyond-visual-range and highly dynamic environments. Especially in complex electromagnetic environments or heavily obscured operating areas, a single perception mode is prone to data distortion or blind spots, rendering collision risk assessment ineffective. Furthermore, existing systems often utilize cellular networks for inter-UAV communication, which presents challenges such as strong reliance on public network coverage, high communication costs, and uncontrollable data transmission delays. This makes it difficult to meet the comprehensive real-time, cost-effective, and safety requirements of large-scale swarm operations.

[0003] Currently, drone collaborative obstacle avoidance technology faces three core challenges. First, traditional communication technologies struggle to balance coverage and power efficiency. Short-range communications (such as WiFi and ZigBee) are limited by transmission distance, while cellular networks (such as 5G) suffer from base station dependence and excessive energy consumption. Second, centralized decision-making architectures are prone to single points of failure, and the real-time response capabilities of multi-drone collaborative obstacle avoidance are limited by computing and communication bottlenecks at the central node. Third, there is a lack of unified standards for the collaborative control of heterogeneous drone swarms. Existing systems generally suffer from poor hardware compatibility and insufficient protocol openness, resulting in weak cross-platform collaboration. These technical shortcomings severely restrict the reliable deployment and application expansion of drone swarms in environments without public network support, such as remote mountainous areas and offshore operations.

[0004] Breakthroughs in low-power wide-area IoT (LPWAN) technology offer new solutions to these challenges. LoRa technology, with its sub-GHz frequency band transmission, adaptive rate adjustment, and forward error correction coding, significantly optimizes long-range communication and low-power performance. However, existing research has primarily focused on the application of LoRa in static IoT nodes, and a comprehensive communication-control collaborative architecture for dynamic drone swarms has yet to be established. Building a decentralized, real-time information exchange network based on LoRa and designing a compatible distributed obstacle avoidance decision-making mechanism are key breakthroughs in improving the safety and collaborative efficiency of drone swarm operations.

[0005] A Chinese patent application (Application Number: 202411148051.9) discloses a method and system for fixed-wing swarm drone collision avoidance based on multi-element game theory. The system includes the following steps: Step 1: role selection and positioning and orientation analysis; Step 2: performing a collision avoidance item calculation sub-step on the leader side, and a collision avoidance item calculation sub-step on the wingman side. This invention can adapt to the swarm control mode and implement different collision avoidance strategies for different roles. Each aircraft calculates the global situation for each object it needs to avoid, obtaining multiple collision avoidance elements. Through updating, all collision avoidance elements are game-determined to obtain the final collision avoidance strategy. This solves the problems of incomplete situation, short collision avoidance time, and additional risks caused by route deviation in traditional methods.

[0006] A Chinese patent application (Application No. 201811486092.3) discloses a ZigBee-based drone collision avoidance method, drone, and server. This method involves using drones to establish an ad hoc network using ZigBee. The drones act as network nodes in the ad hoc network; the drones transmit flight missions to the server via the ZigBee network; the drones adopt multiple flight strategies; and during flight, the drones establish data channels with other drones via the ZigBee network through the server. The drones then select a flight strategy based on the instructions generated by the server. The method utilizes the communication modules in the ZigBee network to coordinate communication between thousands of tiny sensors. These sensors transmit data from one sensor to another via radio waves, requiring minimal energy. This method offers very high communication efficiency and enables more efficient real-time drone collision avoidance. This invention has applications in the field of drone collision avoidance.

[0007] The patented method and system for preventing collisions of fixed-wing swarm drones based on multi-element game theory has the following deficiencies: 1. Reliance on complex game theory calculations results in insufficient real-time performance, and the communication method is not optimized and lacks a dynamic priority mechanism;

[0008] 2. Generate avoidance strategies through multi-element global situation calculations and game-based decision-making, which has high computational complexity and makes it difficult to quickly respond to collision risks within the beyond-visual-range range.

[0009] Patented UAV anti-collision method based on ZigBee network, UAV and server have the following shortcomings: 1. Short communication distance, reliance on centralized server and limited network capacity;

[0010] 2. The transmission distance of the ZigBee network is limited, which makes it difficult to meet the needs of drones' beyond-visual-range collaborative flight. In particular, communication interruptions are prone to occur in large-scale scenarios such as logistics and transportation, which can easily lead to the failure of anti-collision.

[0011] Therefore, how to solve the problems of insufficient communication coverage and excessive energy consumption in wide-area collaborative scenarios, as well as delayed obstacle avoidance decisions and path conflicts in dynamic airspace is the technical problem that the present invention aims to solve. Summary of the Invention

[0012] The purpose of the present invention is to provide a UAV collision avoidance method based on LoRa communication to solve the problems raised in the above background technology.

[0013] The object of the present invention is achieved as follows: a UAV anti-collision method based on LoRa communication, characterized in that the method comprises the following steps:

[0014] Step S1: Build an ad hoc network communication architecture to achieve flight status broadcast and synchronization;

[0015] Step S2: Use the spatiotemporal alignment algorithm to fuse the multi-source data and construct a dynamic airspace situation map centered on the aircraft, updating the position, speed, and heading information of surrounding UAVs in real time;

[0016] Step S3: Based on the kinematic prediction model and risk quantification algorithm, the time window and spatial overlap probability of the expected intersection point are calculated;

[0017] Step S4: Design a multi-priority dynamic negotiation protocol to support autonomous decision-making of drone clusters in scenarios without a central node;

[0018] Step S5: Generate a multi-objective optimized trajectory based on the negotiation results, and dynamically adjust the flight path, speed, and altitude through a collaborative obstacle avoidance algorithm;

[0019] Step S6: The UAV performs a climb, yaw, or deceleration operation along the newly planned trajectory, while continuously broadcasting the adjusted flight status.

[0020] Preferably, the ad hoc network communication architecture includes a LoRa wireless communication mechanism, a hybrid communication mechanism, and a time synchronization mechanism. The LoRa wireless communication mechanism adopts spread spectrum modulation and an adaptive power control mechanism, adaptively optimizes communication parameters according to channel quality and communication distance, monitors environmental interference intensity and the relative motion state of the drone in real time, and dynamically selects the optimal spreading factor and bandwidth combination, specifically:

[0021] Define the joint optimization function of spreading factor (SF) and bandwidth (BW):

[0022] ;

[0023] in, is the received signal strength, is the signal-to-noise ratio, is the safety distance threshold;

[0024] The hybrid communication mechanism uses periodic broadcast and event-driven intensive communication technology. The periodic broadcast intensive communication technology is used during normal cruising, broadcasting flight status at a low frequency; the event-driven intensive communication technology can automatically increase the broadcast frequency when collision risk occurs.

[0025] Introduce the broadcast frequency control function, the expression is:

[0026] ;

[0027] The time synchronization mechanism uses the broadcast timestamp information to fuse with the flight control IMU clock to ensure the time consistency of data received by different drones.

[0028] Preferably, in step S2, a spatiotemporal alignment algorithm is used to fuse the multi-source data, and a dynamic airspace situation map centered on the aircraft is constructed, and the position, speed and heading information of the surrounding UAVs are updated in real time, specifically:

[0029] Step S2-1: Use the spatiotemporal alignment algorithm to fuse multi-source data, specifically:

[0030] Time alignment of received data, that is, using the local receiving time With receiving delay Map the received data to the local unified time base. The expression is:

[0031] ;

[0032] in, Contains the sender timestamp for each data frame. ;

[0033] For the same moment The data from different sensors are fused using Kalman filtering, which can be expressed as:

[0034] ;

[0035] in, is the predicted state, is the Kalman gain, is the measurement matrix;

[0036] Step S2-2: Construct a dynamic airspace situation map centered on the aircraft.

[0037] Preferably, in step S2, a spatiotemporal alignment algorithm is used to fuse the multi-source data, and a dynamic airspace situation map centered on the aircraft is constructed, and the position, speed and heading information of the surrounding UAVs are updated in real time, specifically:

[0038] Step S2-1: Use the spatiotemporal alignment algorithm to fuse multi-source data, specifically:

[0039] Time alignment of received data, that is, using the local receiving time With receiving delay Map the received data to the local unified time base. The expression is:

[0040] ;

[0041] in, Contains the sender timestamp for each data frame. ;

[0042] For the same moment The data from different sensors are fused using Kalman filtering, which can be expressed as:

[0043] ;

[0044] in, is the predicted state, is the Kalman gain, is the measurement matrix;

[0045] Step S2-2: Construct a dynamic airspace situation map centered on the aircraft.

[0046] Preferably, the step S2-2 constructs a dynamic airspace situation map centered on the aircraft, specifically:

[0047] The fused and spatiotemporally aligned state data of each drone is converted into the local coordinate system of the drone to obtain the relative position vector and the corresponding relative speed , and calculate the relative distance, the calculation formula is:

[0048] ;

[0049] After summarizing the relative position information, relative speed and heading information of all neighboring UAVs, a two-dimensional or three-dimensional dynamic airspace situation map centered on the UAV is constructed. In the dynamic situation map, the information of each UAV node can be expressed in vector form, as follows:

[0050] ;

[0051] in, The heading angle information of the UAV is provided as input to the collision risk model, which is used to calculate the future intersection position and collision possibility.

[0052] Preferably, in step S3, the time window and spatial overlap probability of the expected intersection point are calculated based on the kinematic prediction model and the risk quantification algorithm, specifically:

[0053] Step S3-1: Construct a kinematic prediction model based on the uniformly accelerated linear motion assumption to predict the future time window of each drone. The flight trajectory within is deduced, and the expression is:

[0054] ;

[0055] in, is the current position, is the current speed, is the estimated average acceleration, is the position at the predicted moment;

[0056] Step S3-2: After identifying the potential intersection trajectory, calculate the collision risk index , specifically:

[0057] ;

[0058] in, is the minimum relative distance between UAVs within the prediction time window; To arrive from the current time The time required, i.e. the collision time margin;

[0059] Step S3-3: triggering the hierarchical response and robustness assurance mechanism, specifically:

[0060] Setting risk thresholds 、 Implement collision response classification: If , it is judged as low risk, and the system only performs trajectory recording and status monitoring;

[0061] like , it is judged as medium risk, the system sends an obstacle avoidance warning signal and prepares to enter the obstacle avoidance negotiation process;

[0062] like , determined to be high risk, the system immediately triggers the distributed obstacle avoidance mechanism, broadcasts a synchronous collaborative obstacle avoidance request to the conflicting UAVs through LoRa, and enters the trajectory adjustment phase.

[0063] Preferably, a multi-priority dynamic negotiation protocol is designed in step S4, which specifies a priority processing mechanism for multiple drones, specifically:

[0064] Step S4-1: Generate an obstacle avoidance strategy based on a dynamic priority decision model with multi-dimensional weights, taking into account task urgency, remaining energy, and relative motion state;

[0065] The priority weight function is expressed as:

[0066] ;

[0067] in, is the task priority coefficient, is the energy status factor, is the real-time collision risk value;

[0068] Step S4-2: Adopt a two-stage asynchronous confirmation mechanism:

[0069] After identifying a pair of drones at risk of collision, the initiator broadcasts a proposal frame containing alternative detour paths and sends candidate obstacle avoidance paths to potential conflicting nodes. The receiver returns confirmation feedback information based on its own risk assessment and priority results, including whether to accept the proposal or propose an alternative path. The negotiation process does not rely on synchronous control and adopts an asynchronous confirmation strategy, allowing each node to respond asynchronously based on its own computing resources. The response feedback is counted within a set time limit. When the majority of nodes participating in the negotiation reach a consensus, the consistent trajectory adjustment instruction is triggered, and the subsequent flight path update phase begins.

[0070] Preferably, in step S5, a multi-objective optimized trajectory is generated based on the negotiation result, and the flight path, speed and altitude are dynamically adjusted through a collaborative obstacle avoidance algorithm, specifically:

[0071] Step S5-1: Define collision risk function:

[0072] ;

[0073] in, is the relative position vector between UAVs, is the estimated time of collision;

[0074] Step S5-2: Generate a smooth obstacle avoidance path that complies with dynamic constraints, specifically:

[0075] The obstacle avoidance trajectory is generated by minimizing the weighted objective function of path risk and adjusting energy consumption. The optimization objective function is defined as:

[0076] ;

[0077] in, To generate the residual collision risk function of the path during the obstacle avoidance process, The energy consumption caused by track deviation is related to factors such as flight distance and climb / descent amplitude; 、 Risk weight and energy consumption weight factors are used to adjust the system's balance preference between safety and energy efficiency;

[0078] The trajectory is parameterized using a third-order Bezier curve to generate a smooth path that satisfies the dynamic constraints. The trajectory is expressed as:

[0079] ;

[0080] in, is the current drone position; The position of the target after obstacle avoidance or the original track return point; 、 The control point is determined by the heading angle, speed change rate and target offset to ensure that the trajectory is smooth and the steering angle is continuous; after the trajectory is generated, the system verifies the trajectory curvature in real time to ensure that the heading change rate does not exceed the maximum steering rate threshold allowed by the flight control system and broadcast the path parameters to the cooperative drone nodes through the LoRa network.

[0081] The UAV anti-collision system based on LoRa communication is characterized by comprising the UAV anti-collision method based on LoRa communication according to any one of claims 1 to 6;

[0082] The UAV anti-collision system includes a LoRa communication module, a flight status receiving module, a collision risk assessment module, a negotiation module, a flight trajectory adjustment module, and a heterogeneous device compatible architecture. The LoRa communication module is used to support wide-area broadcasting and reception of flight status information between UAVs;

[0083] After receiving flight status data from other drones, the flight status receiving module verifies the integrity of the information through a multi-source data verification mechanism to eliminate errors caused by communication packet loss or interference;

[0084] The collision risk assessment module calculates the intersection probability and collision time window of the relative motion trajectories of multiple aircraft in real time based on the kinematic prediction model and risk quantification algorithm;

[0085] The negotiation module is used to design a multi-priority dynamic negotiation protocol to support autonomous decision-making of drone clusters in scenarios without a central node;

[0086] The flight trajectory adjustment module generates a multi-objective optimized trajectory based on the negotiation results and dynamically adjusts the flight path, speed, and altitude through a collaborative obstacle avoidance algorithm. The flight trajectory adjustment module has a built-in trajectory smoothing mechanism to minimize energy loss caused by sudden changes in heading while avoiding collisions, and synchronizes the adjusted state parameters to the cluster via the LoRa network in real time.

[0087] The heterogeneous device compatible architecture is used to fuse with sensors and perform information sharing and collaboration among multiple drones through LoRa wireless communication.

[0088] Compared with the prior art, the present invention has the following improvements and advantages:

[0089] 1. Based on the LoRa self-organizing network communication architecture, through spread spectrum modulation technology and adaptive power control mechanism, it achieves beyond-line-of-sight communication coverage of more than 10km, while reducing the power consumption of single-machine communication to less than 20% of traditional cellular solutions; adopting a hybrid transmission mode combining event triggering and periodic broadcasting, while ensuring the real-time delivery of key obstacle avoidance information, it maximizes the flight time of drones and provides reliable communication guarantee for long-term cluster operations in remote areas and without public network environments.

[0090] 2. By integrating kinematic prediction models with risk quantification algorithms, millisecond-level identification and graded response to collision threats are achieved. When a potential conflict is detected, each drone autonomously generates an obstacle avoidance proposal based on a preset rule set, conducts multiple rounds of negotiations through the LoRa network, and reaches a globally optimal avoidance strategy. This effectively avoids the communication delays and computational bottlenecks caused by traditional centralized decision-making, ensuring real-time collaborative obstacle avoidance capabilities in complex airspace.

[0091] 3. By adopting a lightweight edge computing framework, the computing power requirements of the core obstacle avoidance algorithm are reduced, so that existing drones only need to undergo low-cost firmware upgrades to obtain cluster collaboration capabilities, significantly reducing the deployment and maintenance costs of large-scale drone clusters in logistics, agriculture, emergency response and other fields.

[0092] 4. Through open hardware interfaces and standardized communication protocol stacks, it supports drone platforms from different manufacturers to quickly access the system through modular components. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 Schematic diagram of the process of the present invention.

[0094] Figure 2 This is a structural diagram of the UAV collision avoidance system.

[0095] Figure 3 Schematic diagram of the drone's obstacle avoidance trajectory. DETAILED DESCRIPTION

[0096] The present invention is further summarized below with reference to the accompanying drawings.

[0097] like Figure 1 As shown, the UAV anti-collision method based on LoRa communication includes the following steps:

[0098] Step S1: Build an ad hoc network communication architecture to achieve flight status broadcast and synchronization;

[0099] The ad hoc network communication architecture includes the LoRa wireless communication mechanism, hybrid communication mechanism, and time synchronization mechanism. The LoRa wireless communication mechanism adopts spread spectrum modulation and adaptive power control mechanism. It adaptively optimizes communication parameters according to channel quality and communication distance, monitors environmental interference intensity and the relative motion state of the drone in real time, and dynamically selects the optimal spreading factor and bandwidth combination. Specifically:

[0100] Define the joint optimization function of spreading factor (SF) and bandwidth (BW):

[0101] ;

[0102] in, is the received signal strength, is the signal-to-noise ratio, is the safety distance threshold;

[0103] The hybrid communication mechanism uses periodic broadcast and event-driven intensive communication technologies. The periodic broadcast intensive communication technology is used during normal cruising phase, broadcasting flight status at a low frequency; the event-driven intensive communication technology can automatically increase the broadcast frequency when collision risk occurs.

[0104] Introduce the broadcast frequency control function, the expression is:

[0105] ;

[0106] The time synchronization mechanism uses the broadcast timestamp information to fuse with the flight control IMU clock to ensure the time consistency of data received by different drones.

[0107] Step S2: Use the spatiotemporal alignment algorithm to fuse the multi-source data and construct a dynamic airspace situation map centered on the aircraft itself, updating the position, speed, and heading information of surrounding UAVs in real time. Specifically:

[0108] Step S2-1: Use the spatiotemporal alignment algorithm to fuse multi-source data, specifically:

[0109] Time alignment of received data, that is, using the local receiving time With receiving delay Map the received data to the local unified time base. The expression is:

[0110] ;

[0111] in, Contains the sender timestamp for each data frame. ;

[0112] For the same moment The data from different sensors are fused using Kalman filtering, which can be expressed as:

[0113] ;

[0114] in, is the predicted state, is the Kalman gain, is the measurement matrix;

[0115] Step S2-2: Construct a dynamic airspace situation map centered on the aircraft, specifically:

[0116] The fused and spatiotemporally aligned state data of each drone is converted into the local coordinate system of the drone to obtain the relative position vector and the corresponding relative speed , and calculate the relative distance, the calculation formula is:

[0117] ;

[0118] After summarizing the relative position information, relative speed and heading information of all neighboring UAVs, a two-dimensional or three-dimensional dynamic airspace situation map centered on the UAV is constructed. In the dynamic situation map, the information of each UAV node can be expressed in vector form, as follows:

[0119] ;

[0120] in, The heading angle information of the UAV is provided as input to the collision risk model, which is used to calculate the future intersection position and collision possibility.

[0121] Step S3: Based on the kinematic prediction model and risk quantification algorithm, the time window and spatial overlap probability of the expected intersection point are calculated, specifically:

[0122] Step S3-1: Construct a kinematic prediction model based on the uniformly accelerated linear motion assumption to predict the future time window of each drone. The flight trajectory within is deduced, and the expression is:

[0123] ;

[0124] in, is the current position, is the current speed, is the estimated average acceleration, is the position at the predicted moment;

[0125] Step S3-2: After identifying the potential intersection trajectory, calculate the collision risk index , specifically:

[0126] ;

[0127] in, is the minimum relative distance between UAVs within the prediction time window; To arrive from the current time The time required, i.e. the collision time margin;

[0128] Step S3-3: triggering the hierarchical response and robustness assurance mechanism, specifically:

[0129] Setting risk thresholds 、 Implement collision response classification: If , it is judged as low risk, and the system only performs trajectory recording and status monitoring;

[0130] like , it is judged as medium risk, the system sends an obstacle avoidance warning signal and prepares to enter the obstacle avoidance negotiation process;

[0131] like , determined to be high risk, the system immediately triggers the distributed obstacle avoidance mechanism, broadcasts a synchronous collaborative obstacle avoidance request to the conflicting UAVs through LoRa, and enters the trajectory adjustment phase.

[0132] Step S4: Design a multi-priority dynamic negotiation protocol. The multi-priority dynamic negotiation protocol specifies the priority processing mechanism of multiple drones, specifically:

[0133] Step S4-1: Generate an obstacle avoidance strategy based on a dynamic priority decision model with multi-dimensional weights, taking into account task urgency, remaining energy, and relative motion state;

[0134] The multi-priority dynamic negotiation protocol is used for information exchange and collaboration between drones when there is a risk of collision. The protocol includes the following:

[0135] Information format: The protocol specifies the format of status information broadcast by the drone, including key parameters such as current position, speed, heading, and planned flight path;

[0136] Negotiation rules: When multiple drones receive signals of potential collision, they negotiate according to the rules in the protocol to determine whether they need to adjust their flight paths, change their speed or altitude to eliminate the risk of collision.

[0137] The protocol specifies a priority handling mechanism for multiple drones. It defines a dynamic priority decision model based on multi-dimensional weights, which generates an obstacle avoidance strategy based on the urgency of the mission, the remaining energy, and the relative motion state. The priority weight function is expressed as:

[0138] ;

[0139] in, is the task priority coefficient, is the energy status factor, is the real-time collision risk value;

[0140] Step S4-2: Adopt a two-stage asynchronous confirmation mechanism:

[0141] After identifying a pair of drones at risk of collision, the initiator broadcasts a proposal frame containing alternative detour paths and sends candidate obstacle avoidance paths to potential conflicting nodes. The receiver returns confirmation feedback information based on its own risk assessment and priority results, including whether to accept the proposal or propose an alternative path. The negotiation process does not rely on synchronous control and adopts an asynchronous confirmation strategy, allowing each node to respond asynchronously based on its own computing resources. The response feedback is counted within a set time limit. When the majority of nodes participating in the negotiation reach a consensus, the consistent trajectory adjustment instruction is triggered, and the subsequent flight path update phase begins.

[0142] Step S5: Generate a multi-objective optimized trajectory based on the negotiation results, and dynamically adjust the flight path, speed, and altitude through a collaborative obstacle avoidance algorithm. Specifically:

[0143] Step S5-1: Define collision risk function:

[0144] ;

[0145] in, is the relative position vector between UAVs, is the estimated time of collision;

[0146] Step S5-2: Generate a smooth obstacle avoidance path that complies with dynamic constraints, specifically:

[0147] The obstacle avoidance trajectory is generated by minimizing the weighted objective function of path risk and adjusting energy consumption. The optimization objective function is defined as:

[0148] ;

[0149] in, To generate the residual collision risk function of the path during the obstacle avoidance process, The energy consumption caused by track deviation is related to factors such as flight distance and climb / descent amplitude; 、 Risk weight and energy consumption weight factors are used to adjust the system's balance preference between safety and energy efficiency;

[0150] The trajectory is parameterized using a third-order Bezier curve to generate a smooth path that satisfies the dynamic constraints. The trajectory is expressed as:

[0151] ;

[0152] in, is the current drone position; The position of the target after obstacle avoidance or the original track return point; 、 The control point is determined by the heading angle, speed change rate and target offset to ensure that the trajectory is smooth and the steering angle is continuous; after the trajectory is generated, the system verifies the trajectory curvature in real time to ensure that the heading change rate does not exceed the maximum steering rate threshold allowed by the flight control system and broadcast the path parameters to the cooperative drone nodes through the LoRa network.

[0153] like Figure 2 As shown in the figure, the UAV collision avoidance system based on LoRa communication includes a LoRa communication module, a flight status receiving module, a collision risk assessment module, a negotiation module, a flight trajectory adjustment module, and a heterogeneous device compatible architecture. The LoRa communication module is used to support wide-area broadcasting and reception of flight status information between UAVs.

[0154] The LoRa communication module utilizes LoRa technology to build an ad hoc communication architecture, employing spread spectrum modulation and adaptive power control to support wide-area broadcast and reception of flight status information between drones. Each drone periodically broadcasts status data including position, speed, heading, and altitude over the LoRa wireless network, dynamically adjusting transmit power to account for environmental interference and changes in communication distance. The module also features a built-in hybrid transmission mode, employing low-frequency periodic broadcasts during normal flight to reduce power consumption. Upon detecting a collision risk, it automatically switches to an event-triggered, intensive communication mode, ensuring real-time transmission of critical obstacle avoidance commands.

[0155] Flight Status Receiving Module: After receiving flight status data from other drones, the module verifies the integrity of the information through a multi-source data verification mechanism, eliminating errors caused by communication packet loss or interference. The module integrates a spatiotemporal alignment algorithm to synchronize and fuse the received heterogeneous drone status information with the local drone's sensors (including GPS, radar, vision, and inertial navigation data). This creates a dynamic global airspace situation map, providing high-precision input for collision risk assessment.

[0156] Collision Risk Assessment Module: Based on a kinematic prediction model and risk quantification algorithm, it calculates the intersection probability and collision time window of multiple aircraft's relative motion trajectories in real time. The module employs a graded response mechanism, assigning risk thresholds based on threat levels. For low-risk scenarios, only trajectory monitoring is performed; for medium- and high-risk scenarios, an early warning is triggered and obstacle avoidance negotiation is initiated. An environmental perception compensation mechanism is also introduced, prioritizing LoRa communication data for risk assessment in the event of complex electromagnetic interference or sensor failure, ensuring system robustness.

[0157] Negotiation Module: A multi-priority dynamic negotiation protocol is designed to support autonomous decision-making in drone swarms in scenarios without a central node. When a collision risk is detected, the associated drones automatically generate obstacle avoidance proposals based on a preset set of rules (including mission urgency, remaining energy, and flight permissions), initiating multiple rounds of negotiation over the LoRa network. The protocol utilizes an asynchronous confirmation mechanism, allowing each node to dynamically adjust its response timing based on local computing resources. Ultimately, a globally optimal obstacle avoidance strategy is determined through majority consensus, avoiding path adjustment conflicts.

[0158] The Flight Trajectory Adjustment Module generates a multi-objective optimized trajectory based on negotiation results, dynamically adjusting the flight path, speed, and altitude using a collaborative obstacle avoidance algorithm. The module features a built-in trajectory smoothing mechanism to minimize energy loss caused by sudden changes in course while avoiding collisions. The module also synchronizes these adjusted state parameters to the cluster via the LoRa network in real time. Furthermore, it supports seamless integration with existing flight control systems and is compatible with multiple path planning algorithms through an open interface, ensuring plug-and-play compatibility with traditional drone platforms.

[0159] Heterogeneous device-compatible architecture: This system integrates existing vision sensors, radar, GPS / Beidou / RTK, IMU, and other sensors. Building on existing perception technologies, it enables multi-UAV information sharing and collaboration through LoRa wireless communication, enhancing obstacle avoidance capabilities. This system enables low-power, secure, and efficient collaborative operations, particularly in environments without public network support. A standardized communication protocol stack and modular hardware interface are developed to support rapid system integration for UAV platforms from different manufacturers. The protocol stack utilizes a lightweight design, shielding underlying hardware differences through abstraction layer technology, ensuring cross-platform compatibility in flight status data formats, communication frequency bands, and control commands.

[0160] In order to verify the feasibility and effectiveness of the present invention, a scenario in which two UAVs (high-priority UAV A and low-priority UAV B) are flying towards each other in the horizontal direction is simulated;

[0161] In the initial setup, Figure 3As shown in the figure, drone A flies eastward at 2 m / s from the origin, while drone B flies westward at 2 m / s from position (30, 5). Their original trajectories would result in a collision at position (15, 5). When the system detects a collision risk, it triggers a priority-based distributed negotiation mechanism. Drone B, with a lower priority, generates a detour, avoiding the obstacle through vertical offsets. The results show that the adjusted trajectory successfully avoids the collision risk, validating the effectiveness of the priority negotiation mechanism and trajectory planning algorithm.

[0162] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A UAV anti-collision method based on LoRa communication is characterized by: The method comprises the following steps: Step S1: Build an ad hoc network communication architecture to achieve flight status broadcast and synchronization; Step S2: Use the spatiotemporal alignment algorithm to fuse the multi-source data and construct a dynamic airspace situation map centered on the aircraft, updating the position, speed, and heading information of surrounding UAVs in real time; Step S3: Based on the kinematic prediction model and risk quantification algorithm, the time window and spatial overlap probability of the expected intersection point are calculated; Step S4: Design a multi-priority dynamic negotiation protocol to support autonomous decision-making of drone clusters in scenarios without a central node; Step S5: Generate a multi-objective optimized trajectory based on the negotiation results, and dynamically adjust the flight path, speed, and altitude through a collaborative obstacle avoidance algorithm; Step S6: The UAV performs a climb, yaw, or deceleration operation along the newly planned trajectory, while continuously broadcasting the adjusted flight status.

2. The UAV anti-collision method based on LoRa communication according to claim 1, characterized in that: The ad hoc network communication architecture includes LoRa wireless communication mechanism, hybrid communication mechanism and time synchronization mechanism. The LoRa wireless communication mechanism adopts spread spectrum modulation and adaptive power control mechanism, adaptively optimizes communication parameters according to channel quality and communication distance, monitors environmental interference intensity and relative motion state of drones in real time, and dynamically selects the optimal spreading factor and bandwidth combination. Specifically: Define the joint optimization function of spreading factor SF and bandwidth BW: ； in, is the received signal strength, is the signal-to-noise ratio, is the safety distance threshold; The hybrid communication mechanism uses periodic broadcast and event-driven intensive communication technology. The periodic broadcast intensive communication technology is used during normal cruising, broadcasting flight status at a low frequency; the event-driven intensive communication technology can automatically increase the broadcast frequency when collision risk occurs. Introduce the broadcast frequency control function, the expression is: ； The time synchronization mechanism uses the broadcast timestamp information to fuse with the flight control IMU clock to ensure the time consistency of data received by different drones.

3. The UAV anti-collision method based on LoRa communication according to claim 1, wherein: In step S2, a spatiotemporal alignment algorithm is used to fuse multi-source data and construct a dynamic airspace situation map centered on the aircraft, updating the position, speed, and heading information of surrounding drones in real time. Specifically, Step S2-1: Use the spatiotemporal alignment algorithm to fuse multi-source data, specifically: Time alignment of received data, that is, using the local receiving time Delayed reception Map the received data to the local unified time base. The expression is: ； in, Contains the sender timestamp for each data frame. ; For the same moment The data from different sensors are fused using Kalman filtering, which can be expressed as: ； in, is the predicted state, is the Kalman gain, is the measurement matrix; Step S2-2: Construct a dynamic airspace situation map centered on the aircraft.

4. The UAV anti-collision method based on LoRa communication according to claim 1, characterized in that: The step S2-2 constructs a dynamic airspace situation map centered on the aircraft, specifically: The fused and spatiotemporally aligned state data of each drone is converted into the local coordinate system of the drone to obtain the relative position vector and the corresponding relative speed , and calculate the relative distance, the calculation formula is: ； After summarizing the relative position information, relative speed and heading information of all neighboring UAVs, a two-dimensional or three-dimensional dynamic airspace situation map centered on the UAV is constructed. In the dynamic situation map, the information of each UAV node can be expressed in vector form, as follows: ； in, The heading angle information of the UAV is provided as input to the collision risk model, which is used to calculate the future intersection position and collision possibility.

5. The UAV anti-collision method based on LoRa communication according to claim 1, characterized in that: In step S3, the time window and spatial overlap probability of the expected intersection point are calculated based on the kinematic prediction model and the risk quantification algorithm, specifically: Step S3-1: Construct a kinematic prediction model based on the uniformly accelerated linear motion assumption to predict the future time window of each drone. The flight trajectory within is deduced, and the expression is: ； in, is the current position, is the current speed, is the estimated average acceleration, is the position at the predicted moment; Step S3-2: After identifying the potential intersection trajectory, calculate the collision risk index , specifically: ； in, is the minimum relative distance between UAVs within the prediction time window; To arrive from the current time The time required, i.e. the collision time margin; Step S3-3: triggering the hierarchical response and robustness assurance mechanism, specifically: Setting risk thresholds 、 Implement collision response classification: If , it is judged as low risk, and the system only performs trajectory recording and status monitoring; like , it is judged as medium risk, the system sends an obstacle avoidance warning signal and prepares to enter the obstacle avoidance negotiation process; like , determined to be high risk, the system immediately triggers the distributed obstacle avoidance mechanism, broadcasts a synchronous collaborative obstacle avoidance request to the conflicting UAVs through LoRa, and enters the trajectory adjustment phase.

6. The UAV anti-collision method based on LoRa communication according to claim 1, characterized in that: In step S4, a multi-priority dynamic negotiation protocol is designed. The multi-priority dynamic negotiation protocol stipulates a priority processing mechanism for multiple drones, specifically: Step S4-1: Generate an obstacle avoidance strategy based on a dynamic priority decision model with multi-dimensional weights, taking into account task urgency, remaining energy, and relative motion state; The priority weight function is expressed as: ； in, is the task priority coefficient, is the energy status factor, is the real-time collision risk value; Step S4-2: Adopt a two-stage asynchronous confirmation mechanism: After identifying a pair of drones at risk of collision, the initiator broadcasts a proposal frame containing alternative detour paths and sends candidate obstacle avoidance paths to potential conflicting nodes. The receiver returns confirmation feedback information based on its own risk assessment and priority results, including whether to accept the proposal or propose an alternative path. The negotiation process does not rely on synchronous control and adopts an asynchronous confirmation strategy, allowing each node to respond asynchronously based on its own computing resources. The response feedback is counted within a set time limit. When the majority of nodes participating in the negotiation reach a consensus, the consistent trajectory adjustment instruction is triggered, and the subsequent flight path update phase begins.

7. The UAV anti-collision method based on LoRa communication according to claim 1, characterized in that: In step S5, a multi-objective optimized trajectory is generated based on the negotiation results, and the flight path, speed and altitude are dynamically adjusted through a collaborative obstacle avoidance algorithm, specifically: Step S5-1: Define collision risk function: ； in, is the relative position vector between UAVs, is the estimated time of collision; Step S5-2: Generate a smooth obstacle avoidance path that complies with dynamic constraints, specifically: The obstacle avoidance trajectory is generated by minimizing the weighted objective function of path risk and adjusting energy consumption. The optimization objective function is defined as: ； in, To generate the residual collision risk function of the path during the obstacle avoidance process, The energy consumption caused by track deviation is related to factors such as flight distance and climb / descent amplitude; 、 Risk weight and energy consumption weight factors are used to adjust the system's balance preference between safety and energy efficiency; The trajectory is parameterized using a third-order Bezier curve to generate a smooth path that satisfies the dynamic constraints. The trajectory is expressed as: ； in, is the current drone position; The position of the target after obstacle avoidance or the original track return point; 、 The control point is determined by the heading angle, speed change rate and target offset to ensure that the trajectory is smooth and the steering angle is continuous; after the trajectory is generated, the system verifies the trajectory curvature in real time to ensure that the heading change rate does not exceed the maximum steering rate threshold allowed by the flight control system and broadcast the path parameters to the cooperative drone nodes through the LoRa network.

8. The UAV anti-collision system based on LoRa communication is characterized by: The UAV anti-collision method based on LoRa communication comprising any one of claims 1 to 6; The UAV anti-collision system includes a LoRa communication module, a flight status receiving module, a collision risk assessment module, a negotiation module, a flight trajectory adjustment module, and a heterogeneous device compatible architecture. The LoRa communication module is used to support wide-area broadcasting and reception of flight status information between UAVs; After receiving flight status data from other drones, the flight status receiving module verifies the integrity of the information through a multi-source data verification mechanism to eliminate errors caused by communication packet loss or interference; The collision risk assessment module calculates the intersection probability and collision time window of the relative motion trajectories of multiple aircraft in real time based on the kinematic prediction model and risk quantification algorithm; The negotiation module is used to design a multi-priority dynamic negotiation protocol to support autonomous decision-making of drone clusters in scenarios without a central node; The flight trajectory adjustment module generates a multi-objective optimized trajectory based on the negotiation results and dynamically adjusts the flight path, speed, and altitude through a collaborative obstacle avoidance algorithm. The flight trajectory adjustment module has a built-in trajectory smoothing mechanism to minimize energy loss caused by sudden changes in heading while avoiding collisions, and synchronizes the adjusted state parameters to the cluster via the LoRa network in real time. The heterogeneous device compatible architecture is used to fuse with sensors and perform information sharing and collaboration among multiple drones through LoRa wireless communication.

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