A multi-session concurrent AI-based unmanned aerial vehicle cluster real-time formation and avoidance control system

Abstract

The application discloses a kind of unmanned aerial vehicle cluster real-time formation and evasion control system based on multi-session concurrent AI, by cloud mother AI, unmanned aerial vehicle intelligent client, sensing module, communication module and alarm module composition.Mother AI adopts high-concurrent multi-session parallel mechanism, and respectively establishes independent, two-way, low-delay communication link with multiple unmanned aerial vehicles, and constructs strong central top command system.The system innovatively adopts continuous spectral proportional interval control logic, replaces traditional 0~1 discrete control mode, and can generate smooth, continuous, controllable formation trajectory in real time without pre-programming condition.Unmanned aerial vehicle real-time back position, attitude, sensing data and environmental information, mother AI overall planning completes fault unmanned aerial vehicle identification, elimination, adjacent machine intelligent replacement, and quickly commands cluster dispersion evasion and reorganization in the event of sudden risk.The system can dispatch multiple machines to work together, adapt to agricultural plant protection, power inspection, emergency search and rescue, wedding ceremony, film multi-camera shooting and other multi-element civilian scenes, effectively solve the industry pain points such as existing cluster scheduling rigidity, poor cooperativity, uncontrollable pure decentralized architecture and inability to land, and have complete engineering landing nature and practical value.

Description

Technical Field

[0001] This invention relates to the field of intelligent drone swarm control technology, specifically to a drone swarm real-time formation, safety avoidance, and multi-scenario collaborative control system based on multi-session concurrent AI and continuous spectrum proportional control technology, which realizes strong central unified scheduling and highly adaptive lower-level nodes. Background Technology

[0002] Drone technology has been widely used in the civilian sector, but there are still significant technical bottlenecks in multi-drone swarm collaboration: First, many existing solutions blindly rely on the concept of pure decentralization and lack unified top-level scheduling and rule constraints.

[0003] Practice has shown that clusters without a unified overall control will inevitably suffer from problems such as a lack of unified goals, a lack of unified security boundaries, and disordered and conflicting actions, making it impossible to achieve effective collaboration in precise civilian scenarios.

[0004] Secondly, traditional clusters are mostly based on pre-programmed fixed trajectories for execution, with rigid control logic. They cannot be dynamically adjusted when the environment changes or there are sudden failures, which can easily lead to task interruption, formation instability, or even overall paralysis.

[0005] Third, the cluster lacks anti-interference capabilities, and single-machine failures can easily trigger a chain reaction. It also lacks automatic removal, intelligent replacement, and flexible scheduling mechanisms, making it impossible to guarantee operational continuity.

[0006] Fourth, in complex scenarios such as emergency search and rescue, wedding photography, and film and television location shooting, it is impossible to achieve multi-machine division of labor and multi-task parallelism. Manual operation is inefficient and cannot meet the diverse and highly flexible civilian operation needs.

[0007] Real-world engineering projects follow the same principles as human organizations: the larger the number and the more complex the scale, the more necessary it is to rely on unified control to maintain order; and the more powerful an entity is, the more it needs top-level rules to constrain it, otherwise it will inevitably lead to disorder, congestion, or even paralysis.

[0008] One of the core flaws of existing technologies is the confusion between the concepts of distributed intelligence and decentralized command, which leads to the lack of necessary central control in the system and ultimately makes it impossible to implement. Summary of the Invention

[0009] This system comprises a cloud-based master AI, a drone intelligent client, a global perception module, a real-time communication module, and a risk alarm module. The overall architecture follows a human-centric organizational and control tower logic, combining strong central control with highly adaptive individual units, achieving a balance between order and efficiency. The master AI is equipped with a high-concurrency, multi-session interaction mechanism, allocating an independent communication channel to each drone for precise, low-latency one-to-one scheduling. This architecture explicitly rejects the purely decentralized concept, insisting on addressing the fundamental engineering problem of disorder without a central authority, ensuring clear overall cluster objectives, controllable actions, and manageable risks.

[0010] The mother AI innovatively adopts continuous spectrum proportional range control logic to replace the traditional 0~1 discrete control method, realizing continuous and smooth control of flight attitude, speed and trajectory, making the swarm action smoother, more stable and more predictable.

[0011] The AI ​​can calculate the formation points, paths and division of labor in real time according to the task instructions, without the need to write programs in advance. It supports real-time dynamic adjustment of tasks to meet the needs of diverse civilian scenarios.

[0012] The drones transmit status data in real time, and the master AI quickly identifies anomalies and eliminates faults. At the same time, it drives neighboring drones to intelligently fill in the gaps, ensuring the continuity and robustness of the cluster operation.

[0013] When encountering risks such as obstacles, severe weather, or communication interference, the parent AI, acting as the unified command core, quickly issues avoidance strategies, allowing the drones to disperse and avoid danger. Once the risk is eliminated, the drones are precisely regrouped and restored to their formation. In the event of a communication interruption, the drones execute preset emergency strategies, automatically return to control after reconnection, and are reintegrated into the parent AI's unified scheduling system.

[0014] The system can flexibly schedule multiple drones to perform panoramic, close-up, and multi-camera simultaneous recording for film and television shooting; panoramic shooting, close-up of newlyweds, petal scattering, and atmosphere creation for wedding ceremonies; precision spraying and variable operation for agricultural plant protection; high-precision defect identification for power line inspection; emergency search and rescue life positioning; and temporary communication relay, fully demonstrating the high practicality of multi-tasking parallel operation and multi-drone mixed operation.

[0015] The core innovation of this invention lies in confirming and reinforcing that powerful central control and highly developed individual adaptation are complementary, not contradictory. The more intelligent the individual, the greater the need for order constraints; the larger the number, the greater the need for unified overall control to avoid congestion and disorder. The system adopts a two-layer tower-control architecture. The upper-layer master AI is responsible for global strategy, rules, tasks, and security, while the lower-layer individual UAVs are responsible for local perception, obstacle avoidance, attitude correction, and adaptive execution. This truly realizes an engineered mapping of human organizational systems, ensuring system stability, controllability, and large-scale scalability. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to the embodiments.

[0017] The drones are connected to the cloud-based master AI to complete identity authentication, positioning calibration, status detection, and initial data upload. The master AI assigns an independent communication channel to each drone and establishes a strong central scheduling connection.

[0018] The AI ​​receives job requirements and plans flight paths and task assignments in real time based on scenarios such as filming locations, wedding performances, and power line inspections. It generates continuous, smooth, and adjustable trajectory instructions based on continuous spectrum ratio range control logic.

[0019] The drones execute tasks such as flying, shooting, dropping, and spreading according to instructions, and transmit data back in real time. The main AI dynamically fine-tunes parameters, and the lower-level individual drones autonomously complete distributed operations such as local obstacle avoidance and attitude adjustment.

[0020] In film and television scenes, multiple cameras simultaneously execute panoramic, close-up, and tracking shots, resulting in richer stereoscopic image quality. In wedding scenes, multiple cameras cover panoramic, close-up, ambient lighting, and petal scattering. In agricultural scenes, multiple cameras spray according to the prescription map. In power scenes, multiple cameras collaborate to identify line defects. In emergency scenes, multiple cameras build temporary communication networks and perform life thermal imaging searches.

[0021] When the parent AI detects an anomaly or risk, it commands the cluster to avoid it and neighboring machines to fill in the gaps. When communication is interrupted, individual machines execute emergency strategies and automatically return to control after reconnection to ensure continuous system operation.

Claims

1. A real-time formation and avoidance control system for drone swarms based on multi-session concurrent AI, characterized in that, It includes a cloud-based AI, a drone intelligent client, a global perception module, a real-time communication module, and a risk alarm module. The AI ​​establishes an independent two-way communication channel with each drone through a multi-session parallel mechanism, building a strong central top-level command system to achieve unified concurrent scheduling and order control, thus eliminating the uncontrollable defects of a purely decentralized architecture.

2. The system according to claim 1, characterized in that, By adopting continuous spectrum proportional range control logic to replace the traditional 0~1 discrete control method, smooth control and continuous predictability of the flight attitude and trajectory of UAV swarms can be achieved.

3. The system according to claim 1, characterized in that, The mother AI can generate formation points and flight paths in real time according to mission instructions, without the need for pre-written programs, and supports real-time dynamic adjustment of missions and rapid switching between multiple scenarios.

4. The system according to claim 1, characterized in that, The drones transmit status and operation data in real time, and the master AI completes the identification and removal of faulty drones and the automatic replacement of neighboring drones, ensuring the continuity and stability of the cluster operation.

5. The system according to claim 1, characterized in that, The system adheres to unified master machine scheduling, unified task rules, and unified security boundaries, and supports multiple machines to perform tasks such as shooting, material delivery, and atmosphere creation. It is suitable for collaborative operations in multiple scenarios such as wedding ceremonies, film and television shooting, emergency search and rescue, agricultural plant protection, and power line inspection.

6. The system according to claim 1, characterized in that, When the system detects a security risk, the parent AI acts as the unified command core to direct the cluster to avoid reorganization. When communication is interrupted, the drones execute preset emergency strategies and automatically return to control after communication is restored. It also supports mixed collaborative operations of heterogeneous drones.

7. The system according to any one of claims 1 to 6, characterized in that, The underlying layer supports distributed execution of adaptive operations such as local obstacle avoidance and attitude correction by drone nodes, while the upper layer adheres to unified scheduling by the mother AI, forming a practical architecture of central command plus distributed execution. This architecture maps the operating rules of human organizations and the flight control logic of the control tower, fundamentally solving the industry pain point that pure decentralized architecture cannot be applied.