Prompt Description
This project designs a full-stack system architecture encompassing mapping and localization, path planning, and control modules. Built on Ubuntu 22.04 and the ROS2 Humble environment, it utilizes MAVROS to establish an efficient communication link between the upper-level planning algorithms and the lower-level APM flight controller. This achieves low-latency transmission of command instructions and state feedback, ensuring decoupling and collaborative operation between functional modules. For mapping and localization, current mainstream SLAM algorithms are deployed, with a focus on LiDAR-inertial odometry (such as the FAST-LIO series), visual-inertial odometry (such as the VINS-Fusion series), and feature-point-based visual SLAM (such as the ORB-SLAM3 series). The front-end implements search-based A* and JPS algorithms, as well as the sampling-based RRT* algorithm. The back-end uses B-spline interpolation and minimum control effort (Minimum Snap) numerical optimization methods to generate smooth, flyable trajectories that satisfy the drone's dynamic constraints. Control is achieved using methods such as PID, LQR, and MPC.
![Method 2: Dynamic Coefficient Learning Based on Attention Mechanism
- **Core Idea**: Introduce an attention layer to allow the model to automatically learn the weights of different tokens in emotion regulation. Implementation steps:
(1) Construct Token Feature Representation: Extract the Token state H=[h1, h2, ....hn] (where n is the number of Tokens) from a certain layer of the model, and use the function vector (A - B) as the 'emotion query vector'.
(2) Calculate Token-Emotion Attention Weights: Calculate the matching degree between each Token and the emotion query vector through scaled dot-product attention:
ai = Softmax (huaye)
where d is the dimension of hi.
(3) Adaptive Injection of Function Vectors: Apply hi+ai.(A-B) to each Token state. This is the idea for my part.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2F21yePdfXfrXMwKl3x4fhPD0TH1d7yghe%2F4a7680a8-334b-44a9-bf77-6adf2b1dd5e7%2Fcd765bfe-d81d-4f08-b6e5-510889b6cb6c.png&w=3840&q=75)
![APPROVED
This section focuses on experimental verification and model evolution. The core aspects include: 1. Evolutionary Strategy Fusion: Integrating the Analytic Hierarchy Process (AHP) with adversarial distillation heterogeneous techniques to achieve self-evolution of the anti-interference model. 2. Model Library Generation: Applying graph-driven pruning and multi-metric channel compression to generate various intelligent anti-interference decision model variants. 3. Layered Testing: Conducting dual verification of basic performance and adversarial robustness on embedded devices and GPU server platforms.
The final content includes the following: 5. [Bottom Illustration/Formula]: Depicting a **"model evolution funnel"**: Original large model $\xrightarrow{Pruning/Distillation}$ Evolved Models V1/V2/V3 $\xrightarrow{Testing}$ Optimal Strategy.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2Fd4HJgWhDq5YPdBs9p3hto7zsqac9CQfA%2Ffe88eb94-ac9e-440f-8ba6-70151a779b37%2F53b4a50e-b721-4fe0-8cbb-60ab4bb29cd7.png&w=3840&q=75)