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🔥 Content Introduction
1. System Architecture
- Main Class
<span>DroneSimulation</span>: Encapsulates all simulation logic (vehicle modeling, drone dynamics, control, visualization), with a clear structure for easy expansion. - Modular Design: Each function is independent as a method (e.g.,
<span>updateVehicles</span>,<span>detectAruco</span>,<span>updateVisualization</span>), allowing for individual modification or replacement.
2. Core Functional Modules
(1) Modeling of Moving Vehicles (3 Vehicle Fleet)
- Path Type: Supports
<span>Circular Path</span>(default) and<span>Straight Return Path</span>, switchable via<span>obj.vehicles.path_type</span>. - Fleet Coordination: In a circular path, the front and rear vehicles maintain a fixed angular difference (0.2 rad) to ensure safe spacing; in a straight path, they automatically return upon reaching the boundary.
- ArUco Markers: Each vehicle is equipped with a unique ID (1/2/3) ArUco marker on its roof for drone detection and authentication.
(2) Drone Dynamics and Control
- VTOL Characteristics: Supports vertical takeoff and landing, and hovering, simplifying the six-degree-of-freedom model, with gravity compensation and boundary constraints added.
- Dual Control Modes:
- Automatic Mode: Tracks the target vehicle (based on ArUco ID), automatically descends to land after authentication.
- Manual Mode: WASD controls horizontal movement, Q/E controls elevation, suitable for manual position adjustments.
- PID Position Control: Parameters are adjustable (
<span>Kp_pos</span>/<span>Ki_pos</span>/<span>Kd_pos</span>), ensuring smooth and stable tracking.
⛳️ Operation Results
📣 Sample Code
🔗 References
🎈 Some theoretical references are from online literature; please contact the author for removal if there is any infringement.
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🌟 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, 3D packing, logistics site selection, cargo position optimization, bus scheduling optimization, charging station layout optimization, workshop layout optimization, container ship loading optimization, pump combination optimization, medical resource allocation optimization, facility layout optimization, visible base station and drone site selection optimization, knapsack problem, wind farm layout, time slot allocation optimization, optimal distributed generation unit allocation, multi-stage pipeline maintenance, factory-center-demand point three-level site selection problem, emergency supply distribution center site selection, base station site selection, road lamp post arrangement, hub node deployment, transmission line typhoon monitoring devices, container scheduling, unit optimization, investment portfolio optimization, cloud server combination optimization, antenna linear array distribution optimization, CVRP problem, VRPPD problem, multi-center VRP problem, multi-layer network VRP problem, multi-center multi-vehicle VRP problem, dynamic VRP problem, two-layer vehicle routing problem (2E-VRP), electric vehicle routing problem (EVRP), hybrid vehicle routing problem, mixed flow shop problem, order splitting scheduling problem, bus scheduling optimization problem, flight shuttle vehicle scheduling problem, site selection path planning problem, port scheduling, port bridge scheduling, parking space allocation, airport flight scheduling, leak source localization, cold chain, time windows, multi-parking lots, etc., site selection optimization, port bridge scheduling optimization, traffic impedance, redistribution, parking space allocation, airport flight scheduling, communication upload and download allocation optimization
🌟 Time series, regression, classification, clustering, and dimensionality reduction in machine learning and deep learning
2.1 BP time series, regression prediction, and classification
2.2 ENS voice neural network time series, regression prediction, and classification
2.3 SVM/CNN-SVM/LSSVM/RVM support vector machine series time series, regression prediction, and classification
2.4 CNN|TCN|GCN convolutional neural network series time series, regression prediction, and classification
2.5 ELM/KELM/RELM/DELM extreme learning machine series time series, regression prediction, and classification
2.6 GRU/Bi-GRU/CNN-GRU/CNN-BiGRU gated neural network time series, regression prediction, and classification
2.7 Elman recurrent neural network time series, regression prediction, and classification
2.8 LSTM/BiLSTM/CNN-LSTM/CNN-BiLSTM long short-term memory neural network series time series, regression prediction, and classification
2.9 RBF radial basis neural network time series, regression prediction, and classification
2.10 DBN deep belief network time series, regression prediction, and classification
2.11 FNN fuzzy neural network time series, regression prediction
2.12 RF random forest time series, regression prediction, and classification
2.13 BLS broad learning time series, regression prediction, and classification
2.14 PNN pulse neural network classification
2.15 Fuzzy wavelet neural network prediction and classification
2.16 Time series, regression prediction, and classification
2.17 Time series, regression prediction, and classification
2.18 XGBOOST ensemble learning time series, regression prediction, and classification
2.19 Transform various combinations of time series, regression prediction, and classification
Directions cover wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, electricity consumption prediction, water body optical parameter inversion, NLOS signal recognition, subway parking precision prediction, transformer fault diagnosis
🌟 In image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing
🌟 In path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), three-dimensional path planning for drones, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problem, electric vehicle routing problem (EVRP), two-layer vehicle routing problem (2E-VRP), hybrid vehicle routing problem, ship trajectory planning, full path planning, warehouse patrol, bus time scheduling, reservoir scheduling optimization, multimodal optimization
🌟 In drone applications
Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, drone secure communication trajectory online optimization, vehicle collaborative drone path planning,
🌟 In communication
Sensor deployment optimization, communication protocol optimization, routing optimization, target localization optimization, Dv-Hop localization optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI localization optimization, underwater communication, communication upload and download allocation
🌟 In signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, electromyography signals, electroencephalography signals, signal timing optimization, electrocardiogram signals, DOA estimation, encoding and decoding, variational mode decomposition, pipeline leakage, filters, digital signal processing + transmission + analysis + denoising, digital signal modulation, bit error rate, signal estimation, DTMF, signal detection
🌟 In power systems
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration, orderly charging, MPPT optimization, household electricity, electric/cold/heat load forecasting, power equipment fault diagnosis, battery management system (BMS) SOC/SOH estimation (particle filter/Kalman filter), multi-objective optimization in power system scheduling, photovoltaic MPPT control algorithm improvement (perturbation observation method/incremental conductance method), electric vehicle charging and discharging optimization, microgrid day-ahead optimization, energy storage optimization, household electricity optimization, supply chain optimization
🌟 In cellular automata
Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion
🌟 In radar
Kalman filter tracking, track association, track fusion, SOC estimation, array optimization, NLOS recognition
🌟 In workshop scheduling
Zero-wait flow shop scheduling problem NWFSP, Permutation flow shop scheduling problem PFSP, Hybrid flow shop scheduling problem HFSP, zero idle flow shop scheduling problem NIFSP, distributed permutation flow shop scheduling problem DPFSP, blocking flow shop scheduling problem BFSP
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