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π₯ Content Introduction
1. Basics of 3D Grid Map Modeling
- Data Collection: Acquire 3D point cloud data using depth cameras (such as Kinect), LiDAR, or RGB-D sensors.
- Preprocessing:
- Denoising: Remove outliers from the point cloud (e.g., using statistical filtering, setting a standard deviation threshold of 1.0-2.0).
- Registration: Stitch multiple frames of point clouds (using the ICP algorithm, with 20-50 iterations, error < 0.01m).
- Voxelization: Map point cloud data to a 3D grid, marking the state of each cell based on point cloud density (e.g., if the number of points > 10, classify as an obstacle).
- Example: After voxelization of a point cloud from an indoor scene, a grid map of 100Γ100Γ30 (d=0.1m) is generated, with approximately 15% of the cells classified as obstacles.
2. Core Path Planning Algorithm (3D Scene Adaptation)
β³οΈ Results
π References
[1] Jiang Yingjie, LΓΌ Xueqin, Duan Liwei. Path Planning for Substation Inspection Robots Using Grid Genetic Algorithms*[J]. Science and Technology Innovation, 2015(6):3. DOI:10.15913/j.cnki.kjycx.2015.06.012.
[2] Wang Yubin, Shen Zhenjun, Wang Yucheng, et al. Research on Path Planning of Tetrahedral Robots Based on A* Algorithm[J]. Mechanical Transmission, 2024, 48(2):42-47. DOI:10.16578/j.issn.1004.2539.2024.02.006.
[3] Jiang Yingjie, LΓΌ Xueqin, Duan Liwei. Path Planning for Substation Inspection Robots Using Grid Genetic Algorithms*[J]. Microcomputer Information, 2015(006):000.
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π Various intelligent optimization algorithm improvements and applications
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, 3D packing, logistics site selection, cargo location 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, visual field 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-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.
π Machine learning and deep learning for time series, regression, classification, clustering, and dimensionality reduction
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 function 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 system 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 forecasting, stock price prediction, PM2.5 concentration prediction, battery health status prediction, electricity consumption prediction, water body optical parameter inversion, NLOS signal identification, precise prediction of subway stops, transformer fault diagnosis.
π In image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing.
π In path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), 3D path planning for drones, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problems, electric vehicle routing problem (EVRP), two-layer vehicle routing problem (2E-VRP), hybrid vehicle routing problem, ship trajectory planning, full path planning, warehouse patrol.
π In drone applications
Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, online optimization of safe communication trajectories for drones, vehicle collaborative drone path planning.
π 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.
π In signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signals, EEG signals, signal timing optimization, ECG 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.
π In power systems
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration, orderly charging, MPPT optimization, household electricity.
π In cellular automata
Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion.
π In radar
Kalman filter tracking, trajectory association, trajectory fusion, SOC estimation, array optimization, NLOS identification.
π 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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