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🔥 Content Introduction
1. System Architecture
- Sensor Module: Includes LiDAR, visual sensors (cameras), and Inertial Measurement Unit (IMU). LiDAR is used to detect the distance and location of surrounding obstacles, providing data support for the robot’s path planning and obstacle avoidance; the camera can identify the status of LCD products (such as whether they are damaged, whether their position is correct, etc.) and potential risks in the transport environment; the IMU measures the robot’s posture and motion parameters, such as acceleration and angular velocity, helping the robot maintain stable operation.
- Control Module: Receives data from the sensors and processes it according to preset algorithms and rules, such as path planning, speed control, and posture adjustment. At the same time, the control module is also responsible for interacting with the early warning module, adjusting the robot’s operating strategy based on early warning information.
- Early Warning Module: Based on sensor data and decisions from the control module, it determines whether the robot faces potential risks during the transport of LCD products and issues corresponding early warning signals. The early warning module needs to consider various factors, such as obstacle distance, robot speed, and the stability of the LCD products.
- Communication Module: Enables communication between the robot and external systems (such as monitoring centers and other robots). On one hand, the robot can send its status information (including early warning information) to the monitoring center, allowing operators to understand the situation in a timely manner; on the other hand, robots can share information with each other, such as passing on obstacle information and path planning information during collaborative transport of LCD products.
2. Early Warning Functions
- Collision Warning: When LiDAR detects that an obstacle is within a set threshold distance, the early warning module assesses the risk of collision based on the robot’s speed and direction. If there is a possibility of collision, the control module adjusts the robot’s movement strategy, such as slowing down or changing the path, and issues a warning signal to the operator, such as an audible alarm or flashing lights.
- Posture Abnormality Warning: The IMU continuously monitors the robot’s posture information. When the robot’s posture changes beyond the normal range (such as excessive tilt angle or abnormal acceleration), the early warning module issues a posture abnormality warning. This helps prevent damage to LCD products during transport due to unstable robot posture.
- LCD Product Status Warning: The visual sensor monitors the LCD products in real-time. When it detects displacement or damage to the LCD products, the early warning module promptly issues a warning signal. For example, during transport, the LCD products may shift or be damaged due to vibrations or collisions, and the visual sensor can capture these changes and report them to the early warning module.
- Path Planning Irregularity Warning: While performing path planning, the control module evaluates the rationality of the path based on environmental information and the robot’s status. If there are potential risks along the path (such as narrow passages or high-risk areas), the early warning module issues a warning about path planning irregularities, prompting the control module to replan the path.
3. Technical Implementation
- Data Processing and Analysis: The data collected by the sensors needs to be preprocessed, such as filtering and fusion, to improve accuracy and reliability. For example, LiDAR data may be affected by noise, and filtering algorithms can reduce this noise; the fusion of LiDAR and camera data can provide more comprehensive environmental information.
- Early Warning Algorithms: The algorithms used by the early warning module need to quickly and accurately assess potential risks. Common algorithms include rule-based algorithms and machine learning algorithms. Rule-based algorithms make early warning judgments based on preset rules, such as obstacle distance rules and posture change rules; machine learning algorithms can improve the accuracy and adaptability of warnings by learning from large amounts of historical data, such as using neural networks to classify and predict the status of LCD products.
- Human-Machine Interaction Interface: Operators receive early warning information and remotely control the robot through a human-machine interaction interface. This interface needs to be intuitive and user-friendly, capable of displaying the robot’s status and early warning information in real-time, allowing operators to adjust the robot’s operating parameters or intervene in path planning based on the warning information.
4. Application Cases and Development Trends
- Application Cases: In LCD production factories, transport robots are responsible for transporting LCD products from the production line to warehouses or other processing stages. The early warning system effectively prevents collisions between robots and other equipment or personnel during transport, while ensuring the safe transport of LCD products. For example, when a robot approaches a warehouse shelf, the early warning system can adjust the robot’s speed and path in a timely manner based on the shelf’s position and the robot’s motion state to prevent collisions with the shelf or damage to the LCD products.
- Development Trends: With the development of technologies such as the Internet of Things and artificial intelligence, the LCD transport robot early warning system will become more intelligent and automated. For example, through IoT technology, collaborative early warning and control among multiple robots can be achieved, improving transport efficiency and safety; using artificial intelligence algorithms for more accurate perception and prediction in complex environments will further enhance the performance of the early warning system.
⛳️ Operation Results
🔗 References
📣 Partial Code
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Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo position optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, container ship loading optimization, pump combination optimization, medical resource allocation optimization, facility layout optimization, visual domain 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 planning (2E-VRP), electric vehicle routing planning (EVRP), hybrid vehicle routing planning, 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
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2.1 BP time series, regression prediction, and classification
2.2 ENS voice neural network time series, regression prediction, and classification
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2.4 CNN|TCN|GCN convolutional neural network series time series, regression prediction, and classification
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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
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2.14 PNN pulse neural network classification
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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
🌈 Image Processing Aspects
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing
🌈 Path Planning Aspects
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), drone three-dimensional path planning, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problem, electric vehicle routing planning (EVRP), two-layer vehicle routing planning (2E-VRP), hybrid vehicle routing planning, ship trajectory planning, full path planning, warehouse patrol
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Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, drone secure communication trajectory online optimization, vehicle collaborative drone path planning
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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
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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
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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 (perturb and observe method/incremental conductance method)
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Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion
🌈 Radar Aspects
Kalman filter tracking, track association, track fusion, SOC estimation, array optimization, NLOS identification
🌈 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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