Why Choose CASUN AI Algorithms For AGV China
CASUN AI Algorithms For Visual Navigation And Positioning
Visual Navigation And Positioning
Advantages: Casun's visual positioning algorithm for autonomous industrial robots combines more than 15 algorithms, and each step of multi-vision processing is considered. At the beginning of the algorithm design, it begins to consider the accuracy, cost, and subsequent optimization space of the algorithm. This algorithm for automated guided vehicle China has been used in a variety of scenarios, including local positioning, object recognition, location recognition, qr code navigation, 3D security and other fields. It supports continuous 24-hour per day indoor operation and has strong adaptability to different external scenes.
CASUN AI Algorithms For SLAM Positioning Algorithm
SLAM Positioning Algorithm
1) Strong environmental adaptability, 60% adaptation rate to environmental change, learn and build maps independently, support continuous optimization of maps during operation.
2) Superior in large-space built-in map positioning, combined with pose algorithm, the positioning algorithm has low resource overhead and good robustness.
3) Support multiple algorithm compatibility, high positioning accuracy which can reach plus or minus 5MM.
CASUN AI Algorithms For Dispatching Algorithm
Dispatching Algorithm
Advantage:1. High processing efficiency which can dispatch 500+ vehicles.
2. Support mixed dispatching of multiple types of vehicles.
3. Support cross-floor dispatching.
4. Support joint dispatching with other brand cars.
5. Based on big data analysis and AI modelling at the customer site, we will continue to learn and optimize the dispatching strategy so as to obtain the optimal strategy.
CASUN AI Algorithms For Path Planning Algorithm
Path Planning Algorithm
Advantage:1. The route can be dynamically adjusted according to the real-time congestion situation to improve the operating efficiency of the vehicle.
2. Dynamically adjust the weight of the path in real-time to completely avoid collision problems.
3. Adopt the algorithm of deep reinforcement learning combined with dynamic programming, and continuously optimize the algorithm strategy during the operation process, so as to get high execution efficiency and quick response.
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