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133results about How to "Increase guide" patented technology

Automatic charging system and automatic charging method for robot

InactiveCN104635728ANot easy to loseAn edge-guided strategy is simple and easyPosition/course control in two dimensionsButt jointEngineering
The invention provides an automatic charging system for a robot. The automatic charging system for the robot comprises an infrared transmitting tube, infrared receiving tubes, a DSP (digital signal processor) controller, an edge guiding module, an angle compensating module and a charging module; and the infrared receiving tubes are arranged in the front of the robot and at the rear of the robot. According to the automatic charging system and an automatic charging method for the robot in an embodiment, the single infrared transmitting tube is used for transmitting infrared signals, the edge characteristics of an infrared transmitting range are skillfully used, and butt joint guidance on the robot can be implemented by an angle compensation strategy. Because edge angle characteristics of different infrared transmitting tubes are different, when the automatic charging system is used for the first time, a tangent angle of the transmitting angle at a transmitting point requires to be measured manually. Even if an approach angle has deviation due to external interferences, a charging head is within a range of a charging socket through the angle compensation strategy. Deviation of an inserting angle can be compensated through a mechanical structure of a charging station. In addition, the invention also provides an automatic charging method for the robot.
Owner:SHENYANG SIASUN ROBOT & AUTOMATION

Cascade reservoir optimal scheduling method based on self-adaptive improved particle swarm optimization algorithm

The invention discloses a cascade reservoir optimal scheduling method based on a self-adaptive improved particle swarm optimization algorithm, and belongs to the field of water conservancy and hydropower. The cascade reservoir optimal scheduling method comprises the steps: obtaining basic information of a cascade reservoir system; constructing a cascade reservoir optimal scheduling model taking the water level of each time period in the reservoir scheduling period as a decision variable and the maximum generating capacity as an objective function, and determining constraint conditions; initializing particle swarm parameters according to basic information and constraint conditions of the cascade reservoir system; calculating the relative progress of each particle, updating the inertia weight, cognitive learning factor and social learning factor of each particle according to the relative progress, and further updating the speed and position of each particle; and calculating the optimal adaptive value after the particle swarm updating is completed, and obtaining the power generation capacity, the month end water level and the reservoir outlet flow of the cascade reservoir system in each month. According to the cascade reservoir optimal scheduling method, the particle swarm parameters are adaptively adjusted by using the particle relative progress, and the defect that the traditional particle swarm algorithm is easy to fall into local optimum is overcome, and the power generation benefit of the cascade reservoir is improved.
Owner:HUAZHONG UNIV OF SCI & TECH +1
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