AMICRO SEMICONDUCTOR CO., LTD.

The present applicant discloses an image processing method based on a convolution algorithm and a chip using the same. The image processing method comprises: step A, using a fast Fourier transform and a fast Fourier inverse transform to control an image template and an image to be processed to obtain convolution result elements in a space domain in a form of multiplication in a frequency domain; and step B, in which if there is a convolution result element that satisfies a preset gray value condition, then a gray value of a pixel point corresponding to the convolution result element in the image to be processed is set as a preset target gray value. The complexity of the calculation time is reduced, the volume of data for processing pixel points is also decreased, and the real-time performance of image processing is ensured.

A positioning method for a ceiling vision robot are provided. The method includes: during a moving process, a ceiling vision robot acquiring a ceiling image in real time; acquiring existing road sign distribution information according to the ceiling image; determining whether to establish a new road sign, according to the existing road sign distribution information, if so, establishing a new road sign at a acquisition position of the ceiling image, if not, not establishing the new road sign at the acquisition position of the ceiling image; and monitoring the positioning effectiveness of an existing road sign according to the existing road sign distribution information, if the positioning of the existing road sign is effective, performing positioning by using pose information corresponding to the existing road sign, if the positioning of the existing road sign is ineffective, not performing positioning by using the pose information corresponding to the existing road sign.

The present applicant discloses a robot suspension judgment method based on laser points, a map update method, and a chip. The method comprises: laser beams emitted by the laser sensor are controlled to scan an area to be detected, so as to obtain a laser beam simulation line segment; at the same time, a pre-built grid map is obtained; according to the number of obstacle grids passed by the laser beam simulation line segment within the allowable range of the ranging error, a laser beam simulation line segment with judgment function is obtained, and the laser beam simulation line segment with judgment function is counted; and according to a ratio of the number of laser beam simulation line segments with judgment function to the number of all laser beam simulation line segments, it is judged whether the mobile robot is in a suspended state.

A phase-locked loop working system includes: a pre-frequency divider, a phase frequency detector, a charge pump, a filter, a voltage-controlled oscillator, a loop frequency divider and a circuit parameter regulator. The phase frequency detector, the charge pump, the filter, the voltage-controlled oscillator and the loop frequency divider are sequentially coupled end to end to form a phase-locked loop circuit. The circuit parameter regulator is coupled to the phase-locked loop circuit and is used to adjust a filtering resistor and a filter capacitor in the filter, to control loop parameters generated by the phase-locked loop circuit to be within a preset margin range. The circuit parameter regulator is coupled to the phase-locked loop circuit and is further used to adjust a frequency-division coefficient of the pre-frequency divider and/or a frequency of the signal to be processed inputted into the pre-frequency divider.

A robot positioning method includes: collecting, by a robot, data of laser points by using a laser sensor; obtaining multiple layers of grid maps layer by layer in order from low resolution to high resolution; traversing candidate solutions in a current layer of grid map; controlling a plurality of occupancy probability values obtained correspondingly for the data of laser points at a currently traversed candidate solution to be sequentially summed when the robot determines that the currently traversed candidate solution is a feasible solution; setting, based on a ratio of a resolution of a next layer of grid map to a resolution of the current layer of grid map, the determined feasible solution as a candidate solution of the next layer of grid map; recursively determining feasible solutions in candidate solutions of the layers of grid map in the order from low resolution to high resolution.

Disclosed is a visual tracking method of robot, wherein an execution body of the visual tracking method of the robot is a robot fixedly equipped with a camera and an inertial sensor; the visual tracking method of the robot includes: the robot performing image tracking using a window-based matching mode, and when the robot succeeds in performing tracking using the window-based matching mode, the robot stopping performing image tracking using the window-based matching mode, and then the robot performing image tracking using a projection-based matching model; and then, when the robot fails to perform tracking using the projection-based matching mode, the robot stopping performing image tracking using the projection-based matching mode, and then the robot performing image tracking using the window-based matching mode.

Disclosed are a cleaning path planning method based on a pathfinding cost, a chip, and a cleaning robot. The method includes step 1, configuring cleaning lines in a map matching a cleaning area according to map boundaries and obstacle boundaries in different directions in the cleaning area of a mobile robot; step 2, setting a candidate entrance/exit satisfying a preset pathfinding cost condition and found in a current non-cleaned sub-block by the mobile robot at a current position as a cleaning entrance position of the current non-cleaned sub-block; step 3, controlling the mobile robot to move to the cleaning entrance position of the current non-cleaned sub-block; step 4, searching for and planning a cleaning entrance position and a cleaning termination position of a next non-cleaned sub-block by repeating steps 2-3 described above in a case of the mobile robot moves to the cleaning termination position of the current non-cleaned sub-block.

The present applicant discloses a robot suspension judgment method based on laser points, a map update method, and a chip. The method comprises: laser beams emitted by the laser sensor are controlled to scan an area to be detected, so as to obtain a laser beam simulation line segment; at the same time, a pre-built grid map is obtained; according to the number of obstacle grids passed by the laser beam simulation line segment within the allowable range of the ranging error, a laser beam simulation line segment with judgment function is obtained, and the laser beam simulation line segment with judgment function is counted; and according to a ratio of the number of laser beam simulation line segments with judgment function to the number of all laser beam simulation line segments, it is judged whether the mobile robot is in a suspended state.

A map region contour-based setting method and a method for controlling robot edgewise walking termination. The method includes: a robot setting a plurality of unit regions to be subjected to edgewise walking by executing a setting method; then, the robot performing edgewise walking in a working region; when the robot walks through all unit regions to be subjected to edgewise walking, the robot stopping walking along the edge; and when the robot repeatedly walks through the same batch of unit regions to be subjected to edgewise walking, the robot stopping walking along the edge, wherein the same batch of unit regions to be subjected to edgewise walking are a preset number of unit regions to be subjected to edgewise walking that are different from each other, and the number of unit regions to be subjected to edgewise walking set by using the setting method is greater than the preset number.

Disclosed are a carpet detection method for a robot, an obstacle avoidance method, a robot, and a chip. The carpet detection method includes: Step S 1 , in a case that the robot has detected an obstacle with an uneven contour, performing plane scanning based on data from a line laser sensor; and Step S 2 , in a case that an unevenness degree of the contour is within a preset range, determining that the obstacle is a carpet based on data obtained after the plane scanning.