Multi-degree-of-freedom robot control method and system

Abstract

The invention discloses a multi-degree-of-freedom robot control method and system, and relates to the field of robot control. Determining the direction of an obstacle and the distance between the obstacle in the direction of the obstacle and the multi-degree-of-freedom robot; drawing a three-dimensional obstacle map based on the direction and the distance of the obstacle; judging whether the multi-degree-of-freedom robot can cross an obstacle or not; the controller dispatches operation parameters of all parts of the multi-degree-of-freedom robot to complete obstacle crossing; the size and the shape of an object needing to be grabbed are determined; based on the size and shape of a grabbed object, the processor determines the grabbing point position and force of the multi-degree-of-freedom robot. The multi-degree-of-freedom robot has the advantages that by arranging the obstacle model building module, the obstacle crossing judgment module, the route bypassing module and the object grabbing module, accurate control over obstacle crossing and object picking of the multi-degree-of-freedom robot is achieved, and grabbing errors or crossing errors caused by irregular calling of some parts are avoided.

Description

Technical Field

[0001] The present invention relates to the field of robot control, and specifically relates to a multi-degree-of-freedom robot control method and system. Background Art

[0002] A multi-degree-of-freedom robot refers to a robot with multiple independent degrees of freedom of motion. These degrees of freedom usually refer to the number of axes or joints that the robot can independently move in three-dimensional space. The degree of freedom is the core index to measure the motion flexibility and function range of the robot, and directly determines the complexity and accuracy of the tasks that the robot can perform.

[0003] Due to the large number of axes and joints of the multi-degree-of-freedom robot, problems such as incoordination of related joints are likely to occur when controlling the multi-degree-of-freedom robot, which will cause difficulties in completing operations. Although the multi-degree-of-freedom robot has more refined and flexible operations, it has higher requirements for the control of the multi-degree-of-freedom robot, and a series of operation criteria need to be set. Summary of the Invention

[0004] To solve the above technical problems, a multi-degree-of-freedom robot control method and system are proposed, and the present technical solution solves the problems proposed in the above background art.

[0005] To achieve the above purposes, the technical solution adopted by the present invention is as follows: A multi-degree-of-freedom robot control method, comprising: Obtaining the picture information of a vision sensor, and a processor analyzes the picture information to obtain a picture processing result; Based on the picture processing result, determining the direction of the obstacle, and using an ultrasonic sensor to determine the distance between the obstacle in the direction of the obstacle and the multi-degree-of-freedom robot; Based on the direction and distance of the obstacle, drawing a three-dimensional obstacle map; Based on the height of the obstacle and various parameters of the robot itself, judging whether the multi-degree-of-freedom robot can cross the obstacle; If it can, the robot crosses the obstacle; Based on the height of the obstacle, a controller schedules the operating parameters of each component of the multi-degree-of-freedom robot to complete crossing the obstacle; If not, the robot plans a detour route; If there is a grasping task, based on the judgment of the picture information of the vision sensor and the distance of the ultrasonic sensor, determining the size and shape of the object to be grasped; Based on the size and shape of the grasped object, the processor determines the grasping points and forces of the multi-degree-of-freedom robot.

[0006] Preferably, the obtaining the picture information of a vision sensor, and a processor analyzes the picture information to obtain a picture processing result includes: A multi-degree-of-freedom robot rotates its head-mounted vision sensor to obtain images from different perspectives; The horizontal line in the middle of the image is denoted as the horizon line; Find at least two pairs of parallel lines in the real world within the image. Extend the at least two pairs of two-dimensional parallel lines in the image, and the intersection point obtained is denoted as the vanishing point, and the parallel lines are denoted as vanishing lines; If the distance of the first object on the same vanishing line from the vanishing point is greater than the distance of the second object from the vanishing point, then the first object is behind the second object; Obtain the height difference between the object and the horizon line. If the height difference between the object and the horizon line is negative, it means that the height of the object is lower than that of the object with a positive height difference; Store the positional relationship and height information of the objects in a memory.

[0007] Preferably, select the object with the shortest straight-line distance from the vision sensor as the reference object; Measure the distances from the vision sensor to the bottom and top of the reference object, and use the Pythagorean theorem to obtain the height of the reference object; Based on the relative height ratio between at least one object in the image and the reference object, multiply the relative height ratio by the height of the reference object to obtain the height of the object. The relative height ratio is the height of the object in the image from the horizon line in the image divided by the height of the reference object in the image from the horizon line in the image; Use the distance calculation formula to calculate the distance between objects; The distance calculation formula is as follows: ; In the formula, S is the distance between the first object and the second object, is the distance from the first reference object to the vision sensor, is the distance from the second reference object to the vision sensor, is the distance from the first object to the vanishing point, is the distance from the second object to the vanishing point, is the distance from the first reference object to the vanishing point, is the distance from the second reference object to the vanishing point.

[0008] Statistically analyze the heights, distances between objects, and the front-back relationship between objects in the image. Abstract the objects into geometric bodies to obtain a three-dimensional obstacle map.

[0009] Preferably, statistically analyze the bending angles of the hip joint, knee joint, and ankle joint when normal humans cross obstacles, and establish a database; Obtain the lengths from the hip joint to the knee joint and from the knee joint to the ankle joint when the multi-degree-of-freedom robot stands normally; Match the angles of hip joint, knee joint and ankle joint flexion of humans when crossing obstacles of the same height with the data related to the legs of a multi-degree-of-freedom robot in the database; Analyze the physical center of gravity when the flexion angle of each joint reaches the maximum, and the steps are as follows: Divide the multi-degree-of-freedom robot into upper and lower parts at the hip joint, and the physical center of gravity of the upper part is on the same vertical line as the geometric center of gravity of the upper part; Divide the upper part into upper and lower halves along the horizontal line in the middle of the vertical line located in the upper part, and count the mass of the upper half and the mass of the lower half; The physical center of gravity of the upper part is located at a preset distance from the midpoint of the vertical line located in the upper part. If the preset value is positive, it means that the physical center of gravity is above the midpoint. If the preset value is negative, it means that the physical center of gravity is below the midpoint; Obtain the physical centers of gravity of the thighs and calves on both sides of the multi-degree-of-freedom robot by the suspension method; Obtain the three-dimensional coordinates of the physical centers of gravity of the thighs and calves on both sides when the flexion angles of each joint of the multi-degree-of-freedom robot reach the maximum; Use the center of gravity position formula to calculate the coordinates of the physical center of gravity of the lower part. The center of gravity position formula is as follows: ; In the formula, is the physical center of gravity of the lower part, is the mass of the left thigh, is the mass of the right thigh, is the mass of the left calf, is the mass of the right calf, is the coordinate of the left thigh, is the coordinate of the right thigh, is the coordinate of the left calf, is the coordinate of the right calf; Obtain the coordinates of the physical center of gravity of the upper part. Each component coordinate of the physical center of gravity of the multi-degree-of-freedom robot is the sum of the product of the component coordinate of the physical center of gravity of the upper part and the mass of the upper part and the product of the component coordinate of the physical center of gravity of the lower part and the mass of the lower part, divided by the total mass of the multi-degree-of-freedom robot; Judge whether the physical center of gravity and the geometric center of gravity of the multi-degree-of-freedom robot are on the same vertical line. If so, the multi-degree-of-freedom robot can cross the obstacle; The steps to obtain the preset value are as follows: Use the distance formula to obtain the preset value. The distance formula is as follows: ; In the formula, A is the preset value, the mass of the upper half is , the mass of the lower half is , and the length of the vertical line located in the upper part is l.

[0010] Preferably, the angles of flexion of the hip joint, knee joint, and ankle joint of normal humans when crossing obstacles are statistically analyzed to establish a database; Obtain the length from the hip joint to the knee joint and the length from the knee joint to the ankle joint of the multi-degree-of-freedom robot when standing normally; Match the angles of flexion of the hip joint, knee joint, and ankle joint of humans in the database whose leg-related data is consistent with that of the multi-degree-of-freedom robot when crossing obstacles of the same height; The controller adjusts the bending angles of each joint of the multi-degree-of-freedom robot according to the matching data.

[0011] Preferably, obtain the height and maximum width of the multi-degree-of-freedom robot; Based on the three-dimensional obstacle map, filter out the entrances whose height is greater than the height of the multi-degree-of-freedom robot and whose width is greater than the maximum width of the multi-degree-of-freedom robot, and record them as alternative entrances; The multi-degree-of-freedom robot preferentially selects the rightmost alternative entrance to enter. After entering the entrance, determine whether the multi-degree-of-freedom robot can cross the obstacles encountered again. If so, cross the obstacles encountered again. If not, plan a detour route for the obstacles encountered again until the multi-degree-of-freedom robot reaches the working position.

[0012] Preferably, use an ultrasonic sensor to obtain the distance and direction between the ultrasonic sensor and at least one point on the grasped object; In the simulation system, project the positions of at least one point according to the distance directions of at least one point; Based on the distribution positions of the point map, connect at least one point to construct a surface to obtain the basic model of the grasped object; Based on the image information of the vision sensor, use artificial intelligence to search and compare to determine the type of the object in the image; Based on the type of the grasped object, refine the surface roughness on the basis of the basic model to obtain the surface friction coefficient of the grasped object; Based on the type of the grasped object, determine the density of the grasped object, determine the volume of the object by image recognition, and calculate the gravity of the object based on the volume and density of the object.

[0013] Preferably, uniformly take at least one sampling point on the object surface, make a tangent plane of the object surface at the sampling point, and use the angle between the tangent plane corresponding to the sampling point and the vertical direction as the relative angle; Take the number of fingers of the multi-degree-of-freedom robot as the eigenvalue; Divide the gravity of the object by the friction coefficient at the grasping point and then divide by the eigenvalue to obtain the finger grasping force; According to the type of the object, obtain the bearable pressure of the sampling point of the object; Take the sampling points that can withstand a pressure greater than the finger grasping force as the target sampling points; Randomly combine the target sampling points to form at least one target sampling point combination, and the number of sampling points included in the target sampling point combination is equal to the eigenvalue; Take the target sampling point combination with the target sampling points coplanar as the calibration sampling point combination; Take the closed planar area formed by successively connecting adjacent target sampling points in the calibration sampling point combination as the planar sampling area; Take the area passed by moving the planar sampling area in the vertical direction as the three-dimensional projection range of the calibration sampling point combination; Obtain the center of gravity of the object, and take the calibration sampling point combination corresponding to the three-dimensional projection range containing the center of gravity of the object as the preliminary sampling point combination; Take the surface of the object above the plane where the planar sampling area is located as the characteristic surface; Select the characteristic sampling point combination from at least one calibration sampling point combination, satisfying that the area of the characteristic surface generated by the planar sampling area corresponding to the characteristic sampling point combination is the smallest; Use the sampling points in the characteristic sampling point combination as the grasping points.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: determining three-dimensional obstacles based on a vision sensor and an ultrasonic sensor provides a basis for a multi-degree-of-freedom robot to cross obstacles, judging in advance whether the multi-degree-of-freedom robot can cross the obstacles, avoiding waste of energy caused by finding that it cannot cross during the crossing process, an important basis for judging whether the multi-degree-of-freedom robot crosses the obstacles is whether the center of gravity is unstable and causes a fall during the crossing process, avoiding damage to the robot caused by a fall during the crossing process, and avoiding the grabbed object from slipping by positioning the grasping points. Description of the Drawings

[0015] Figure 1 It is a schematic flow chart of a control method for a multi-degree-of-freedom robot of the present invention; Figure 2 It is a schematic flow chart of obtaining the picture information of a vision sensor, and a processor analyzes the picture information to obtain the picture processing result of the present invention; Figure 3 It is a schematic flow chart of drawing a three-dimensional obstacle map based on the direction and distance of an obstacle of the present invention; Figure 4 It is a schematic flow chart of judging whether a multi-degree-of-freedom robot can cross an obstacle based on the height of the obstacle and various parameters of the robot itself of the present invention; Figure 5 It is a schematic flow chart of a controller scheduling the operating parameters of each component of a multi-degree-of-freedom robot to complete obstacle crossing based on the height of the obstacle of the present invention; Figure 6 Schematic diagram of the process for the robot of the present invention to plan a detour route; Figure 7 Schematic diagram of the process for the present invention to determine the size and shape of an object to be grasped based on the screen information of a vision sensor and the distance judgment of an ultrasonic sensor; Figure 8 Schematic diagram of the process for the present invention to determine the grasping points and forces of a multi-degree-of-freedom robot based on the size and shape of the grasped object. Detailed implementation manners

[0016] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0017] Referring to Figure 1 As shown, a multi-degree-of-freedom robot control method includes: Obtain the screen information of a vision sensor, and the processor analyzes the screen information to obtain a screen processing result; Based on the screen processing result, determine the direction of the obstacle, and use an ultrasonic sensor to determine the distance between the obstacle in the direction of the obstacle and the multi-degree-of-freedom robot; Based on the direction and distance of the obstacle, draw a three-dimensional obstacle map; Based on the height of the obstacle and the various parameters of the robot itself, determine whether the multi-degree-of-freedom robot can cross the obstacle; If it can, the robot crosses the obstacle; Based on the height of the obstacle, the controller schedules the operating parameters of each component of the multi-degree-of-freedom robot to complete crossing the obstacle; If not, the robot plans a detour route; If there is a grasping task, based on the screen information of the vision sensor and the distance judgment of the ultrasonic sensor, determine the size and shape of the object to be grasped; Based on the size and shape of the grasped object, the processor determines the grasping points and forces of the multi-degree-of-freedom robot.

[0018] It can be explained that determining the three-dimensional obstacle based on the vision sensor and the ultrasonic sensor provides a basis for the multi-degree-of-freedom robot to cross the obstacle, and judges in advance whether the multi-degree-of-freedom robot can cross the obstacle, avoiding waste of energy caused by finding that it cannot cross during the crossing process. An important basis for judging whether the multi-degree-of-freedom robot can cross the obstacle is whether it will fall due to unstable center of gravity during the crossing process, avoiding damage to the robot caused by falling during the crossing process, and avoiding the grasped object from slipping by positioning the grasping point.

[0019] Referring to Figure 2As shown, obtain the image information of the vision sensor, and the processor analyzes the image information to obtain the image processing results including: The multi-degree-of-freedom robot rotates the vision sensor of its head to obtain images from different perspectives; Record the horizontal line in the middle of the image as the horizon line; Find at least two parallel lines in reality in the image, extend the at least two parallel lines in the two-dimensional image, and record the obtained intersection point as the vanishing point, and the parallel lines as the vanishing lines; If the distance of the first object on the same vanishing line from the vanishing point is greater than the distance of the second object from the vanishing point, then the first object is behind the second object; Obtain the height difference between the object and the horizon line. If the height difference between the object and the horizon line is negative, it means that the height of the object is lower than that of the object with a positive height difference; Store the positional relationship and height information of the object in the memory.

[0020] It can be explained that the vanishing point in the image is determined by using the perspective method. The so-called vanishing point refers to the point where parallel lines in the three-dimensional world intersect in the two-dimensional drawing. By using the distance of the object from the vanishing point, the distance relationship between objects can be determined, and then by using the characteristic that the horizon line is parallel to the vision sensor and the height difference between the object and the horizon line, the height difference between objects can be determined.

[0021] Refer to Figure 3 As shown, based on the direction and distance of the obstacle, draw a three-dimensional obstacle map including: Select the object with the shortest straight-line distance from the vision sensor as the reference object; Measure the distances from the vision sensor to the bottom and top of the reference object, and use the Pythagorean theorem to obtain the height of the reference object; Based on the relative height ratio of at least one object and the reference object in the image, multiply the relative height ratio by the height of the reference object to obtain the height of the object. The relative height ratio is the height of the object in the image from the horizon line in the image divided by the height of the reference object in the image from the horizon line in the image; Use the distance calculation formula to calculate the distance between objects; The distance calculation formula is: ; In the formula, S is the distance between the first object and the second object, is the distance from the first reference object to the vision sensor, is the distance from the second reference object to the vision sensor, is the distance from the first object to the vanishing point, is the distance from the second object to the vanishing point, is the distance from the first reference object to the vanishing point, is the distance from the second reference object to the vanishing point.

[0022] Statistically analyze the height of all objects in the scene, the distances between objects, and the front-back relationships between objects, abstract the objects into geometric bodies, and obtain a three-dimensional obstacle map.

[0023] It can be explained that the ultrasonic sensor, the top and bottom of the reference object form a right triangle. Given the distances from the ultrasonic sensor to the top and bottom of the reference object, the height of the reference object can be obtained. Then, using the height of the reference object and the relative height ratios between other objects and the reference object, the heights of other objects can be obtained. By using the fact that the ratio of the distance difference between two objects to the distance between the two objects on the screen is fixed, the distances between objects can be obtained. The distances between objects can be obtained from the distance of the ultrasonic sensor, and the distance difference between two objects on the screen can be calculated from the distance difference between the objects to the vanishing point.

[0024] Refer to Figure 4 As shown, based on the height of the obstacle and the various parameters of the robot itself, determining whether a multi-degree-of-freedom robot can cross the obstacle includes: Statistically analyze the bending angles of the hip joint, knee joint, and ankle joint when normal humans cross obstacles, and establish a database; Obtain the length from the hip joint to the knee joint and the length from the knee joint to the ankle joint when the multi-degree-of-freedom robot stands normally; Match the bending angles of the hip joint, knee joint, and ankle joint when a human whose leg-related data in the database is consistent with that of the multi-degree-of-freedom robot crosses an obstacle of the same height; Analyze the physical center of gravity when the bending angle of each joint reaches the maximum, and the steps are as follows: Divide the multi-degree-of-freedom robot into upper and lower parts from the hip joint. The physical center of gravity of the upper part is on the same vertical line as the geometric center of gravity of the upper part; Divide the upper part into upper and lower halves along the horizontal line in the middle of the vertical line located in the upper part, and statistically analyze the mass of the upper half and the mass of the lower half; The physical center of gravity of the upper part is located at a preset distance from the midpoint of the vertical line located in the upper part. If the preset value is positive, it means the physical center of gravity is above the midpoint. If the preset value is negative, it means the physical center of gravity is below the midpoint; Obtain the physical centers of gravity of the two thighs and calves on both sides of the multi-degree-of-freedom robot through the suspension method; Obtain the three-dimensional coordinates of the physical centers of gravity of the two thighs and calves on both sides when the bending angles of the various joints of the multi-degree-of-freedom robot reach the maximum; Use the center-of-gravity position formula to calculate the physical center-of-gravity coordinates of the lower part. The center-of-gravity position formula is as follows: ; In the formula, is the physical center of gravity of the lower part, is the mass of the left thigh, is the mass of the right thigh, is the mass of the left calf, is the mass of the right calf, are the coordinates of the left thigh, are the coordinates of the right thigh, are the coordinates of the left calf, are the coordinates of the right calf; Obtain the coordinates of the physical center of gravity of the upper part. Each sub - coordinate of the physical center of gravity of the multi - degree - of - freedom robot is the sum of the product of the sub - coordinate of the physical center of gravity of the upper part and the mass of the upper part plus the product of the sub - coordinate of the physical center of gravity of the lower part and the mass of the lower part, divided by the overall mass of the multi - degree - of - freedom robot; Judge whether the physical center of gravity and the geometric center of gravity of the multi - degree - of - freedom robot are on the same vertical line. If so, the multi - degree - of - freedom robot can cross the obstacle; The steps for obtaining the preset value are as follows: Use the distance formula to obtain the preset value. The distance formula is as follows: ; In the formula, A is the preset value, the upper - half mass is , the lower - half mass is , and the length of the vertical line located in the upper part is l.

[0025] It can be explained that the multi - degree - of - freedom robot is made with reference to the human body structure. When the multi - degree - of - freedom robot crosses an obstacle, it refers to the joint rotation angles of humans, which is beneficial for better crossing of obstacles. Whether the multi - degree - of - freedom robot can cross an obstacle, in addition to the maximum limit height being greater than the obstacle height, also needs to consider whether the multi - degree - of - freedom robot will tip over during the crossing process. We judge the stability of the multi - degree - of - freedom robot by determining whether the physical center and the geometric center are on the same vertical line when each joint of the multi - degree - of - freedom robot rotates to the maximum angle.

[0026] Refer to Figure 5 As shown, based on the height of the obstacle, the controller schedules the operating parameters of each component of the multi - degree - of - freedom robot to complete obstacle crossing, including: Statistically analyze the bending angles of the hip joint, knee joint, and ankle joint when normal humans cross an obstacle, and establish a database; Obtain the length from the hip joint to the knee joint and the length from the knee joint to the ankle joint when the multi - degree - of - freedom robot stands normally; Match the bending angles of the hip joint, knee joint, and ankle joint of humans in the database whose leg - related data are consistent with those of the multi - degree - of - freedom robot when crossing an obstacle of the same height; The controller adjusts the bending angles of each joint of the multi - degree - of - freedom robot according to the matching data.

[0027] It can be explained that a database is established to statistically analyze the bending angles of various joints of humans when crossing obstacles. The multi-degree-of-freedom robot is developed with reference to the human structure. When crossing, it refers to the actions of humans crossing obstacles to improve the success rate of crossing. By matching the lengths of the thighs and calves, the accuracy of the rotating joints is improved.

[0028] Refer to Figure 6 As shown, the planned detour route of the robot includes: Obtain the height and maximum width of the multi-degree-of-freedom robot; Based on the three-dimensional obstacle map, filter out the entrances with a height greater than the height of the multi-degree-of-freedom robot and a width greater than the maximum width of the multi-degree-of-freedom robot, and record them as alternative entrances; The multi-degree-of-freedom robot preferentially selects the rightmost alternative entrance to enter. After entering the entrance, determine whether the multi-degree-of-freedom robot can cross the obstacle encountered again. If so, cross the obstacle encountered again. If not, plan a detour route for the obstacle encountered again until the multi-degree-of-freedom robot reaches the working position.

[0029] It can be explained that first, filter out the passable entrances as the alternative option entrances. By regularly taking the right entrances, if the next route cannot pass, return to the previous intersection and try the next entrance again. Regularly try every possibility to ensure that no possibility is missed, achieve a traversal of all possible choices, and finally find the correct route.

[0030] Refer to Figure 7 As shown, if there is a grasping task, based on the visual information of the visual sensor and the distance judgment of the ultrasonic sensor, determine the size and shape of the object to be grasped, including: Use the ultrasonic sensor to obtain the distance and direction between the ultrasonic sensor and at least one point on the grasping object; In the simulation system, project the positions of at least one point according to the distance directions of at least one point; Based on the distribution positions of the dot map, connect at least one point to construct a surface to obtain the basic model of the grasping object; Based on the visual information of the visual sensor, use artificial intelligence to search and compare to determine the types of objects in the picture; Based on the type of the grasping object, refine the surface roughness on the basis of the basic model to obtain the surface friction coefficient of the grasping object; Based on the type of the grasping object, determine the density of the grasping object, determine the volume of the object by image recognition, and calculate the gravity of the object based on the volume and density of the object.

[0031] It can be explained that the direction and distance of the points on the grasped object are determined by the ultrasonic sensor, a point map is drawn, and the basic model of the grasped object is determined. The type of the grasped object is determined by the vision sensor, and further the surface friction coefficient and density of the grasped object are determined, and the gravity of the grasped object is calculated to facilitate the determination of the grasping force.

[0032] Referring to Figure 8 As shown, based on the size and shape of the grasped object, the processor determines the grasping points and force of the multi-degree-of-freedom robot, including: Take at least one sampling point uniformly on the object surface. At the sampling point, make the tangent plane of the object surface, and take the angle between the tangent plane corresponding to the sampling point and the vertical direction as the relative angle; Take the number of fingers of the multi-degree-of-freedom robot as the eigenvalue; The gravity of the object is divided by the friction coefficient of the grasping point and then divided by the eigenvalue to obtain the finger grasping force; According to the type of the object, obtain the bearable pressure of the sampling points of the object; Take the sampling points whose bearable pressure is greater than the finger grasping force as the target sampling points; Randomly combine the target sampling points to form at least one target sampling point combination, and the number of sampling points included in the target sampling point combination is equal to the eigenvalue; Take the target sampling point combination where the target sampling points are coplanar as the calibrated sampling point combination; Take the closed plane area formed by successively connecting adjacent target sampling points in the calibrated sampling point combination as the plane sampling area; Take the area passed by the plane sampling area moving along the vertical direction as the three-dimensional projection range of the calibrated sampling point combination; Obtain the center of gravity of the object, and take the calibrated sampling point combination corresponding to the three-dimensional projection range containing the center of gravity of the object as the preliminary sampling point combination; Take the surface of the object above the plane where the plane sampling area is located as the characteristic surface; Select the characteristic sampling point combination from at least one calibrated sampling point combination, satisfying that the area of the characteristic surface generated by the plane sampling area corresponding to the characteristic sampling point combination is the smallest; Use the sampling points in the characteristic sampling point combination as the grasping points.

[0033] It can be explained that the pressures exerted by each finger form frictions to jointly support the grasped object in the hand. The bearable force of the sampling points needs to be greater than the grasping force to prevent damage to the grasped object. The smaller the angle between the tangent plane corresponding to the sampling point and the vertical direction, the greater the vertical component of the friction force caused by the pressure exerted by the finger, and the smaller the force required, which is convenient for saving energy. Ensuring that the center is within the three-dimensional projection range can ensure that the grasped object is not easy to slip.

[0034] Furthermore, this solution also proposes a storage medium, on which a computer-readable program is stored. When the computer-readable program is called, it executes the above-mentioned multi-degree-of-freedom robot control method and method.

[0035] It can be understood that the storage medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid-state disk (SSD).

[0036] In summary, the advantages of the present invention are as follows: determining three-dimensional obstacles based on a vision sensor and an ultrasonic sensor provides a basis for a multi-degree-of-freedom robot to cross obstacles, pre-judging whether the multi-degree-of-freedom robot can cross obstacles, avoiding waste of energy caused by finding that it cannot cross during the crossing process, and an important basis for judging whether the multi-degree-of-freedom robot crosses obstacles is whether it will fall due to unstable center of gravity during the crossing process, avoiding damage to the robot caused by falling during the crossing process, and avoiding the slipping of the grabbed object by positioning the grabbing point.

[0037] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-degree-of-freedom robot control method, characterized in that, Including: Obtain the frame information of the vision sensor, and the processor analyzes the frame information to obtain the frame processing result; Based on the frame processing result, determine the direction of the obstacle, and use the ultrasonic sensor to determine the distance between the obstacle in the direction of the obstacle and the multi-degree-of-freedom robot; Based on the direction and distance of the obstacle, draw a three-dimensional obstacle map; Based on the height of the obstacle and various parameters of the robot itself, determine whether the multi-degree-of-freedom robot can cross the obstacle; If it can, the robot crosses the obstacle; Based on the height of the obstacle, the controller schedules the operating parameters of each component of the multi-degree-of-freedom robot to complete crossing the obstacle; If not, the robot plans a detour route; If there is a grasping task, based on the judgment of the frame information of the vision sensor and the distance of the ultrasonic sensor, determine the size and shape of the object to be grasped; Based on the size and shape of the grasped object, the processor determines the grasping point and force of the multi-degree-of-freedom robot.

2. The multi-degree-of-freedom robot control method according to claim 1, wherein The obtaining the frame information of the vision sensor, and the processor analyzes the frame information to obtain the frame processing result includes: The multi-degree-of-freedom robot rotates the head vision sensor to obtain frames from different perspectives; Record the horizontal line in the middle of the frame as the horizon line; Find at least two real parallel lines in the frame, extend the at least two parallel lines in the two-dimensional frame, and record the obtained intersection point as the vanishing point, and the parallel lines as the vanishing lines; If the distance of the first object on the same vanishing line from the vanishing point is greater than the distance of the second object from the vanishing point, then the first object is behind the second object; Obtain the height difference between the object and the horizon line. If the height difference between the object and the horizon line is negative, it means that the height of the object is lower than the object with a positive height difference; Store the position relationship and height information of the object in the memory.

3. A multi-degree-of-freedom robot control method according to claim 2, characterized in that The drawing the three-dimensional obstacle map based on the direction and distance of the obstacle includes: Select the object with the shortest straight-line distance from the vision sensor as the reference object; Measure the distances from the vision sensor to the bottom and top of the reference object, and use the Pythagorean theorem to obtain the height of the reference object; Based on the relative height ratio between at least one object in the frame and the reference object, multiply the relative height ratio by the height of the reference object to obtain the height of the object. The relative height ratio is the height of the object in the frame from the horizon line in the frame divided by the height of the reference object in the frame from the horizon line in the frame; Use the distance calculation formula to calculate the distance between objects; The distance calculation formula is as follows: ; Where S is the distance between the first object and the second object, is the distance from the first reference object to the vision sensor, is the distance from the second reference object to the vision sensor, is the distance from the first object to the vanishing point, is the distance from the second object to the vanishing point, is the distance from the first reference object to the vanishing point, is the distance from the second reference object to the vanishing point; Statistically analyze the heights, distances between objects, and front-back relationships between objects in the frame, abstract the objects into geometric bodies, and obtain a three-dimensional obstacle map.

4. A multi-degree-of-freedom robot control method according to claim 3, characterized in that, The judging whether the multi-degree-of-freedom robot can cross the obstacle based on the height of the obstacle and various parameters of the robot itself includes: Statistically analyze the angles of flexion of the hip joint, knee joint, and ankle joint when a normal human crosses an obstacle, and establish a database; Obtain the length from the hip joint to the knee joint and the length from the knee joint to the ankle joint when the multi-degree-of-freedom robot stands normally; Match the angles of flexion of the hip joint, knee joint, and ankle joint when a human with leg-related data in the database consistent with the multi-degree-of-freedom robot crosses an obstacle of the same height; Analyze the physical center of gravity when the flexion angle of each joint reaches the maximum, and the steps are as follows: The multi - degree - of - freedom robot is divided into upper and lower parts at the hip joint, and the physical center of gravity of the upper part is on the same vertical line as the geometric center of gravity of the upper part; The upper part is divided into upper and lower halves along the horizontal line in the middle of the vertical line located in the upper part, and the mass of the upper half and the mass of the lower half are counted; The physical center of gravity of the upper part is located at a preset distance from the mid - point of the vertical line located in the upper part. If the preset value is positive, it means the physical center of gravity is above the mid - point. If the preset value is negative, it means the physical center of gravity is below the mid - point; By the suspension method, obtain the physical centers of gravity of the thighs and calves on both sides of the multi - degree - of - freedom robot; Obtain the three - dimensional coordinates of the physical centers of gravity of the thighs and calves on both sides when the bending angles of the joints of the multi - degree - of - freedom robot reach the maximum; Using the center - of - gravity position formula, calculate the coordinates of the physical center of gravity of the lower part. The center - of - gravity position formula is as follows: ； In the formula, is the physical center of gravity of the lower part, is the mass of the left thigh, is the mass of the right thigh, is the mass of the left calf, is the mass of the right calf, are the coordinates of the left thigh, are the coordinates of the right thigh, are the coordinates of the left calf, are the coordinates of the right calf; Obtain the coordinates of the physical center of gravity of the upper part. Each component coordinate of the physical center of gravity of the multi - degree - of - freedom robot is the sum of the product of the component coordinates of the physical center of gravity of the upper part and the mass of the upper part and the product of the component coordinates of the physical center of gravity of the lower part and the mass of the lower part, divided by the total mass of the multi - degree - of - freedom robot; Judge whether the physical center of gravity and the geometric center of gravity of the multi - degree - of - freedom robot are on the same vertical line. If so, the multi - degree - of - freedom robot can cross the obstacle; The steps for obtaining the preset value are as follows: Use the distance formula to obtain the preset value. The distance formula is as follows: ； where A is a preset value, the upper mass is , and the lower mass is , and the length of the vertical line located in the upper part is l.

5. A multi-degree-of-freedom robot control method according to claim 4, characterized in that, Based on the height of the obstacle, the controller schedules the operating parameters of each component of the multi - degree - of - freedom robot to complete obstacle crossing, including: Count the bending angles of the hip joint, knee joint, and ankle joint of normal humans when crossing obstacles, and establish a database; Obtain the length from the hip joint to the knee joint and the length from the knee joint to the ankle joint of the multi - degree - of - freedom robot when standing normally; Match the bending angles of the hip joint, knee joint, and ankle joint of humans in the database whose leg - related data are the same as those of the multi - degree - of - freedom robot when crossing obstacles of the same height; The controller adjusts the bending angles of each joint of the multi - degree - of - freedom robot according to the matching data.

6. A multi-degree-of-freedom robot control method according to claim 5, characterized in that The robot's planned detour route includes: Obtain the height and maximum width of the multi - degree - of - freedom robot; Based on the three - dimensional obstacle map, screen the entrances whose height is greater than the height of the multi - degree - of - freedom robot and whose width is greater than the maximum width of the multi - degree - of - freedom robot, and record them as alternative entrances; The multi - degree - of - freedom robot preferentially selects the right - most alternative entrance to enter. After entering the entrance, judge whether the multi - degree - of - freedom robot can cross the obstacle encountered again. If so, cross the obstacle encountered again. If not, plan a detour route for the obstacle encountered again until the multi - degree - of - freedom robot reaches the operation position.

7. A multi-degree-of-freedom robot control method according to claim 6, characterized in that, If there is a grasping task, based on the visual information of the visual sensor and the distance of the ultrasonic sensor, determine the size and shape of the object to be grasped, including: Use the ultrasonic sensor to obtain the distance and direction between the ultrasonic sensor and at least one point on the grasping object; In the simulation system, project the positions of at least one point according to the distance direction of at least one point; Based on the distribution positions of the dot map, connect at least one point to construct a surface to obtain the basic model of the grasping object; Based on the visual information of the visual sensor, use artificial intelligence search and comparison to determine the type of the object in the picture; Based on the type of the object to be grasped, refine the surface roughness on the basis of the basic model to obtain the surface friction coefficient of the object to be grasped; Based on the type of the object to be grasped, determine the density of the object to be grasped, determine the volume of the object by means of image recognition, and calculate the gravity of the object based on the volume and density of the object.

8. A multi-degree-of-freedom robot control method according to claim 7, characterized in that The processor determines the grasping points and forces of the multi-degree-of-freedom robot based on the size and shape of the object to be grasped, including: Uniformly take at least one sampling point on the object surface, make a tangent plane of the object surface at the sampling point, and take the angle between the tangent plane corresponding to the sampling point and the vertical direction as the relative angle; Take the number of fingers of the multi-degree-of-freedom robot as the eigenvalue; Divide the gravity of the object by the friction coefficient of the grasping point and then divide by the eigenvalue to obtain the finger grasping force; According to the type of the object, obtain the bearable pressure of the sampling point of the object; Take the sampling points with bearable pressure greater than the finger grasping force as the target sampling points; Randomly combine the target sampling points to form at least one target sampling point combination, and the number of sampling points included in the target sampling point combination is equal to the eigenvalue; Take the target sampling point combination with the target sampling points coplanar as the calibrated sampling point combination; Take the closed plane area formed by successively connecting adjacent target sampling points in the calibrated sampling point combination as the plane sampling area; Take the area passed by moving the plane sampling area in the vertical direction as the three-dimensional projection range of the calibrated sampling point combination; Obtain the center of gravity of the object, and take the calibrated sampling point combination corresponding to the three-dimensional projection range containing the center of gravity of the object as the preliminary sampling point combination; Take the surface of the object above the plane where the plane sampling area is located as the characteristic surface; Screen out the characteristic sampling point combination from at least one calibrated sampling point combination, satisfying that the area of the characteristic surface generated by the plane sampling area corresponding to the characteristic sampling point combination is the smallest; Use the sampling points in the characteristic sampling point combination as the grasping points.

9. A multi-degree-of-freedom robot control system for implementing a multi-degree-of-freedom robot control method according to any one of claims 1-8, characterized in that, Including: An obstacle model establishment module, which determines the height and distance of the obstacle based on a vision sensor and an ultrasonic sensor, and constructs a three-dimensional obstacle map; An obstacle crossing determination module, which determines whether the multi-degree-of-freedom robot can cross the obstacle based on the height of the obstacle and the own data of the multi-degree-of-freedom robot; A route bypass module, which provides a bypass route for the multi-degree-of-freedom robot; An object grasping module, which determines the bending degree of the finger joints and the grasping force of the multi-degree-of-freedom robot during grasping based on the size and shape of the object.

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