JP2013182554A - Holding attitude generation device, holding attitude generation method and holding attitude generation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To solve the problem of the conventional holding attitude generation methods in which a solution cannot be obtained, as a problem of inverse kinematics, unless end point positions of a hand, a finger and the like are correctly specified.SOLUTION: A holding attitude generation device 10 of the present invention includes: an input part 12 for inputting a shape of an object to be held; a hand holding attitude candidate generation part 13 for generating a holding attitude candidate being a candidate of a holding attitude of a hand corresponding to the input object; a part-except-hand attitude generation part 14 for obtaining an angle of each joint between a position of the holding attitude candidate and another position of a part, except hands, on a body; an attitude evaluation part 15 for evaluating the holding attitude candidate generated by the hand holding attitude candidate generation part 13, and the attitudes of the parts except hands generated by the part-except-hand attitude generation part 14, using one or more evaluation functions; and an output part 16 for outputting the attitudes of the hand and the parts except hands evaluated by the attitude evaluation part 15.

Description

  The present invention relates to a gripping posture generation device, a gripping posture generation method, and a gripping posture generation program that generate a gripping posture of a part on the body including a gripping posture of a hand that grips an object.

  In various fields, such as automatic generation of character animation and movement generation of humanoid robots, it is required to generate human-like postures. There are various types of postures for gripping an object with a hand, particularly those showing human-like postures. Further, in the hand gripping posture, the gripping posture is properly used depending on the shape of the object to be gripped and the work purpose, and the posture is very complicated and various. Therefore, generating the posture of the upper limb including such a gripping posture of the hand must consider the movement of the upper limb in addition to the various and complicated gripping postures of the hand, and more complicated and diverse postures. There is a difficulty that must be generated.

  In computer graphics, the structure of a skeletal model such as a human body is treated as a rigid link connected by a joint having a degree of freedom of rotation. The degree of freedom at the end in the three-dimensional space is 6 degrees of freedom in terms of position and direction, but the joint degree of freedom of the human body is generally larger than that. Therefore, in inverse kinematics that deals with the problem of obtaining the joint angle of each joint when the position of the end is given, there are redundant degrees of freedom, so the solution cannot be obtained uniquely.

  Therefore, a method for optimizing the posture using a function that evaluates the naturalness of the posture with the joint angle as an argument has been proposed (for example, see Non-Patent Document 1).

  In addition, the following method has been proposed as a method when the upper limb is the target of inverse kinematics. That is, since the shoulder joint has 3 degrees of freedom, the elbow joint has 2 degrees of freedom, and the wrist joint has 2 degrees of freedom, the total degree of freedom of the joints in the upper limb is 7. For this reason, if the position of the wrist joint and the position of the shoulder joint are fixed, the elbow joint position can be swung in a circle, so analysis is performed by giving the swivel angle that is the angle at this time Inverse kinematics can be solved (see, for example, Non-Patent Document 2).

  Furthermore, a method for estimating a joint range of motion by defining a probability density function based on motion capture data has been proposed (see, for example, Non-Patent Documents 3 and 4).

R. Timothy Marler, Jasbir S. Arora, Jingzou Yang, Hyung-Joo Kim and Karim Abdel-Malek, `` Use of multi-objective optimization for digital human posture prediction '', Engineering Optimization, Vol. 41, No. 10, pages 925 -943, 2009. Marcelo Kallmann, `` Analytical Inverse Kinematics with Body Posture Control '', Computer Animation and Virtual Worlds (CAVW), Vol. 19, No. 2, pages 79-91, 2008. Hiroshi Yasuda, Suguru Saito and Masayuki Nakajima, `` Range of motion estimation from Mocap data '', In Proceedings of the 2005 International Conference on Cyberworlds, pages 483-489, 2005. Hiroshi Yasuda, Go Saito, Masayuki Nakajima, “Determining the range of motion of a ball joint human body model by motion capture”, IEICE Transactions D, J92-D (2), pages 255-261, 2009.

  In the method described in Non-Patent Document 1 or Non-Patent Document 2, it is necessary to specify the inside position of a specific hand, a specific position on the surface, or the position of a finger (including which finger) as the end position. .) Must be specified accurately. If the gripping posture of the hand is not fixed, such a designation cannot be made. Therefore, it is difficult to generate a posture including the posture of the hand gripping the object.

  Further, in the hand gripping posture, it is necessary to consider the angle of each finger joint, and the degree of freedom becomes very high, so it is difficult to generate the posture by inverse kinematics. In the method of analytically solving the joint angle described in Non-Patent Document 2, a solution cannot be obtained because the degree of freedom is high. In the method for numerical analysis as an optimization problem described in Non-Patent Document 1, the amount of calculation increases corresponding to the degree of freedom of the target joint, which requires a large amount of computer power and is not practical. There is a problem with.

  The methods of Non-Patent Documents 3 and 4 that use motion capture are to convert the actual movement of the human body etc. into data, and in this method, only the range of motion of the joint is estimated, and this is used to automatically A natural posture cannot be generated.

  Therefore, the present invention grips an object and the object without accurately specifying the gripping posture of the hand, accurately specifying the position of the hand or finger, or specifying any other position on the body as an end point. A gripping posture generation device, a gripping posture generation program, and a gripping device that generate a gripping posture of a hand according to the shape of the object to be gripped and a body part reaching the position of the hand by roughly specifying the gripping position of the hand An object is to provide a posture generation method.

  In view of the above-described problems, the inventors generate a gripping posture of a hand and a posture of a body part other than the hand separately, and combine the separately generated postures, thereby grasping the whole gripping. It came to the idea of generating a posture.

  In other words, the gripping posture generation apparatus of the present invention inputs data of an object to be gripped, and based on a database including data on hand and finger postures suitable for gripping the object, Input the data of the hand gripping posture candidate generation unit for generating data and other positions on the body other than the hand, and between the other position and the hand position generated by the hand gripping posture candidate generation unit A posture generation unit other than the hand for obtaining the angle of the joint, and a posture evaluation unit that evaluates posture data other than the hand including the angle of the joint generated by the posture generation unit other than the hand based on a predetermined evaluation function Prepare. The hand gripping posture candidate generation unit generates data of one or more hand gripping posture candidates, and the posture generation unit other than the hand generates a hand position for each generated hand gripping posture candidate data. Then, the angle of the joint between other positions is obtained, and the posture data other than the hand having the optimum angle is generated from the angles of the joint.

  The gripping posture generation method of the present invention inputs the data of the object to be gripped, and obtains the hand gripping posture candidate data based on a database including data on the hand and finger posture suitable for gripping the object. Generating data, inputting data of other positions on the body other than the hand, and determining an angle of the joint between the other positions and the position of the hand of the generated gripping hand. And evaluating a posture other than the hand based on a predetermined evaluation function. The step of generating hand gripping posture candidate data generates one or more hand gripping posture candidate data, and the step of obtaining joint angles is performed for each generated hand gripping posture candidate data. A joint angle between the hand position and another position is obtained, and posture data other than the hand having the optimum joint angle is generated.

  A gripping posture generation program of the present invention is installed in a computer via a storage medium such as a network or an optical disk, and is executed on the computer to operate as a gripping posture generation device or to configure each step of the gripping posture method. Operate to generate gripping posture data. Further, the gripping posture generation program of the present invention is installed in a server and is executed on a client computer via a network and operates as a gripping posture generation device, or operates each step constituting the gripping posture method, Generate gripping posture data.

  According to the gripping posture generation device, the gripping posture generation method, or the gripping posture generation program of the present invention, a gripping posture of a hand is generated as a gripping posture candidate, and a part other than the hand based on the position of the hand of the gripping posture candidate Therefore, it is possible to determine the angle of each joint by driving inverse kinematics only by designating an approximate gripping position without accurately designating an end point on the body. Then, a plurality of hand gripping posture candidates are generated, and a posture of a part other than the hand is generated for each hand gripping posture candidate, and each posture is evaluated to determine the entire gripping posture including the hand. be able to.

It is the block diagram which showed the structural example of the holding | grip attitude | position production | generation apparatus which concerns on this invention. It is the block diagram which showed the structural example of the holding | grip attitude | position candidate generation part of the hand which comprises the holding | grip attitude | position production | generation apparatus which concerns on this invention. It is a figure which shows the example of correlation of each data of the database which comprises the holding | grip attitude | position candidate production | generation part of a hand. It is a figure for demonstrating the classification | category of the large classification | category holding posture of the database which comprises the holding posture candidate production | generation part of a hand, and each attitude | position. It is a figure which shows the holding | grip attitude | position candidate produced | generated by the grasping attitude | position production | generation part of the hand of the holding | grip attitude | position generator which concerns on this invention. (A) is a gripping posture candidate and local coordinates generated first, and (B) is an X-Z plane around the Y axis of the local coordinates by rotating the local coordinates generated in (A). Is a gripping posture candidate in a state of being rotated 90 degrees. (C) is a gripping posture candidate in a state where the XZ plane is rotated 180 degrees around the Y axis of the local coordinates of the gripping posture candidate generated in (A). It is a figure which shows the skeleton model of an upper limb. It is an example of the flowchart for demonstrating operation | movement of the holding | grip attitude | position production | generation apparatus which concerns on this invention. (A) is a figure which shows the result of having calculated using the evaluation function described in the nonpatent literature 1, and the holding | grip posture of the produced | generated upper limbs. (B) is a figure which shows the result of having performed the calculation using the probability density function described in the nonpatent literature 3 as an evaluation function, and the holding | grip posture of the produced | generated upper limbs. (C) is a figure which shows the result obtained from the sum of the evaluation function used by (A) and (B), and the holding | grip posture of the produced | generated upper limbs. It is a figure which shows the holding | grip attitude | position of the upper limb produced | generated by the attitude | position generating apparatus which concerns on this invention, and rotated the local coordinate with respect to the holding | grip attitude of a hand 90 degree | times centering on the Y-axis until (A)-(D). It is a figure which shows the holding posture of the upper limb in the case. It is a figure which shows the holding | grip attitude | position of the upper limb produced | generated by the attitude | position production | generation apparatus which concerns on this invention. It is a figure which shows the holding posture of the upper limb at the time of moving. It is a figure which shows the holding posture of the appropriate upper limb produced | generated with respect to the holding posture of the appropriate hand which hold | gripped various holding | grip target objects. It is a flowchart for demonstrating operation | movement of the holding | grip posture candidate production | generation part of a hand. It is a figure which shows the coordinate setting for calculating | requiring the data of the shape feature-value for every large classification | category holding attitude. (A) is a figure corresponding to a holding grip posture (Pinch type), (B) is a parallel grip posture (Parallel type), and (C) is a surrounding grip posture (Circular type). (A)-(C) is a figure for demonstrating the correlation of the representative point and contact point group of the side surface grasping | gripping attitude | position (Lat type) which is one of the small classification | category grasping attitude | positions. (A)-(C) is a figure for demonstrating the correlation of the representative point and contact point group of the extension grasping gripping posture (PE type) which is one of the small classification | category gripping postures.

[Configuration of gripping posture generation device]
FIG. 1 is a block diagram illustrating a configuration example of a gripping posture generation apparatus to which the present invention is applied. The gripping posture generation device 10 includes an input unit 12 that inputs the shape of an object to be gripped. In addition, the gripping posture generation device 10 includes a hand gripping posture candidate generation unit 13 that generates a gripping posture candidate that is a hand gripping posture candidate corresponding to the input object, and the position of the hand gripping posture candidate other than the hand. And a posture generation unit 14 other than the hand for obtaining an angle of each joint with another position on the body. In addition, the gripping posture generation apparatus 10 represents one or more hand gripping posture candidates generated by the hand gripping posture candidate generation unit 13 and / or postures other than the hand generated by the posture generation unit 14 other than the hand. A posture evaluation unit 15 that evaluates using an evaluation function is provided. In addition, the gripping posture generation apparatus 10 includes an output unit 16 that outputs the posture of the hand and a part other than the hand evaluated by the posture evaluation unit 15.

  The input unit 12 is connected to the hand gripping posture candidate generation unit 13 and provides data to the hand gripping posture candidate generation unit 13. Further, a fixed point of a part other than the hand may be input to the posture generation unit 14 other than the hand. For the input unit 12, an input device such as a keyboard and a mouse is used. By using the touch panel, a device that inputs data on the same screen as the data output to the output unit 16 can be used, and other input devices can be used. Alternatively, data can be input by using another storage device that stores data to be input as a file. Note that the fixed point of the part other than the hand with respect to the posture generation unit 14 other than the hand may be input by the input unit 12. However, in the posture generation unit 14 other than the hand, for example, the shoulder joint is input in advance as a fixed point. You may keep it.

  Based on the data of the three-dimensional object input from the input unit 12, the hand gripping posture candidate generation unit 13 holds the hand based on a database including data on the hand and finger posture suitable for the object data. Generate as posture candidates.

  Although it is preferable to use the gripping posture generation device disclosed in Japanese Patent Application No. 2011-184498 for the hand gripping posture candidate generation unit 13, it is not limited thereto.

  As shown in FIG. 2, the hand gripping posture candidate generation unit 13 stores a database 20 relating to hand and finger postures, and a gripping posture generation that generates hand gripping postures and outputs gripping posture candidates. Part 22.

  The storage unit 21 stores a database 20 including data such as a gripping posture, and is connected to the gripping posture generation unit 22 to exchange data. The storage unit 21 is preferably a readable / writable storage device such as a hard disk device, but is not limited to a hard disk device, and may be a solid-state memory such as a flash memory, and other storage devices may be used. .

  The gripping posture generation unit 22 is connected to the storage unit 21, accesses the database 20, searches for data on the database 20, retrieves data from the database 20, writes data into the database 20, etc. Can communicate. The gripping posture generation unit 22 can also access the storage unit 21 based on data input from the input unit 12 and perform predetermined processing based on data in the database 20. The gripping posture generation unit 22 reads data input from the input unit 12 according to a command from the CPU into a memory such as a RAM connected to the CPU, and accesses the database 20 in the storage unit 21 based on the data. To search data. As described above, new data can be generated from the retrieved data and written into the database 20 again. The data to be output is processed based on the written data. Then, output of the processed data is controlled. A series of these controls is performed to generate a desired hand gripping posture candidate.

  Here, the configuration of the database 20 will be described.

  As shown in FIG. 3, the database 20 includes a plurality of classified classification posture data, a classified classification posture data associated with each major classification posture, and a classified classification posture data. Including. The data of the large classification gripping posture is classified into gripping postures according to the shape of the object to be gripped, and has virtual finger data. The virtual finger data is associated with the corresponding finger data of the small classification grip posture data. Here, the virtual finger is a virtual finger that is composed of a finger, a combination of a plurality of fingers, or the palm of the hand, and comes into contact with the opposite surface when gripping an object. The data of the small classification gripping posture is obtained by further classifying the data of the large classification gripping posture by data such as the hand position and the finger placement. An object of a basic shape is gripped for each small classification gripping posture, and the gripping posture data at that time is indexed by the object width data and stored in the database 20. Data of the gripping posture when gripping with changing the width of the object having the basic shape is also indexed by the data of the object width and stored in the database 20. For each indexed gripping posture, joint angle data of each joint of each finger is also indexed and stored in the database 20. The data of the representative points arranged on the finger corresponding to each virtual finger and the data of the contact point group on the target finger are associated with the small classification gripping posture and stored in the database 20.

  As shown in FIG. 3, preferably, the data of the large classification gripping posture includes three types of gripping posture data. One of the data of the large classification gripping posture is data of a gripping gripping posture (hereinafter also referred to as “Pinch type”), which is a data representing a gripping posture that sandwiches an object with two belly or side surfaces of two fingers facing each other. It is. The other is data on a parallel gripping posture (hereinafter also referred to as “Parallel type”), where the belly of the thumb and the fingers other than the thumb are aligned in parallel, and the object is placed by facing the belly or palm of the finger. This is data representing a gripping posture to be grasped. The remaining one is data of the surrounding grip posture (hereinafter also referred to as “Circular type”), which is data representing the posture of gripping the object by wrapping the object with the belly or side surface of each finger. Note that the gripping posture data is not limited to being classified into three types, but may be classified into arbitrary types.

  Preferably, the small classification gripping posture data includes 14 types of gripping posture data. The 14 types of gripping posture data include gripping force standard gripping posture (hereinafter also referred to as PoS type), gripping force grasping vertical gripping posture (hereinafter also referred to as PoH), and gripping force grasping finger extending gripping posture (hereinafter referred to as PoI). A gripping force grasping and extending type gripping posture (hereinafter also referred to as PoE type), a side surface grasping type gripping posture (hereinafter also referred to as Lat type), and a three-sided gripping type gripping posture (hereinafter also referred to as Tpd). Three-surface grasping subtype I gripping posture (hereinafter also referred to as TpdV1 type), three-surface grasping subtype II gripping posture (hereinafter also referred to as TpdV2 type), three-surface grasping subtype III gripping posture (hereinafter also referred to as TpdV3 type). ), Parallel light bend grasping posture (hereinafter also referred to as PMF type), surrounding light bend grasping and holding posture (hereinafter also referred to as CMF type), fingertip grasping and holding posture (hereinafter also referred to as Tip type), parallel. Extension grasp Posture (hereinafter, also referred to as a PE type.) And adducted grasp gripping position (hereinafter, referred to as Add-type.) Consisting of the data. The classification is not limited to 14 types of gripping posture data, but may be classified into more types, or may be less than 14 types by integrating these classifications.

  FIG. 4 shows the postures classified into the large classification grip postures. As shown in FIG. 4A, numbers are assigned to the fingers of the hand to indicate the thumb 1, the indicating finger 2, the middle finger 3, the ring finger 4, and the little finger 5, and the palm is P. FIG. 4B shows a state in which the object 6 is gripped by a pinch-type gripping posture. The Pinch type is a gripping posture in which the object 6 is pinched by the thumb 1 and the index finger 2. FIG. 4C shows a state in which the object 6 is gripped with a Parallel-type gripping posture. The Parallel type is a gripping posture in which the object finger 6 is gripped with the index finger 2 to the little finger 5 arranged substantially parallel to the thumb 1 facing the object 6. FIG. 4D shows a situation where the object 6 is gripped in a circular gripping posture.

  Here, a finger or a palm that is opposed to the object 6 and contacts the object 6 is a virtual finger by combining a single finger or a plurality of fingers. In FIG. 3, for example, in the PoS-type row and P in the column of VF1, the palm P is the first virtual finger VF1, the index finger 2 to the little finger 5 are combined into the second virtual finger VF2, and the object 6 is Indicates gripping. The parts facing the object 6 include the belly of the finger, the side surface of the finger, and the palm P. In the PoS type, as shown in FIG. 3, the palm P is the facing part. In this case, the other facing portion with respect to the palm P is the belly of the finger. The bellies of the fingers face each other, and the side surfaces of the fingers face each other. FIG. 3 also shows the association between the large classification gripping posture and the small classification gripping posture.

  By configuring the hand gripping posture candidate generation unit 13 using such a database, a posture suitable for gripping the object is output as a hand gripping posture candidate simply by inputting the object to be gripped. can do.

  The posture generation unit 14 other than the hand, the gripping posture candidate generated by the hand gripping posture candidate generation unit 13 by inverse kinematics that handles the problem of obtaining the joint angle of each joint when the end position is given, The angle of each joint with another part on the specified body (hereinafter also referred to as “base point”) is obtained. For example, when the wrist and shoulder joints are fixed and the posture of the upper limb is generated, the angle of each joint is analytically obtained using the method described in Non-Patent Document 2, and Non-Patent Documents 1 and 3 are used. 4 is used to obtain the optimal value of the rotation angle of the elbow joint, that is, the turning angle, using, for example, the golden section method.

  The output unit 16 is a display that is connected to the posture evaluation unit 15 and displays the gripping posture generated by the posture evaluation unit 15 on the screen. Not only the display but also a printer or other storage device may be output in a file format. Further, the output unit 16 can be used, for example, by connecting to a robot and generating and controlling the posture of the robot.

  In the configuration example of FIGS. 1 and 2, a gripping posture candidate generation unit 13 including an input unit 12, a storage unit 21, and a gripping posture generation unit 22, a posture generation unit 14 other than a hand, a posture evaluation unit 15, and an output The units 16 are connected in a stand-alone manner with appropriate interfaces, but some or all of them may be arranged and connected on the network.

  Note that the present invention is not limited to the hardware configuration described above, and it is also possible to generate software-suitable gripping posture data based on a CPU command from a computer by a gripping posture generation program. The grip posture generation program is downloaded via a network or provided by a storage medium such as an optical disk and installed in a computer. Alternatively, the gripping posture generation program can be installed in a server on the network and can be executed by being accessed by a client computer.

[Operation of gripping posture generator]
As an example, the operation when the position of the shoulder is fixed and used as a base point to generate a gripping posture of the upper limb will be specifically described.

  As shown in FIG. 5, arbitrary local coordinates can be set by the hand gripping posture candidate generation unit 13 for the gripping posture candidate generated by the hand gripping posture candidate generation unit 13. After setting the local coordinates at a position as shown in FIG. 5A, it is possible to set different local coordinates by rotating the XZ plane about the Y axis from the state of FIG. 5A by an arbitrary angle. . For example, as shown in FIG. 5B, local coordinates rotated by 90 degrees around the Y axis from the state of FIG. 5A can be set. As shown in FIG. Furthermore, the local coordinates rotated by 90 degrees can be set. By arbitrarily setting the local coordinates in this way, it is possible to generate different gripping posture candidates only by changing the local coordinates with respect to the gripping posture candidates generated by the hand gripping posture candidate generation unit 13. it can. In this way, after generating hand gripping posture candidates, a large number of hand gripping posture candidates can be easily generated by changing the local coordinates.

  The angle of each joint is obtained by inverse kinematics using a skeleton model of the upper limb as shown in FIG. In the skeleton model of the upper limb, the posture generation unit 14 other than the hand uses the coordinates with Ow as the origin for the wrist joint, the coordinates with Oe as the origin for the elbow joint, and Os as the origin for the shoulder joint. Set the coordinates.

  An operation for generating a gripping posture using such a skeleton model of the upper limb will be described with reference to a flowchart shown in FIG.

  In step S <b> 1, data of a three-dimensional object to be grasped is input via the input unit 12.

  In step S2, the user inputs initial conditions. As an initial condition, a grip position, a grip direction, or both of the object can be designated. Furthermore, it is possible to specify an initial posture other than the hand or an arbitrary point on the body from the base point to the end, for example, the position of the wrist or the like. In addition, it is possible to prevent the user from designating the hand gripping position or gripping direction at all by automatically searching and determining the gripping posture by the hand gripping posture candidate generation unit 13.

  When the gripping position is designated, the gripping posture with respect to the designated position is generated as the first candidate. In this case, regarding the gripping direction, a plurality of candidates may be generated, or one or a specified number may be generated.

  When the gripping direction is designated, a gripping posture with respect to the designated direction is generated as the first candidate at the gripping position searched automatically. In this case, as many gripping posture candidates as the number of searched gripping positions may be generated, or one or a specified number may be generated.

  When the grip position and the grip direction are designated, the grip posture at the designated grip position and direction is generated as the first candidate.

  When an initial posture other than the hand is given, the position of the hand is first obtained by forward kinematics. When all the joint angles are not given, a plurality of candidates may be generated, or one or a specified number may be generated. Then, the gripping posture closest to the obtained hand position may be generated by the hand gripping posture candidate generation unit 13 and set as the first candidate.

  If a position on the body from the base point to the end, for example, a position such as a wrist, is given, a posture other than the hand for that position is generated by inverse kinematics in the same manner as in step S6 described later, The same processing as described above is performed as the initial posture other than the hand.

  When no initial conditions such as a gripping position are specified, the hand gripping posture candidate generation unit 13 automatically searches for a gripping position and generates a gripping posture candidate. In this case, as many gripping posture candidates as found may be generated, or one or a specified number may be generated.

  In step S3, the hand gripping posture candidate generation unit 13 generates local coordinates as shown in FIG. 5, and sets one or a plurality of local coordinates.

  In step S4, the hand holding posture candidate generation unit 13 generates a hand holding posture candidate. The operation of the hand gripping posture candidate generation unit 13 will be described later in order to prevent the description of the operation from becoming unnecessarily complicated.

  In step S <b> 5, the shoulder is designated as the base point and input via the input unit 12. As described above, the posture generation unit 14 other than the hand may input a shoulder as a base point in advance.

  In step S6, the posture generation unit 14 other than the hand obtains the angle of the elbow joint Oe when the wrist joint Ow and the shoulder joint Os are fixed by an analytical method, and obtains the position of the elbow joint using an evaluation function.

  As shown in FIG. 6, the shoulder joint Os has three degrees of freedom of swing rotation (vertical movement around the X axis, longitudinal movement around the Y axis) and twist rotation around the Z axis by a rotation axis perpendicular to the Z axis. Have. The elbow joint Oe has two degrees of freedom: rotation around the Y axis (back and forth movement) and twist rotation around the Z axis. The wrist joint Ow has two degrees of freedom of swing rotation (vertical movement around the X axis, forward and backward movement around the Y axis) by a rotation axis perpendicular to the Z axis. Therefore, the total degree of freedom of the joint of the upper limb is 7, and there is one redundant degree of freedom for 6 degrees of freedom of the hand position. Then, when the position of the wrist and shoulder joints is fixed, the joint position of the elbow can be rotated to draw a circle, and the joint angle of the upper limb can be obtained analytically by giving the turning angle φ. (For example, refer nonpatent literature 2). The following function is defined as a function for evaluating the naturalness of the posture of the upper limb, and the turning angle φ at which the evaluation function is minimized is obtained by the golden section method.

  The evaluation function fdisplacement is defined as the sum of weighted evaluation functions for each joint as the distance from the reference joint angle at rest becomes larger, as in the following equation (1).

Also, an evaluation function fpotential based on the difference in potential potential energy of each link is defined as in the following equation (2). The position potential energy is calculated by using the height difference Δh _l of each part l in the posture in which the arm is hung directly below.

Here, m _l is the mass of that portion, g is the gravitational acceleration.

Furthermore, an evaluation function fdiscomfort is defined that indicates that each joint rapidly increases as it approaches the range of motion. For example, as shown in FIG. 6, in the shoulder joint Os, the rotation around the Y axis (back and forth movement) can be largely moved in the front direction, but the movement in the rear direction is limited. Such an operation is defined as the following equations (3) to (6). _QU i limit range of motion, the lower limit, respectively, and QL _i.

Here, G = 0.01 and n = 10. Δv _i ^norm , QU _i , and QL _i are defined by the following equations (4) to (6).

Furthermore, in addition to the above-described evaluation function, an evaluation function fpdf of the following equation (7) may be introduced, and the sum of all evaluation functions may be used as a comprehensive evaluation result. The evaluation function fpdf of Expression (7) uses a technique for estimating the joint range of motion by obtaining the probability density distribution from the actually measured motion data (see, for example, Non-Patent Documents 3 and 4). The reciprocal of the probability density distribution pdf _j of each joint j estimated from the motion data is used as an evaluation function representing the naturalness of the posture.

  In FIG. 8, the result of having evaluated the attitude | position of the upper limb using the evaluation function mentioned above is shown. 8A to 8C, the horizontal axis represents the turning angle of the elbow joint Oe, the vertical axis represents the return value of the evaluation function, and the iterative process by the golden section method is shown in the graph. Is plotted. As shown in FIG. 8A, when the gripping posture is evaluated using the evaluation functions of Expressions (1) to (3), a favorable evaluation value (small) is obtained at a relatively narrow turning angle. Evaluation value) was obtained. As shown in FIG. 8B, when the gripping posture is evaluated using the evaluation function of Expression (7), a small evaluation value is output in a wide range, so that the position of the elbow joint is more It is in a high position. As shown in FIG. 8C, the evaluation value obtained by the evaluation function obtained by adding all of the expressions (1) to (3) and (7) can generate a more natural gripping posture.

  In step S7, the posture evaluation unit 15 evaluates hand gripping posture candidates. Specifically, the collision determination with the object to be grasped and / or the upper limb obtained in step S6 is performed, and when the hand is buried in the object and / or the upper limb, it becomes infinite. It is preferable to perform the evaluation using a binary evaluation function that is 1.

  In step S8, the product of the evaluation value for the posture other than the hand generated in step S6 by the posture evaluation unit 15 and the evaluation value of the hand gripping posture by the collision determination in step S7 is a comprehensive gripping posture evaluation value. Is preferable. By obtaining such an evaluation value, an inappropriate posture in which a hand is buried in an object or the like can be excluded.

  In step S9, the posture evaluation unit 15 obtains a grip position where the comprehensive evaluation value is minimized by using the simulated annealing method with respect to the comprehensive grip evaluation value obtained in step S8. By executing such an operation, it is possible to prevent the posture obtained by the posture evaluation unit 15 from falling into a local solution and outputting an unnatural posture.

  In step S10, when the posture evaluation unit 15 repeatedly generates a gripping posture, the process returns to step S3 to execute the above-described flow for different local coordinates to generate the gripping posture. Then, it is possible to compare the obtained comprehensive gripping posture evaluations with respect to these different local coordinates and select an optimal gripping posture.

  9A to 9D show the gripping postures generated by the above flow. In the same manner as shown in FIG. 5, by rotating the local coordinates for the hand gripping posture by 90 degrees about the Y axis, the gripping postures for four different local coordinates are generated. The Preferably, the smallest of the comprehensive evaluation values of these gripping postures is the most natural gripping posture, and the posture is as shown in FIG.

  As shown in FIG. 10, the local coordinates may be changed not only by rotation with respect to the Y axis but also by movement in the Y axis direction. As shown in FIG. 10 (A), local coordinates are set at the lower grip position limit of the object 6 to be gripped, and sequentially on the Y axis as shown in FIGS. 10 (B) to (D). As shown in FIG. 10E, the local coordinates can be set up to the upper limit of the position where the object can be gripped. In addition to movement in the Y-axis direction, local coordinates may be set by rotating around the Y-axis.

  In the flowchart of FIG. 7, a step of generating local coordinates is executed every time a hand gripping posture candidate is generated. For example, as another embodiment, a hand gripping posture candidate generation unit 13 is used. To generate gripping posture candidates for a plurality of gripping positions in advance, select an optimal gripping posture when the local coordinates are changed for each gripping posture candidate, and then return to step S3 to return to other gripping positions. The optimum gripping posture for the gripping posture candidate may be obtained.

  Note that the above-described evaluation function is an example, and a part of the above-described evaluation functions may be used in combination, and other evaluation functions other than those described above may be used.

  As a result of the evaluation using the above-described evaluation function, the most comprehensive evaluation function becomes smaller in the case of the gripping posture in FIG. 10C, and a result that the gripping posture is natural is output.

  As shown in FIG. 11, according to the gripping posture generation apparatus of the present invention, a natural gripping posture can be easily and quickly generated for gripping target objects having various three-dimensional shapes.

  FIG. 11A shows an example of the optimum gripping posture in a state where the bottom of the triangular cone-shaped object is gripped so as to be supported by the circular hand gripping posture. FIG. 11B shows an example of an optimum gripping posture in a state where the upper and lower surfaces of a thin cylinder (thick disc) are pinched and gripped with a gripping posture of a Pinch type hand. FIG. 11C shows an example of the optimum gripping posture in a state where the gripping position is shifted to the center of the object in the case of the same object as FIG. 11B. FIG. 11D shows an example of the optimum gripping posture in a state where a cubic object is gripped and gripped from above with a circular hand gripping posture. FIG. 11E shows an example of the optimum gripping posture in a state where a cylindrical object is gripped and gripped with a parallel hand gripping posture. FIG. 11F shows an example of the optimum gripping posture in a state in which the same triangular cone-shaped object as in FIG. 11A is gripped by gripping the top portion of the object with the gripping posture of the Parallel type hand.

  For example, as shown in FIGS. 11 (A) and 11 (F), for the same object to be gripped, the gripping position can be manually input via the input unit 12, but the hand gripping posture candidates The generating unit 13 can automatically search for a gripping position and set different local coordinates.

  As described above, it is possible to generate appropriate hand gripping posture candidates for three-dimensional objects having various shapes, and generate an optimal gripping posture of the entire upper limb.

  In the above description, the other part on the body other than the hand has been described as the shoulder joint, but the base point is not limited to the shoulder joint and may be another part. For example, the position of the waist may be used as the base point, or the contact point between the support leg and the ground may be used as the base point. When the position of the waist is the base point, the posture of the upper body is the target of posture generation, and when the contact point between the support leg and the ground is the base point, the posture of the whole body is the target of posture generation.

[Operation of hand gripping posture candidate generation unit]
Since the gripping posture generation device 10 of the present invention can interactively generate various hand gripping postures by the hand gripping posture candidate generation unit 13, it generates a large number of gripping posture candidates and obtains an optimal gripping posture. Can be generated. The operation of the hand gripping posture candidate generation unit 13 will be described with reference to a flowchart shown in FIG.

  As the information input from the input unit 12, data of the object to be gripped that is three-dimensionally modeled is input to the hand gripping posture candidate generation unit 13. With respect to the gripping posture, it is possible to select and input gripping posture data from among the 14 classifications of the small classification gripping posture data as shown in FIG. 2, or the object without inputting the gripping posture. It is also possible to output all of the appropriate gripping postures for the small classification gripping posture that can grip the. In addition, regarding the gripping position, the user can specify and input the gripping portion of the object to be gripped. If the user does not specify the gripping position, each of the plurality of randomly extracted positions is appropriate. A simple gripping posture can also be generated.

  In addition, when inputting the data of the small classification gripping posture, the classification name of the small classification gripping posture may be specified, but by placing thumbnails of the data of the small classification gripping posture on the input screen in advance The user may be able to select it.

  In step S <b> 13, the gripping posture generation unit 22 that has received data from the input unit 12 extracts shape feature data of the object by tracing the object surface. The shape feature amount of the object is a parameter for determining whether or not the object can be grasped depending on the grasping direction. "0" when the finger does not turn to the object to the extent that it can support the object, "1" when the finger can reach the surfaces on both sides and support the object, "2" when the finger reaches the back surface ". More specifically, it is “0” if the change in angle in the normal direction at each end point is less than π, “1” if it is π or more and less than 2π, and “2” if it is 2π or more. For example, in a flat object larger than the hand, the plane direction of the flat plate is expressed as 0-0 because the finger cannot reach in any direction. For example, in a flat object, for gripping from the side of the flat plate, the finger reaches the thickness direction of the flat plate, that is, the left and right surfaces, but the finger reaches the height direction of the flat plate, that is, the upper and lower surfaces. The absence is represented as 1-0. For example, when a columnar object is gripped from the side, the finger turns to the back in the circumferential direction of the object, but the height of the object is high, and the finger does not turn in this direction. For example, when a columnar object is gripped from the end, the finger reaches any side surface in the height direction, which is represented as 1-1. In the columnar object, the finger turns to the back in the circumferential direction of the column, and the finger also reaches the surface in the height direction of the column. For example, in a spherical object, since the finger reaches the back in any direction, it is expressed as 2-2.

  Detect whether the virtual finger of the large classification gripping posture to which the small classification gripping posture can support 1 or 2, that is, the side of the object, or can turn the finger to the back of the object .

  When the grasping posture generation unit 22 determines that the object can be grasped from the trace result of the object surface described above, the grasping posture generation unit 22 sets local coordinates at a position where the object can be grasped.

  As shown in FIG. 13A, when the large classification gripping posture is a Pinch type, the distance between the opposing surfaces in the x-axis direction is the distance between the contact surfaces of the first virtual finger VF1 and the second virtual finger VF2. This is used as shape feature data. As shown in FIG. 13B, when the large classification gripping posture is a parallel type, a plurality of fingers are used as the second virtual finger, and thus the finger thickness is shifted by the thickness of the finger in the y-axis direction. The distance between the opposing surfaces at the position is defined as the distance between the contact surfaces, and the data of the shape feature amount. In addition, like the finger on the lowermost side of the second virtual finger, when the object is outside the object and is not in contact with the object, it is treated as a finger that does not participate in gripping. For example, the distance between contact surfaces is set to zero. As shown in FIG. 13C, when the large classification gripping posture is a circular type, there is a third virtual finger VF3 in addition to the first and second virtual fingers VF1 and VF2 facing each other. The third virtual finger VF3 is treated as a y coordinate axis orthogonal to the x coordinate axis composed of the first and second virtual fingers VF1 and VF2. As described above, by measuring the distance between the contact surfaces of the virtual fingers, the shape feature data of the object 6 to be grasped is extracted. As described above, by associating the shape feature quantity with the large classification gripping posture, it is possible to quickly determine whether or not the given gripping target object can be gripped. Here, when the gripping posture generation unit 22 generates a gripping posture for gripping an object having a physically separated portion such as scissors, each of the separated portions of the object to be gripped is to be generated. Then, the shape feature amount is extracted, and a gripping posture is generated. Therefore, in order to generate a gripping posture in which a part of a handle that is physically separated, such as scissors, is straddled, the object to be gripped is placed in a three-dimensional space including the part of the object that is distant. It is necessary to have a convex hull shape that is the smallest convex figure including the arranged points. Based on the shape of the object 6 input to the gripping posture generation unit 22, a convex hull of the object 6 is generated using, for example, another program that generates a convex hull, and the convex hull is gripped. Input to the generation unit 22. Based on the input shape of the convex hull, the gripping posture generation unit 22 extracts a shape feature amount and generates an appropriate gripping posture.

  In step S14, the database 20 is accessed using the extracted shape feature data as a query, and the gripping posture generation unit 22 searches the corresponding indexed small classification gripping posture data.

  In step S15, when the gripping posture generation unit 22 finds the corresponding indexed small classification gripping posture data in the database 20, the processing proceeds to the next step S17.

  If not found in the database 20, in step S <b> 16, the gripping posture generation unit 22 generates and interpolates data on the distance between the fingers and the joint angle of each finger and writes the data in the database 20. When the small classification gripping posture is a Parallel type or a Circular type, since a plurality of fingers are related to one virtual finger, it is rare that a corresponding one is found only with the gripping posture data existing in the database 20. . Therefore, the database 20 also indexes the angle of each finger with respect to the shape feature data for each indexed small classification gripping posture, using the object width value, that is, the distance between the contact surfaces as the shape feature data. Therefore, the joint angle of an unknown finger can be calculated by performing linear interpolation calculation using a plurality of existing data. Therefore, joint angle data of each finger can be obtained by performing linear interpolation calculation for each shape feature amount data corresponding to each finger. The data of the joint angle of each finger is calculated by linear interpolation independently. As a result, even if the thickness of the gripping position of the object to be gripped is different, the data of the finger joint angle can be interpolated and generated by the finger contact position, and a natural gripping posture is generated. be able to. It should be noted that the newly generated joint angle data of each finger by the linear interpolation calculation is not limited to being written on the database 20, but may be stored in another temporary storage memory such as a cache memory. It may be deleted immediately after being used for searching.

  In step S <b> 17, the gripping posture generation unit 22 aligns the hand and the object based on the coordinate axes of the data of the object to be gripped and the coordinates of the representative point data on the finger of the searched small classification gripping posture.

  In step S <b> 18, the gripping posture generation unit 22 optimizes the arrangement of the aligned hands and fingers by an ICP (Iterative Closest Point) algorithm, which is one of the optimization algorithms based on the distance between the point groups. Here, in the ICP algorithm, the point-to-point association is performed between the point groups belonging to the two coordinate systems of the object and the gripping posture, and the distance between the pair of associated points is minimized. The optimum arrangement is obtained by repeating the process of obtaining the affine transformation between coordinate systems. The algorithm for obtaining the optimal hand placement is not limited to the ICP algorithm, and other three-dimensional shape alignment algorithms can be used.

  The representative point and the contact point group are defined as follows.

  FIGS. 14A and 15A are views showing the state of the hand holding the object 6 in order to explain the representative point 7 and the contact point 8. 14A shows an example of a Lat type (Pinch type holding posture for grasping a side surface), and FIG. 15A shows an example of a PE type (Parallel type holding posture for grasping a parallel extension). As shown in FIG. 14B, for the gripping posture having two virtual fingers, one representative point 7 is set on the first virtual finger VF1 and one on the second virtual finger VF2. . As shown in FIG. 15B, for the gripping posture having three virtual fingers, one representative point 7 is set for each virtual finger VF1, VF2, and VF3. From the data of the positions of these representative points, it is possible to uniquely determine 6-dimensional data of the hand position and direction in the three-dimensional space.

  Further, as shown in FIGS. 14C and 15C, the data of the contact point 8 is set for each finger for each data of the small classification holding posture. By setting the data of the contact point 8 for each finger, it can be used for optimizing the alignment with the object to be grasped.

  The data of the representative point 7 is used to set the coordinates as the initial value of the gripping position of the gripping posture generated by the gripping posture generation unit 22 and determine the hand arrangement together with the coordinates generated on the object 6. Used. The data of the contact point 8 is used to set the determined hand arrangement to an optimum position by using an arbitrary algorithm.

  In addition, the position where the representative point 7 and the contact point 8 described above are arranged is an example, and needless to say, it can be arranged at an arbitrary position.

  A contact point group generated in the vicinity of the gripping position is used as a point group on the surface of the object for the ICP algorithm.

  In step S18, when the calculation for optimizing the arrangement is repeatedly performed, depending on the gripping posture, a part of the fingers arranged on the object 6 may be generated or a part of the fingers may be generated from the object 6. May leave. Therefore, in step S18, the data of the repeated arrangement state is stored every time. Then, while performing a collision determination between the object 6 and each finger, for a buried finger, search for a state immediately before being buried, and for a finger that is not in contact, bend until the object 6 comes into contact. The final optimal placement can be determined.

  Thus, according to the hand gripping posture candidate generation unit 13, it is not necessary to calculate the joint angle of the hand using inverse kinematics, and the hand gripping posture is separated from other parts. Candidates can be generated.

  Further, according to such a gripping posture candidate generation unit 13 of the hand, the gripping posture generation device 10 can specify an appropriate hand only by specifying the gripping position and / or gripping direction in addition to the data of the object to be gripped. A gripping posture candidate can be generated. Furthermore, it is possible to automatically search for a gripping position or the like and generate a plurality of hand gripping posture generation candidates without specifying the gripping position or the like. Postures other than the hand can be generated for each of the plurality of hand gripping posture candidates.

  Therefore, by performing a comprehensive posture evaluation in combination with the posture of a part other than the hand, posture generation including a hand gripping posture can be easily executed. In addition, by extracting the shape feature amount of the gripping target object based on the large classification gripping posture, it is possible to more quickly determine whether or not the gripping target object can be gripped. Makes it possible to enhance sex.

  The gripping posture generation device, the gripping posture generation method, and the gripping posture generation program described above are for explaining specific examples, and are not limited to the above-described embodiments, and depart from the gist of the present invention. Needless to say, various modifications can be made without departing from the scope.

  1 thumb, 2nd finger, 3 middle finger, 4 ring finger, 5 little finger, 6 object, 7 representative point, 8 contact point, 10 gripping posture generation device, 12 input unit, 13 hand gripping posture candidate generation unit, other than 14 hands Posture generation unit, 15 Posture evaluation unit, 16 Output unit, 20 Database, 21 Storage unit, 22 Grasping posture generation unit, P Palm

Claims (12)

Hand gripping posture candidate generation based on a database containing data on hand and finger postures suitable for gripping the object by inputting the data of the object to be gripped. And
The posture generation other than the hand for inputting the data of the other position on the body other than the hand and calculating the angle of the joint between the other position and the position of the hand generated by the hand holding posture candidate generation unit. And
A posture evaluation unit that evaluates posture data other than the hand including the angle of the joint generated by the posture generation unit other than the hand based on a predetermined evaluation function;
The hand gripping posture candidate generation unit generates data of one or more hand gripping posture candidates,
The posture generation unit other than the hand calculates an angle of the joint between the generated hand gripping posture candidate data and the other position, and the posture evaluation unit determines the joint A device for generating a gripping posture of a body part involved in gripping of a hand and a hand other than the hand, characterized in that data of a posture other than the hand having an optimum angle is generated from the angles of the hand. The hand gripping posture candidate generation unit
A storage unit for storing the database;
A gripping posture generation unit that generates gripping posture data in a state of gripping the object based on the input shape data of the object and the database;
The database is
A hierarchy including at least the data of the small classification gripping posture having data relating to the posture of the hand and the finger and the data associated with the data of the small classification gripping posture and acquired in a state of gripping an object having a basic shape having a different width, A hierarchy including data of index small classification gripping posture indexed by width,
The index small classification gripping posture data is associated with each index angle of each indexed finger, and the small classification gripping posture data includes representative point data and contact point group data set on the finger. Data is associated,
The gripping posture generation unit
When the surface of the input object or the convex hull surface of the object generated based on the data of the input object is traced and the input object is gripped, the index small classification grip Detecting whether or not a finger having a posture can be gripped, and searching for data of each joint angle of each finger indexed and associated with the data of the index small classification gripping posture;
When the data of each finger joint angle does not exist in the database, the joint angle data of each finger is newly generated by performing interpolation calculation of the finger joint angle data existing in the database. And
When the data of the joint angle of each finger that can be gripped is found, the coordinates are set based on the data of the representative point, and the arrangement of the hand and fingers and the object are aligned,
2. The gripping posture generation apparatus according to claim 1, wherein an optimum arrangement of the hand and the finger is determined according to data of a contact point group for each finger, with the position obtained by the alignment as an initial value.   The gripping posture generation apparatus according to claim 2, wherein the posture evaluation unit evaluates the hand gripping posture candidates generated by the hand gripping posture candidate generation unit based on a predetermined evaluation function.   4. The gripping posture generation apparatus according to claim 3, wherein the posture evaluation unit determines an overall gripping posture based on an evaluation result of the hand gripping posture candidate and an evaluation result of a posture other than the hand. .   The posture evaluation unit obtains the entire gripping posture based on the evaluation result of the hand gripping posture candidate for each of the generated hand gripping posture candidates and the evaluation result of the posture other than the hand, and calculates all the obtained gripping postures. 5. The gripping posture generating apparatus according to claim 4, wherein an optimal overall gripping posture is determined from the overall gripping posture. The hierarchy including the data of the small classification gripping posture is in a lower hierarchy of the hierarchy including the data of the large classification gripping posture,
The data of the large classification gripping posture is associated with at least one finger or palm of the small classification gripping posture, and has virtual fingers that face the object while facing the object,
Based on the distance between the surfaces of the object in contact with the virtual finger, the shape feature value data of the object is extracted,
6. The gripping posture generation apparatus according to claim 2, wherein the index small-size gripping posture data is searched based on the shape feature amount data of the object.   The grip posture generation apparatus according to any one of claims 2 to 6, wherein the data of the joint angle of each finger is determined independently for each finger. Inputting data of an object to be gripped and generating hand gripping posture candidate data based on a database including data on hand and finger postures suitable for gripping the object;
Inputting data of another position on the body other than the hand to determine an angle of a joint between the other position and the position of the hand of the generated gripping hand;
Evaluating a posture other than the generated hand based on a predetermined evaluation function,
The step of generating the hand gripping posture candidate data generates one or more hand gripping posture candidate data,
The step of determining the angle of the joint determines the angle of the joint between the hand position for each of the generated hand gripping posture candidate data and the other position,
The step of evaluating the posture other than the hand generates posture data other than the hand having an optimum angle from the angles of the joints, and is characterized in that A gripping posture generation method. The step of generating the hand gripping posture candidate data includes:
Generating a gripping posture data generated in a state of gripping the object based on the input shape data of the object and the database;
The database is
A hierarchy including at least the data of the small classification gripping posture having data relating to the posture of the hand and the finger and the data associated with the data of the small classification gripping posture and acquired in a state of gripping an object having a basic shape having a different width, A hierarchy including data of index small classification gripping posture indexed by width,
The index small classification gripping posture data is associated with each index angle of each indexed finger, and the small classification gripping posture data includes representative point data and contact point group data set on the finger. Data is associated,
The step of generating the grip posture data includes
When the surface of the input object or the convex hull surface of the object generated based on the data of the input object is traced and the input object is gripped, the index small classification grip Detecting whether or not a finger having a posture can be gripped, and searching for data of each joint angle of each index indexed and associated with the data of the index small classification gripping posture;
When the data of each finger joint angle does not exist in the database, the joint angle data of each finger is newly generated by performing interpolation calculation of the finger joint angle data existing in the database. And steps to
When the data of the joint angle of each finger that can be gripped is found, the coordinates are set based on the data of the representative point, and the arrangement of the hand and fingers and the object are aligned,
9. The method for generating a gripping posture according to claim 8, further comprising: determining an optimal arrangement of the hand and the finger according to data of a contact point group for each finger, with the position obtained by the alignment as an initial value. .   The gripping posture generation method according to claim 9, further comprising a step of evaluating the generated hand gripping posture candidate based on a predetermined evaluation function.   The gripping posture generation method according to claim 10, further comprising a step of obtaining an overall gripping posture based on the evaluation result of the generated gripping posture candidate of the hand and the evaluation result of the posture other than the generated hand.   A gripping posture generation program for causing a computer to execute the method according to any one of claims 8 to 11.