CN116442246B - Gesture balance control method applied to robot - Google Patents
Gesture balance control method applied to robot Download PDFInfo
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- CN116442246B CN116442246B CN202310699474.9A CN202310699474A CN116442246B CN 116442246 B CN116442246 B CN 116442246B CN 202310699474 A CN202310699474 A CN 202310699474A CN 116442246 B CN116442246 B CN 116442246B
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- 230000035772 mutation Effects 0.000 claims description 108
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- 238000012216 screening Methods 0.000 claims description 5
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- 230000036544 posture Effects 0.000 description 20
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- 210000001503 joint Anatomy 0.000 description 11
- 210000003414 extremity Anatomy 0.000 description 10
- 238000004088 simulation Methods 0.000 description 6
- 210000003423 ankle Anatomy 0.000 description 5
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention provides a gesture balance control method applied to a robot, which comprises the following steps: s1: determining local balance control parameters of each frame of static dynamic model in a real-time motion model of the robot based on local balance requirements of the robot and real-time attitude data of all joints acquired in the action process; s2: summarizing and blurring the local balance control parameters of all static dynamic models in the real-time motion model of the robot to obtain continuous balance control parameters of each frame of static dynamic model; s3: correcting the continuous balance control parameters based on control continuity prediction results of all the continuous balance control parameters to obtain final balance control parameters; s4: controlling the robot based on the final balance control parameters to obtain an attitude balance control result; the method is used for improving the continuity of the gesture balance control of the robot under the condition of ensuring the control precision.
Description
Technical Field
The invention relates to the technical field of robot control, in particular to a gesture balance control method applied to a robot.
Background
Currently, bipedal or multipedal robots are widely used for unknown space exploration or movement in complex terrain because they can move within complex terrain spaces. However, the joints of the biped or multi-legged robot are complex, and the control variables are diversified and have nonlinear relations, so that the gesture balance control is difficult. Many scholars propose to optimize the gesture balance control output of the robot based on fuzzy reinforcement learning, aiming at the problems of low control precision, discrete output of a controller and the like in the control process of the robot, the fuzzy theory is adopted to generalize the action space, the control precision is improved, and the control output is continuous.
However, the above method needs to design a state space and a return function based on the robot characteristics, so as to design a control algorithm flow, and iteratively update the control algorithm flow until the algorithm converges, so that the control design process is complex.
Therefore, the invention provides a gesture balance control method applied to a robot.
Disclosure of Invention
The invention provides a gesture balance control method applied to a robot, which is used for carrying out frame splitting on a motion model of the robot in the moving process, carrying out local balance control calculation based on a static dynamic model after frame splitting, carrying out summarization and fuzzy processing on all determined local balance control parameters in time sequence, improving the continuity of gesture balance control of the robot, and realizing further improvement on the continuity of gesture balance control of the robot through a determined control continuity prediction result.
The invention provides a gesture balance control method applied to a robot, which comprises the following steps:
s1: determining local balance control parameters of each frame of static dynamic model in a real-time motion model of the robot based on local balance requirements of the robot and real-time attitude data of all joints acquired in the action process;
s2: summarizing and blurring the local balance control parameters of all static dynamic models in the real-time motion model of the robot to obtain continuous balance control parameters of each frame of static dynamic model;
s3: correcting the continuous balance control parameters based on control continuity prediction results of all the continuous balance control parameters to obtain final balance control parameters;
s4: and controlling the robot based on the final balance control parameters to obtain an attitude balance control result.
Preferably, the method for controlling the gesture balance applied to the robot comprises the following steps of: based on the local balance requirement of the robot and the real-time attitude data of all joints acquired in the action process, determining the local balance control parameters of each frame of static dynamic model in the real-time motion model of the robot, wherein the method comprises the following steps:
s101: based on the three-dimensional limb model of the robot and the real-time posture data of all joints acquired in the action process, building a real-time motion model of the robot;
S102: extracting a local dynamic model from each frame of static dynamic model of the real-time motion model based on the local balance requirement;
s103: and determining local balance control parameters of the static dynamic model of the corresponding frame based on the local dynamic model and the local balance controller corresponding to the local balance requirement.
Preferably, the method for controlling the gesture balance applied to the robot comprises the following steps of: summarizing and blurring the local balance control parameters of all static dynamic models in the real-time motion model of the robot to obtain continuous balance control parameters of each frame of static dynamic model, wherein the method comprises the following steps:
s201: summarizing and adding the same type of local balance control parameter values in all local balance control parameters of the static dynamics model to obtain personalized values of each balance control parameter in the static dynamics model;
s202: sequencing the personalized values of each balance control parameter in all the static dynamics models according to the time sequence of all the static dynamics models to obtain a control value sequence of each balance control parameter;
s203: performing fuzzy smoothing processing on the control value sequence to obtain a smooth control value sequence;
S204: taking the smoothed personalized value corresponding to each frame of static dynamics model in the smoothing control value sequence as a continuous balance control parameter of the corresponding balance control parameter in the corresponding static dynamics model;
the personalized value is represented by a vector, the direction of the vector represents the control direction of the corresponding balance control parameter, and the value of the vector represents the output value of the corresponding balance control parameter.
Preferably, the method for controlling the posture balance applied to the robot, S203: performing fuzzy smoothing processing on the control value sequence to obtain a smooth control value sequence, wherein the fuzzy smoothing processing comprises the following steps:
the vectors corresponding to the personalized values contained in the control value sequence are connected end to end according to the sequence, and a control value characterization line is obtained;
calculating a mutation vector modulus threshold value of the corresponding balance control parameter based on the maximum angle adjusting range and the maximum numerical adjusting range of the corresponding balance control parameter;
screening out points to be smoothed in a control value characterization line based on the mutation vector modulus threshold;
and carrying out fuzzy smoothing treatment on all the points to be smoothed to obtain a smoothing control numerical sequence.
Preferably, the method for controlling the gesture balance applied to the robot, based on the abrupt vector modulus threshold, screens out the point to be smoothed in the control value characterization line, includes:
The length between the starting point of the former personalized value in the control value characterization line and the ending point of the latter personalized value in the control value characterization line in two adjacent personalized values in the control value sequence is used for obtaining a mutation vector module;
and taking the ratio of the mutation vector modulus to the mutation vector modulus threshold value as a mutation duty ratio, and taking the connection point of two personalized values of which the mutation duty ratio exceeds the mutation duty ratio threshold value in the control value characterization line as a point to be smoothed.
Preferably, the method for controlling the gesture balance applied to the robot performs fuzzy smoothing on all points to be smoothed to obtain a smoothing control value sequence, including:
calculating a first mutation duty ratio maximum adjustable value based on a mutation duty ratio between a personalized value taking a point to be smoothed as an end point and a corresponding previous adjacent personalized value, and calculating a first mutation vector module maximum adjustable value based on the first mutation duty ratio maximum adjustable value and a mutation vector module threshold value;
calculating a second mutation duty ratio maximum adjustable value based on the mutation duty ratio between the personalized value taking the point to be smoothed as a starting point and the corresponding next adjacent personalized value, and calculating a second mutation vector module maximum adjustable value based on the second mutation duty ratio maximum adjustable value and a mutation vector module threshold value;
Determining a point of the point to be smoothed after fuzzy smoothing processing in the control value characterization line based on the first mutation vector mode maximum adjustable value and the second mutation vector mode maximum adjustable value;
and updating the corresponding two personalized values based on the points of all the points to be smoothed after the fuzzy smoothing processing in the control value characterization line, thereby obtaining a smooth control value sequence.
Preferably, the method for controlling the gesture balance applied to the robot determines a point to be smoothed after the fuzzy smoothing processing of the point to be smoothed in the control value characterization line based on the maximum adjustable value of the first mutation vector module and the maximum adjustable value of the second mutation vector module, and includes:
taking a starting point of a personalized numerical value taking a point to be smoothed as an end point in a control numerical value characterization line as a circle center, taking a maximum adjustable value of a first mutation vector module as a radius, and determining a first circle region;
taking the end point of the personalized numerical value taking the to-be-smoothed zone you as the starting point in the control numerical value characterization line as the circle center, taking the maximum adjustable value of the second abrupt change vector mode as the radius, and determining a second circle area;
and taking the point with the minimum distance from the corresponding point to be smoothed in the superposition area of the first circle area and the second circle area as the point after the point to be smoothed is subjected to fuzzy smoothing processing in the control value characterization line.
Preferably, the method for controlling the gesture balance applied to the robot comprises the following steps of: correcting the continuous balance control parameters based on control continuity prediction results of all the continuous balance control parameters to obtain final balance control parameters, including:
s301: constructing a balance pre-control braking state model based on the continuous balance control parameters and all static dynamic models;
s302: simulating a pre-control gravity center point movement track based on a balance pre-control braking state model;
s303: obtaining a control continuity prediction result based on a pre-control center-of-gravity point movement track;
s304: and correcting the continuous balance control parameters based on the continuous prediction result to obtain the final balance control parameters of the static dynamic model of each frame.
Preferably, the method for controlling the posture balance applied to the robot, S303: obtaining a control continuity prediction result based on a pre-control center-of-gravity point movement track, comprising:
taking the projection of the pre-controlled gravity point movement track in a preset coordinate transverse plane as a gravity plane movement track;
and taking the matching degree of the gravity plane moving track and the preset gravity plane moving track as a control continuity prediction result.
Preferably, the method for controlling the gesture balance applied to the robot is S304: correcting the continuous balance control parameters based on the continuous prediction result to obtain final balance control parameters of each frame static dynamics model, wherein the method comprises the following steps:
when the matching degree in the continuity prediction result exceeds a matching degree threshold value, carrying out alignment period division on the gravity plane moving track and the preset gravity plane moving track, and calculating the local matching degree between the partial gravity plane moving track and the partial preset gravity plane moving track obtained after the period division;
and correcting the continuous balance control parameters based on the partial gravity plane movement track of which the partial matching degree exceeds the matching degree threshold value, and obtaining the final balance control parameters of the corresponding frame static dynamics model.
The beneficial effects of the invention are as follows: the method comprises the steps of carrying out frame splitting on a motion model of a robot in a moving process, carrying out local balance control calculation based on a static dynamic model after frame splitting, summarizing all determined local balance control parameters and carrying out fuzzy processing on time sequence, improving the continuity of robot gesture balance control, and realizing further improvement on the continuity of robot gesture balance control through a determined control continuity prediction result, wherein the gesture balance control process is simplified compared with the prior art.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a flowchart of a gesture balance control method applied to a robot in an embodiment of the present invention;
FIG. 2 is a flowchart of another method for controlling the gesture balance of a robot according to an embodiment of the present invention;
FIG. 3 is a flowchart of another method for controlling the gesture balance of a robot according to an embodiment of the present invention;
FIG. 4 is a flowchart of yet another method for controlling the gesture balance of a robot according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a simulation process of pre-controlling a movement track of a center of gravity point in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the invention provides a gesture balance control method applied to a robot, referring to fig. 1, comprising the following steps:
s1: determining local balance control parameters of each frame of static dynamic model in a real-time motion model of the robot based on local balance requirements of the robot and real-time attitude data of all joints acquired in the action process;
s2: summarizing and blurring the local balance control parameters of all static dynamic models in the real-time motion model of the robot to obtain continuous balance control parameters of each frame of static dynamic model;
s3: correcting the continuous balance control parameters based on control continuity prediction results of all the continuous balance control parameters to obtain final balance control parameters;
s4: and controlling the robot based on the final balance control parameters to obtain an attitude balance control result.
In this embodiment, the local balancing requirements are requirements for keeping balance of a certain local part of the robot in the moving process, and each local balancing requirement corresponds to a local balancing controller for solving the corresponding local balancing requirement; for example: the balance of the robot pelvis joint in the horizontal direction in the dynamic motion is kept, and the balance is realized through a ZMP compensator; or counteracting the vibration caused by the collision of the robot foot during landing, and realizing the landing by a soft land controller; and further or compensating for roll vibrations of the center position of the pelvic joint relative to the pitch/roll angle of the torso, by a torso pitch/roll angle controller, and the like.
In this embodiment, the robot may be a bipedal robot or a multipedal robot.
In this embodiment, the motion process is the motion process of the joints when the robot moves in the complex terrain.
In this embodiment, the joints are, for example, pelvic joints, ankle joints, leg intermediate joints, and the like of the robot.
In this embodiment, the real-time posture data is posture data of joint movement, for example: data representing the relative postures of the limbs at both ends of the joint, such as pitch angle, course angle, roll angle, etc., detected by posture data detection means such as accelerometers, gyroscopes, etc., provided at all limb ends at both ends of the joint.
In the embodiment, the real-time motion model is a three-dimensional dynamic model for representing the real-time gesture of the whole robot in the action process.
In this embodiment, the static dynamic model is a static model obtained by dividing (splitting) a real-time motion model frame.
In this embodiment, the local balance control parameter is a robot dynamic model in the local balance controller and the robot static dynamic model corresponding to the local balance requirement, and the output control parameter for keeping the gesture balance of the robot in the state of the corresponding frame static dynamic model is determined.
In this embodiment, the summarizing and blurring process is to sum up and then add up the same type of local balance control parameters, and then perform blurring smoothing process on the added local balance control parameters in time sequence, so that the local balance control parameters have better output continuity.
In this embodiment, the continuous balance control parameters are obtained by summarizing and blurring local balance control parameters of all static dynamic models.
In this embodiment, the control continuity prediction result is a pre-control simulation of the robot based on the continuity balance control parameter (a balanced pre-control braking state model (including a model of the shape and the gesture of the robot) for generating the robot after the robot is controlled based on the continuity balance control parameter), and based on the movement track of the center of gravity of the robot in the balanced pre-control braking state model in the pre-control simulation result, the control continuity of the continuity balance control parameter is pre-evaluated, and further the control continuity prediction result is obtained.
In this embodiment, the final balance control parameter is a balance control parameter obtained by correcting the continuous balance control parameter based on the control continuity prediction result.
In this embodiment, correcting the continuous balance control parameters based on control continuity prediction results of all the continuous balance control parameters to obtain final balance control parameters includes:
in this embodiment, controlling the robot based on the final balance control parameter to obtain the posture balance control result includes:
based on the output force and the corresponding direction which are contained in the final balance control parameters and are used for controlling the output of the single part or the single position of the robot, the output force control is carried out on the single part or the single position of the robot, and then the continuous (small oscillation) gesture balance control result is kept.
In this embodiment, the gesture balance control result is a result of controlling the robot based on the final balance control parameter so that the gesture of the robot is kept in a relatively continuous balance (less oscillation) during the movement process.
In this embodiment, the oscillations occurring during the action of the currently sampled robot may be balanced based on the final balance control parameters.
The beneficial effects of the technology are as follows: the method comprises the steps of carrying out frame splitting on a motion model of a robot in a moving process, carrying out local balance control calculation based on a static dynamic model after frame splitting, summarizing all determined local balance control parameters and carrying out fuzzy processing on time sequence, improving the continuity of robot gesture balance control, and realizing further improvement on the continuity of robot gesture balance control through a determined control continuity prediction result, wherein the gesture balance control process is simplified compared with the prior art.
Example 2:
on the basis of embodiment 1, the attitude balance control method applied to the robot is as follows, S1: based on the local balance requirement of the robot and the real-time attitude data of all joints acquired in the action process, determining the local balance control parameters of each frame of static dynamic model in the real-time motion model of the robot, referring to fig. 2, including:
s101: based on the three-dimensional limb model of the robot and the real-time posture data of all joints acquired in the action process, building a real-time motion model of the robot;
s102: extracting a local dynamic model from each frame of static dynamic model of the real-time motion model based on the local balance requirement;
s103: and determining local balance control parameters of the static dynamic model of the corresponding frame based on the local dynamic model and the local balance controller corresponding to the local balance requirement.
In this embodiment, the three-dimensional limb model is a model for representing the three-dimensional shape and size of the robot limb.
In the embodiment, the local dynamics model is a dynamics model of a part of limbs in the action process of the robot, and the dynamics model of each limb in the part of limbs is represented by the inverted pendulum model, so that a dynamics model representing the part of limbs is obtained.
In this embodiment, the local balance controllers corresponding to the local balance requirements include, for example: the ZMP compensator can correspondingly solve the local balance requirement of maintaining the balance of the robot pelvis joint in the horizontal direction in the dynamic motion; or the soft land controller can correspondingly solve the local balance requirement to counteract the vibration caused by the collision when the robot foot lands; still further, the torso pitch/roll angle controller may correspondingly address the local balancing need to compensate for roll vibration of the center position of the pelvic joint relative to the pitch/roll angle of the torso.
In this embodiment, determining the local balance control parameter of the static dynamic model of the corresponding frame based on the local dynamic model and the local balance controller corresponding to the local balance requirement includes:
substituting dynamics data (such as data of ankle pitch angle required by an upright posture controller, transverse distance between ankle joints of a pitch angle of the whole upper body and the like) contained in the local dynamics model into a corresponding local balance controller (such as an upright posture controller), obtaining local balance control parameters of a static dynamics model of a corresponding frame (such as an upright posture controller outputting a pitch angle adjustment value of the ankle joint of a robot, and determining output force of a corresponding control position point and a corresponding direction thereof as corresponding local balance control parameters based on the adjustment value of the ankle joint pitch angle), wherein the local balance control parameters are preliminarily determined so that the robot can always maintain an upright posture under the posture of the static dynamics model of the corresponding frame.
The beneficial effects of the technology are as follows: and carrying out local splitting on the static dynamic model obtained after the real-time motion model frame is split based on the local balance requirement to obtain a local dynamic model, and combining a local balance controller to obtain local balance control parameters which can realize that the robot meets the corresponding local balance requirement under the gesture of the corresponding frame static dynamic model, namely realizing the local balance control calculation of the robot.
Example 3:
on the basis of embodiment 1, the attitude balance control method applied to the robot is as follows, S2: summarizing and blurring the local balance control parameters of all static dynamic models in the real-time motion model of the robot to obtain continuous balance control parameters of each frame of static dynamic model, and referring to fig. 3, the method comprises the following steps:
s201: summarizing and adding the same type of local balance control parameter values in all local balance control parameters of the static dynamics model to obtain personalized values of each balance control parameter in the static dynamics model;
s202: sequencing the personalized values of each balance control parameter in all the static dynamics models according to the time sequence of all the static dynamics models to obtain a control value sequence of each balance control parameter;
S203: performing fuzzy smoothing processing on the control value sequence to obtain a smooth control value sequence;
s204: taking the smoothed personalized value corresponding to each frame of static dynamics model in the smoothing control value sequence as a continuous balance control parameter of the corresponding balance control parameter in the corresponding static dynamics model;
the personalized value is represented by a vector, the direction of the vector represents the control direction of the corresponding balance control parameter, and the value of the vector represents the output value of the corresponding balance control parameter.
In this embodiment, the sum of the values of the local balance control parameters of the same type is to sum the vectors corresponding to all the local balance control parameters of the same type, where the same type is the same type of data and the control purposes are consistent, for example: and adding the vector corresponding to the output force for adjusting the ankle pitch angle determined based on the vertical posture controller and the vector corresponding to the output force for adjusting the ankle pitch angle determined based on the landing posture controller.
In this embodiment, the balance control parameter is a direct acting parameter that keeps the robot pose balance control system output to the robot, for example: a force is applied to the balance control parameter, and thus the attitude angle or the action speed of the balance control parameter is changed, so that the balance control parameter is expressed by multiple forces.
In this embodiment, the personalized value is a corresponding value (the value is represented by a vector) of a corresponding type of local balance control parameter obtained by summing up and adding up the values of the same type of local balance control parameter in all local balance control parameters of the static dynamics model.
In this embodiment, the control value sequence is a sequence including personalized values of the same balance control parameters in all static dynamic models.
In this embodiment, the control value series is subjected to a fuzzy smoothing process to obtain a new control value series.
In this embodiment, the personalized value is represented by a vector, the direction of which represents the control direction of the corresponding balance control parameter, and the value of which represents the output value of the corresponding balance control parameter, for example: the balance control parameter is the force output by the control system to the corresponding position point of the robot, the numerical value of the force is the module of the vector, and the direction of the force is the module of the vector.
The beneficial effects of the technology are as follows: and the fuzzy processing is carried out on the sequence which is sequenced according to the time sequence after the summarizing and adding of the local balance control parameters in the static dynamics model, so that the preliminary fuzzy smoothing of the control value is realized.
Example 4:
on the basis of embodiment 3, the method for controlling the posture balance applied to the robot, S203: performing fuzzy smoothing processing on the control value sequence to obtain a smooth control value sequence, wherein the fuzzy smoothing processing comprises the following steps:
the vectors corresponding to the personalized values contained in the control value sequence are connected end to end according to the sequence, and a control value characterization line is obtained;
calculating a mutation vector modulus threshold value of the corresponding balance control parameter based on the maximum angle adjusting range and the maximum numerical adjusting range of the corresponding balance control parameter;
screening out points to be smoothed in a control value characterization line based on the mutation vector modulus threshold;
and carrying out fuzzy smoothing treatment on all the points to be smoothed to obtain a smoothing control numerical sequence.
In this embodiment, the control value characterization line is: connecting the end point of the vector corresponding to the first personalized value in the control value sequence with the start point of the vector corresponding to the second personalized value, connecting the end point of the vector corresponding to the second personalized value with the start point of the vector corresponding to the third personalized value, and so on to obtain the folded line segment.
In this embodiment, the maximum angle adjustment range is a preset adjustable maximum angle range of the control object corresponding to each (type of) balance control parameter, for example: for the maximum angular range corresponding to the applicable direction of the output force of the attitude balance control to a certain position point at the time of adjusting the ankle pitch angle, for example: 60 degrees to-60 degrees.
In this embodiment, the maximum numerical adjustment range is a preset adjustable maximum numerical range of the control object corresponding to each (type of) balance control parameter, for example: for example, a range of applicable values for the output force of the attitude balance control to a certain position point at the time of adjusting the ankle pitch angle: no more than 150 newtons.
In this embodiment, the mutation vector modulo threshold value of the corresponding balance control parameter is calculated based on the maximum angle adjustment range and the maximum numerical adjustment range of the corresponding balance control parameter, which is:
combining all integer angles in the maximum angle adjusting range and all integer values in the maximum value adjusting range to determine a plurality of hypothesis vectors;
connecting any two vectors end to end, and determining the distance between the starting point and the end point of the folded line segment obtained after connection;
the maximum distance in all the fold line segments is taken as the abrupt vector modulo threshold corresponding to the balance control parameter.
In this embodiment, the abrupt vector modulus threshold is the result of the vector modulus representation of the maximum allowable difference between the balance control parameters calculated in the static dynamic model of two adjacent frames.
In this embodiment, the point to be smoothed is the point to be smoothed in the control value characterization curve (the point includes the start point and/or the end point of the vector corresponding to the partial personalized value in the control value sequence).
The beneficial effects of the technology are as follows: the mutation vector modulus threshold value is calculated based on the maximum angle adjustment range and the maximum numerical value adjustment range of the balance control parameters, the maximum allowable difference value between the balance control parameters calculated in the static dynamics model of two adjacent frames is expressed by a vector modulus, and the mutation of the balance control parameters in the static dynamics model of the adjacent frames is expressed by the vector by combining the line segments obtained after connecting the vectors corresponding to the personalized numerical values in the control numerical value sequence, so that the point with larger mutation, namely the point to be smoothed, can be screened, and the fuzzy smoothing processing of the control numerical value sequence is realized.
Example 5:
based on embodiment 4, the gesture balance control method applied to the robot, based on the mutation vector modulus threshold, screens out the point to be smoothed in the control value characterization line, includes:
the length between the starting point of the former personalized value in the control value characterization line and the ending point of the latter personalized value in the control value characterization line in two adjacent personalized values in the control value sequence is used for obtaining a mutation vector module;
and taking the ratio of the mutation vector modulus to the mutation vector modulus threshold value as a mutation duty ratio, and taking the connection point of two personalized values of which the mutation duty ratio exceeds the mutation duty ratio threshold value in the control value characterization line as a point to be smoothed.
In this embodiment, the abrupt vector mode is that the abrupt amount of the balance control parameter in the static dynamics model of the adjacent frame is represented by a vector mode.
In this embodiment, the mutation duty cycle is the ratio of the mutation levels used to represent the adjacent two personalized values in the control value sequence.
In this embodiment, the mutation duty ratio threshold is a preset threshold for screening the mutation duty ratio of the reference point to be smoothed.
In this embodiment, the connection point is the connection point of the vectors corresponding to the two personalized values in the control value characterization line.
The beneficial effects of the technology are as follows: the ratio of the mutation vector mode to the mutation vector mode threshold value of each group of adjacent two personalized values in the control value sequence is determined as the mutation duty ratio, so that the accurate quantification of the mutation amount of the balance control parameters in the static dynamic model of the adjacent frames is realized, the two personalized values needing to be adjusted are screened out based on the quantified mutation duty ratio, and the connection points of the two personalized values are taken as points to be smoothed, thereby providing a foundation for the subsequent fuzzy smoothing process.
Example 6:
on the basis of embodiment 5, the gesture balance control method applied to a robot performs fuzzy smoothing on all points to be smoothed to obtain a smoothing control value sequence, including:
Calculating a first mutation duty ratio maximum adjustable value based on a mutation duty ratio between a personalized value taking a point to be smoothed as an end point and a corresponding previous adjacent personalized value, and calculating a first mutation vector module maximum adjustable value based on the first mutation duty ratio maximum adjustable value and a mutation vector module threshold value;
calculating a second mutation duty ratio maximum adjustable value based on the mutation duty ratio between the personalized value taking the point to be smoothed as a starting point and the corresponding next adjacent personalized value, and calculating a second mutation vector module maximum adjustable value based on the second mutation duty ratio maximum adjustable value and a mutation vector module threshold value;
determining a point of the point to be smoothed after fuzzy smoothing processing in the control value characterization line based on the first mutation vector mode maximum adjustable value and the second mutation vector mode maximum adjustable value;
and updating the corresponding two personalized values based on the points of all the points to be smoothed after the fuzzy smoothing processing in the control value characterization line, thereby obtaining a smooth control value sequence.
In this embodiment, calculating the maximum adjustable value of the first mutation duty ratio based on the mutation duty ratio between the personalized value with the point to be smoothed as the end point and the corresponding previous adjacent personalized value includes:
And taking the difference value between the mutation duty ratio threshold value and the mutation duty ratio between the personalized value taking the point to be smoothed as the end point and the corresponding previous adjacent personalized value as the maximum adjustable value of the first mutation duty ratio.
In this embodiment, the first mutation duty ratio maximum adjustable value is the maximum changeable value of mutation duty ratio between the personalized value located in front of the two personalized values and the personalized value located in front of the personalized value, when the point to be smoothed is smoothed and blurred, the two personalized values taking the point to be smoothed as a connection point are changed, and the mutation duty ratio between the two personalized values and the personalized values corresponding to the two adjacent to the two personalized values is also changed.
In this embodiment, calculating the first abrupt vector modulo maximum adjustable value based on the first abrupt duty cycle maximum adjustable value and the abrupt vector modulo threshold value comprises:
the product of the first abrupt change duty cycle maximum adjustable value and the abrupt change vector modulo threshold value is regarded as the first abrupt change vector modulo maximum adjustable value.
In this embodiment, the maximum adjustable value of the first mutation vector mode is the maximum changeable value of the mutation vector mode between the previous personalized value and the previous neighboring personalized value in the two personalized values, which is caused by the change of the two personalized values with the point to be smoothed as the connection point when the point to be smoothed is smoothed and the change of the mutation vector mode between the two personalized values and the corresponding neighboring personalized values.
In this embodiment, calculating the second mutation duty ratio maximum adjustable value based on the mutation duty ratio between the personalized value with the point to be smoothed as the starting point and the corresponding next adjacent personalized value includes:
and taking the difference value between the mutation duty ratio threshold value and the mutation duty ratio between the personalized value taking the point to be smoothed as the starting point and the corresponding next adjacent personalized value as a second mutation duty ratio maximum adjustable value.
In this embodiment, the second mutation duty ratio maximum adjustable value is the maximum changeable value of the mutation duty ratio between the next personalized value and the next adjacent personalized value in the two personalized values, which is caused by changing the two personalized values with the point to be smoothed as the connection point when the point to be smoothed is smoothed and fuzzy.
In this embodiment, calculating the second abrupt vector modulo maximum adjustable value based on the second abrupt duty cycle maximum adjustable value and the abrupt vector modulo threshold value comprises:
the product of the second abrupt change duty cycle maximum adjustable value and the abrupt change vector modulo threshold value is regarded as the second abrupt change vector modulo maximum adjustable value.
In this embodiment, the maximum adjustable value of the second mutation vector mode is the maximum changeable value of the mutation vector mode between the last personalized value and the next adjacent personalized value in the two personalized values, which is caused by the change of the two personalized values with the point to be smoothed as the connection point when the point to be smoothed is smoothed and the mutation vector mode between the two personalized values and the corresponding adjacent personalized values.
In this embodiment, based on two corresponding personalized values updated by points after all points to be smoothed are subjected to fuzzy smoothing processing in the control value characterization line, a smoothing control value sequence is obtained, which is:
taking the vector taking the point to be smoothed as the end point as a first vector to be updated, and taking the vector taking the point to be smoothed as the start point as a second vector to be updated;
updating the end point of the first vector to be updated to be a point obtained by fuzzy smoothing processing of the point to be smoothed in the control value characterization line, so as to realize the updating of the first vector to be updated;
updating the starting point of the second vector to be updated to be a point obtained by fuzzy smoothing processing of the point to be smoothed in the control value characterization line, so as to realize the updating of the second vector to be updated;
And updating all vectors to be updated in the control value sequence to obtain a smooth control value sequence.
The beneficial effects of the technology are as follows: based on the mutation duty ratio and mutation vector mode between two personalized values taking the point to be smoothed as a connecting point and the personalized values corresponding to the two personalized values, calculating the maximum adjustable value of the mutation duty ratio and the maximum adjustable value of the mutation vector mode, and realizing the repeated limitation of the fuzzy smoothing range, so that the smooth control value sequence obtained after the fuzzy smoothing processing meets the control requirement.
Example 7:
based on embodiment 6, the gesture balance control method applied to a robot, based on a first mutation vector mode maximum adjustable value and a second mutation vector mode maximum adjustable value, determines a point to be smoothed after fuzzy smoothing processing in a control value characterization line, including:
taking a starting point of a personalized numerical value taking a point to be smoothed as an end point in a control numerical value characterization line as a circle center, taking a maximum adjustable value of a first mutation vector module as a radius, and determining a first circle region;
taking the end point of the personalized numerical value taking the to-be-smoothed zone you as the starting point in the control numerical value characterization line as the circle center, taking the maximum adjustable value of the second abrupt change vector mode as the radius, and determining a second circle area;
And taking the point with the minimum distance from the corresponding point to be smoothed in the superposition area of the first circle area and the second circle area as the point after the point to be smoothed is subjected to fuzzy smoothing processing in the control value characterization line.
In this embodiment, the first circle region is a closed curve that can be defined by taking a starting point of a personalized numerical value taking a point to be smoothed as an end point in a control numerical value representation line as a circle center and taking a maximum adjustable value of the first abrupt vector mode as a radius.
In this embodiment, the second circle area uses the end point of the personalized numerical value with you as the start point in the control numerical value characterization line as the center of a circle, and uses the maximum adjustable value of the second abrupt vector mode as the radius, and then the determined closed curve can be enclosed into the circle area.
The beneficial effects of the technology are as follows: based on the principle that a circle area with a first abrupt vector mode and a second abrupt vector mode as radiuses is intersected to determine a vector connection point, a point which is reasonable, enables the respective abrupt vector mode adjusting values to respectively meet the corresponding first abrupt vector mode maximum adjustable value and second abrupt vector mode maximum adjustable value, enables the difference value of balance control parameters before and after blurring to be smaller is used as a point, to be smoothed, of a point to be smoothed after blurring smoothing processing in a control value representation line, enables the result after blurring smoothing processing to guarantee control accuracy to a certain extent, has good control continuity, and meets parameter limitation of the balance control parameters.
Example 8:
on the basis of embodiment 1, the attitude balance control method applied to the robot is as follows, S3: correcting the continuous balance control parameters based on the control continuity prediction results of all the continuous balance control parameters to obtain final balance control parameters, referring to fig. 4 and 5, includes:
s301: constructing a balance pre-control braking state model based on the continuous balance control parameters and all static dynamic models;
s302: simulating a pre-control gravity center point movement track based on a balance pre-control braking state model;
s303: obtaining a control continuity prediction result based on a pre-control center-of-gravity point movement track;
s304: and correcting the continuous balance control parameters based on the continuous prediction result to obtain the final balance control parameters of the static dynamic model of each frame.
In this embodiment, a balance pre-control dynamic model is built based on the continuous balance control parameters and all the static dynamic models, namely:
the robot is subjected to pre-control simulation based on the continuous balance control parameters (i.e., a balanced pre-control braking state model (a model including the shape of the robot and the pose of the robot after the robot is controlled based on the continuous balance control parameters) is generated based on the continuous balance control parameters and all static dynamic models).
In this embodiment, the balance pre-control braking state model is a three-dimensional model representing a dynamic posture of the robot after being controlled based on the continuous balance control parameters.
In this embodiment, based on the balanced pre-control braking state model, a pre-control gravity center point movement track is simulated, and referring to fig. 5, it is:
and determining the gravity center of each frame of static model based on the representation of each frame of static model in the balance pre-control braking state model in a preset coordinate system, and connecting the gravity centers in sequence according to the time sequence of the static model to obtain a pre-control gravity center point movement track.
In this embodiment, the pre-control center of gravity point movement track is a movement track of the center of gravity in the three-dimensional space after simulating that the robot is controlled based on the continuous balance control parameters in advance.
The beneficial effects of the technology are as follows: based on the continuous balance control parameters and all static dynamic models, a balance pre-control braking model is built, pre-simulation of the pose of the robot after being interfered is achieved, a moving track of a gravity center point of the robot after being interfered is predicted based on a pre-simulation result, further correction of the continuous balance control parameters is achieved based on the moving track, further balance effect of pose balance control is guaranteed, and moving stability of the robot is further guaranteed.
Example 9:
on the basis of embodiment 8, the method for controlling the posture balance applied to the robot, S303: obtaining a control continuity prediction result based on a pre-control center-of-gravity point movement track, comprising:
taking the projection of the pre-controlled gravity point movement track in a preset coordinate transverse plane as a gravity plane movement track;
and taking the matching degree of the gravity plane moving track and the preset gravity plane moving track as a control continuity prediction result.
In this embodiment, the transverse plane of the preset coordinates is the preset coordinate systemxOyA plane.
In this embodiment, the gravity center plane movement locus pre-controls the locus obtained by connecting projection points of all points in the gravity center point movement locus in the preset coordinate transverse plane.
In this embodiment, the matching degree between the movement track of the gravity center plane and the movement track of the preset gravity center plane is calculated by: aligning the gravity plane moving track and the preset gravity plane moving track (namely aligning a peak curve part in the gravity plane moving track with a peak curve part in the preset gravity plane moving track, aligning a valley curve part in the gravity plane moving track with a valley curve part in the preset gravity plane moving track, and then horizontally aligning the two parts to enable the peak Gu Quxian intersection points of the two parts to coincide), and taking the ratio of the average value of the distance differences of all the points with the same ordinal numbers contained in the two parts to the difference threshold value as the deviation degree and taking the difference value of 1 and the deviation degree as the matching degree after alignment.
The beneficial effects of the technology are as follows: and (3) the projection of the pre-controlled gravity center point movement track in the preset coordinate transverse plane is matched with the preset gravity center plane movement track, so that further checking and calculation of continuity of continuous balance control parameters are realized.
Example 10:
on the basis of embodiment 9, the method for controlling the posture balance applied to the robot, S304: correcting the continuous balance control parameters based on the continuous prediction result to obtain final balance control parameters of each frame static dynamics model, wherein the method comprises the following steps:
when the matching degree in the continuity prediction result exceeds a matching degree threshold value, carrying out alignment period division on the gravity plane moving track and the preset gravity plane moving track, and calculating the local matching degree between the partial gravity plane moving track and the partial preset gravity plane moving track obtained after the period division;
and correcting the continuous balance control parameters based on the partial gravity plane movement track of which the partial matching degree exceeds the matching degree threshold value, and obtaining the final balance control parameters of the corresponding frame static dynamics model.
In this embodiment, the matching degree threshold is a preset judgment threshold for judging whether the continuity of the continuous balance control parameter satisfies the matching degree to be referred to when the requirement.
In this embodiment, the alignment period of the movement track of the gravity center plane and the movement track of the preset gravity center plane is divided, which is that:
aligning the center of gravity plane movement track with the preset center of gravity movement track (i.e., aligning a peak curve portion in the center of gravity plane movement track with a peak curve portion in the preset center of gravity movement track, and aligning a valley curve portion in the center of gravity plane movement track with a valley curve portion in the preset center of gravity movement track);
and then, periodically dividing the gravity center plane movement track and the preset gravity center plane movement track according to the peak-to-valley characteristics of the gravity center plane movement track.
In this embodiment, the partial gravity plane movement locus is a partial gravity plane movement locus corresponding to a single cycle in the gravity plane movement locus.
In this embodiment, the partial preset gravity plane movement track is a part of the preset gravity plane movement track corresponding to a single period in the preset gravity plane movement track.
In this embodiment, the local matching degree is the matching degree between the partial gravity plane moving track and the partial preset gravity plane moving track representing the same period.
In this embodiment, calculating the local matching degree between the partial gravity plane movement track and the partial preset gravity plane movement track obtained after the period division includes:
Aligning the partial gravity plane moving track and the partial preset gravity plane moving track (namely aligning the peak curve part in the partial gravity plane moving track with the peak curve part in the partial preset gravity plane moving track, aligning the valley curve part in the partial gravity plane moving track with the valley curve part in the partial preset gravity plane moving track, and then horizontally coinciding the two parts so as to ensure that the intersection point of the peak Gu Quxian of the two parts coincides), and taking the ratio of the average value of the distance differences of all the points with the same ordinal numbers contained in the two parts and the difference value threshold value as local deviation degree and taking the difference value of 1 and the local deviation degree as local matching degree after alignment.
In this embodiment, based on a partial gravity plane movement track of which the local matching degree exceeds the matching degree threshold, correcting the continuous balance control parameter to obtain a final balance control parameter of a static dynamic model of a corresponding frame, including:
and inputting part of the gravity center plane movement tracks into a pre-trained balance control parameter adjustment quantity determining model (a model obtained by training a large number of gravity center plane movement tracks and corresponding balance control parameter adjustment quantities) to determine balance control parameter adjustment quantities (namely, the balance control parameter types needing to be corrected and the numerical values which should be adjusted) and correcting continuous balance control parameters based on the balance control parameter adjustment quantities to obtain final balance control parameters of the corresponding frame static dynamic model.
The beneficial effects of the technology are as follows: and determining partial gravity plane movement tracks of which the gravity center movement does not meet the requirement based on the partial gravity plane movement tracks after the period division and the partial preset gravity plane movement tracks, and finally correcting continuous balance control parameters based on the partial gravity plane movement tracks and a determined model trained in advance, so that the gesture balance effect of the robot is further ensured.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A posture balance control method applied to a robot, characterized by comprising:
s1: determining local balance control parameters of each frame of static dynamic model in a real-time motion model of the robot based on local balance requirements of the robot and real-time attitude data of all joints acquired in the action process;
s2: summarizing and blurring the local balance control parameters of all static dynamic models in the real-time motion model of the robot to obtain continuous balance control parameters of each frame of static dynamic model;
S3: correcting the continuous balance control parameters based on control continuity prediction results of all the continuous balance control parameters to obtain final balance control parameters;
s4: controlling the robot based on the final balance control parameters to obtain an attitude balance control result;
step S2: summarizing and blurring the local balance control parameters of all static dynamic models in the real-time motion model of the robot to obtain continuous balance control parameters of each frame of static dynamic model, wherein the method comprises the following steps:
s201: summarizing and adding the same type of local balance control parameter values in all local balance control parameters of the static dynamics model to obtain personalized values of each balance control parameter in the static dynamics model;
s202: sequencing the personalized values of each balance control parameter in all the static dynamics models according to the time sequence of all the static dynamics models to obtain a control value sequence of each balance control parameter;
s203: performing fuzzy smoothing processing on the control value sequence to obtain a smooth control value sequence;
s204: taking the smoothed personalized value corresponding to each frame of static dynamics model in the smoothing control value sequence as a continuous balance control parameter of the corresponding balance control parameter in the corresponding static dynamics model;
The personalized values are represented by vectors, the direction of the vectors represents the control direction of the corresponding balance control parameters, and the values of the vectors represent the output values of the corresponding balance control parameters;
step S203: performing fuzzy smoothing processing on the control value sequence to obtain a smooth control value sequence, wherein the fuzzy smoothing processing comprises the following steps:
the vectors corresponding to the personalized values contained in the control value sequence are connected end to end according to the sequence, and a control value characterization line is obtained;
calculating a mutation vector modulus threshold value of the corresponding balance control parameter based on the maximum angle adjusting range and the maximum numerical adjusting range of the corresponding balance control parameter;
screening out points to be smoothed in a control value characterization line based on the mutation vector modulus threshold;
and carrying out fuzzy smoothing treatment on all the points to be smoothed to obtain a smoothing control numerical sequence.
2. The attitude balance control method applied to a robot according to claim 1, characterized in that S1: based on the local balance requirement of the robot and the real-time attitude data of all joints acquired in the action process, determining the local balance control parameters of each frame of static dynamic model in the real-time motion model of the robot, wherein the method comprises the following steps:
s101: based on the three-dimensional limb model of the robot and the real-time posture data of all joints acquired in the action process, building a real-time motion model of the robot;
S102: extracting a local dynamic model from each frame of static dynamic model of the real-time motion model based on the local balance requirement;
s103: and determining local balance control parameters of the static dynamic model of the corresponding frame based on the local dynamic model and the local balance controller corresponding to the local balance requirement.
3. The method for controlling the gesture balance applied to the robot according to claim 1, wherein the step of screening out the point to be smoothed in the control value characterization line based on the abrupt vector modulus threshold value comprises the steps of:
the length between the starting point of the former personalized value in the control value characterization line and the ending point of the latter personalized value in the control value characterization line in two adjacent personalized values in the control value sequence is used for obtaining a mutation vector module;
and taking the ratio of the mutation vector modulus to the mutation vector modulus threshold value as a mutation duty ratio, and taking the connection point of two personalized values of which the mutation duty ratio exceeds the mutation duty ratio threshold value in the control value characterization line as a point to be smoothed.
4. The method for controlling the gesture balance applied to the robot according to claim 3, wherein the step of performing the fuzzy smoothing process on all the points to be smoothed to obtain the smoothing control value sequence comprises the steps of:
Calculating a first mutation duty ratio maximum adjustable value based on a mutation duty ratio between a personalized value taking a point to be smoothed as an end point and a corresponding previous adjacent personalized value, and calculating a first mutation vector module maximum adjustable value based on the first mutation duty ratio maximum adjustable value and a mutation vector module threshold value;
calculating a second mutation duty ratio maximum adjustable value based on the mutation duty ratio between the personalized value taking the point to be smoothed as a starting point and the corresponding next adjacent personalized value, and calculating a second mutation vector module maximum adjustable value based on the second mutation duty ratio maximum adjustable value and a mutation vector module threshold value;
determining a point of the point to be smoothed after fuzzy smoothing processing in the control value characterization line based on the first mutation vector mode maximum adjustable value and the second mutation vector mode maximum adjustable value;
and updating the corresponding two personalized values based on the points of all the points to be smoothed after the fuzzy smoothing processing in the control value characterization line, thereby obtaining a smooth control value sequence.
5. The method for controlling the posture balance of a robot according to claim 4, wherein determining the point of the point to be smoothed after the fuzzy smoothing process in the control value characterization line based on the first mutation vector mode maximum adjustable value and the second mutation vector mode maximum adjustable value comprises:
Taking a starting point of a personalized numerical value taking a point to be smoothed as an end point in a control numerical value characterization line as a circle center, taking a maximum adjustable value of a first mutation vector module as a radius, and determining a first circle region;
taking the end point of the personalized numerical value taking the to-be-smoothed zone you as the starting point in the control numerical value characterization line as the circle center, taking the maximum adjustable value of the second abrupt change vector mode as the radius, and determining a second circle area;
and taking the point with the minimum distance from the corresponding point to be smoothed in the superposition area of the first circle area and the second circle area as the point after the point to be smoothed is subjected to fuzzy smoothing processing in the control value characterization line.
6. The attitude balance control method applied to a robot according to claim 1, characterized by S3: correcting the continuous balance control parameters based on control continuity prediction results of all the continuous balance control parameters to obtain final balance control parameters, including:
s301: constructing a balance pre-control braking state model based on the continuous balance control parameters and all static dynamic models;
s302: simulating a pre-control gravity center point movement track based on a balance pre-control braking state model;
s303: obtaining a control continuity prediction result based on a pre-control center-of-gravity point movement track;
S304: and correcting the continuous balance control parameters based on the continuous prediction result to obtain the final balance control parameters of the static dynamic model of each frame.
7. The method for controlling the posture balance of the robot according to claim 6, wherein S303: obtaining a control continuity prediction result based on a pre-control center-of-gravity point movement track, comprising:
taking the projection of the pre-controlled gravity point movement track in a preset coordinate transverse plane as a gravity plane movement track;
and taking the matching degree of the gravity plane moving track and the preset gravity plane moving track as a control continuity prediction result.
8. The method for controlling the posture balance of the robot according to claim 7, wherein S304: correcting the continuous balance control parameters based on the continuous prediction result to obtain final balance control parameters of each frame static dynamics model, wherein the method comprises the following steps:
when the matching degree in the continuity prediction result exceeds a matching degree threshold value, carrying out alignment period division on the gravity plane moving track and the preset gravity plane moving track, and calculating the local matching degree between the partial gravity plane moving track and the partial preset gravity plane moving track obtained after the period division;
And correcting the continuous balance control parameters based on the partial gravity plane movement track of which the partial matching degree exceeds the matching degree threshold value, and obtaining the final balance control parameters of the corresponding frame static dynamics model.
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