CN111891252B - Body posture slope self-adaptive control method of four-footed bionic robot - Google Patents

Body posture slope self-adaptive control method of four-footed bionic robot Download PDF

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CN111891252B
CN111891252B CN202010783703.1A CN202010783703A CN111891252B CN 111891252 B CN111891252 B CN 111891252B CN 202010783703 A CN202010783703 A CN 202010783703A CN 111891252 B CN111891252 B CN 111891252B
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robot
coordinate system
slope
leg
moment
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CN111891252A (en
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李彬
梁启星
张慧
李志�
刘伟龙
荣学文
范永
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Qilu University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D57/00—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track
    • B62D57/02—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track with ground-engaging propulsion means, e.g. walking members
    • B62D57/032—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track with ground-engaging propulsion means, e.g. walking members with alternately or sequentially lifted supporting base and legs; with alternately or sequentially lifted feet or skid
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/08—Programme-controlled manipulators characterised by modular constructions
    • 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
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • 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
    • B25J9/1679—Programme controls characterised by the tasks executed

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Abstract

The invention provides a body posture slope self-adaptive control method of a four-foot bionic robot, which realizes optimal plantar force distribution and foot end landing prediction under model prediction control; posture adjustment of the robot during climbing is achieved through a body posture slope adaptive algorithm, so that the robot can sense the slope terrain environment, posture adaptive adjustment and stable movement walking of the robot on the slope terrain are achieved, and visual perception information of the robot is not needed.

Description

Body posture slope self-adaptive control method of four-footed bionic robot
Technical Field
The disclosure belongs to the technical field of robot attitude control, and relates to a body attitude slope self-adaptive control method of a four-footed bionic robot.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
At present, the bionic robot gradually moves from a laboratory to an operation field, and the high dynamic property, the complex environment adaptability and the large load capacity of the bionic robot are obviously improved. To accomplish a given task, robots typically employ some single terrain adaptation mode, which significantly reduces the strain capacity of the robot to a variety of different complex environments. In nature, the four-footed mammal can perform self-adaptive adjustment of the posture according to different environmental conditions so as to improve the stability of the robot in the motion process and the self-adaptability to complex environments. Therefore, the method simulates the strong adaptability of the natural quadruped mammal to the complex environment, improves the man-machine interaction capability of the robot in the complex environment, and needs to research the posture self-adaptive adjustment strategy and method of the robot in the slope terrain.
In recent years, Model Predictive Control (MPC) is gradually applied to stable motion Control of a four-legged robot, and a good motion Control effect is obtained, so that more and more scholars deeply study Model Predictive Control. The research results are applied and expanded to model prediction control, and optimization and solution of robot foot position and foot position control are achieved. However, the inventor knows that the adaptability of the robot to a complex road surface is relatively low, and how to introduce terrain information into the attitude motion control of the robot is not considered, so that the stability margin of the robot in the motion process is improved, and the adaptability of the robot to the rugged terrain environment such as a slope is better improved.
Meanwhile, most of the existing documents realize slope adaptive adjustment under the condition of static gait or based on visual information, so that the moving speed of the robot is reduced, the slope walking is not stable enough, or the workload of processing the visual information is increased.
Disclosure of Invention
In order to solve the problems, the body posture slope self-adaptive control method of the four-footed bionic robot is provided, and the optimal plantar force distribution and foot end landing prediction are realized under model prediction control; posture adjustment of the robot during climbing is achieved through a body posture slope adaptive algorithm, so that the robot can sense the slope terrain environment, posture adaptive adjustment and stable movement walking of the robot on the slope terrain are achieved, and visual perception information of the robot is not needed.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a body posture slope self-adaptive control method of a quadruped bionic robot comprises the following steps:
building a bionic robot body and a world coordinate system, modeling the robot into a single rigid body, and building a robot dynamic model by stressing at a contact point;
calculating and tracking the track of the leg of the bionic robot in a world coordinate system, calculating the joint moment of the leg, considering the joint moment in the ground force control process, and determining the optimal control track by utilizing model prediction control;
controlling the motion of the bionic robot according to the obtained optimal control track;
acquiring attitude parameters of the bionic robot at the moment in the walking process, determining coordinate mapping of the foot end position, and adjusting the gravity center position of the robot on the slope;
and setting a virtual slope, calculating the angle of the virtual slope in the current state according to the body pitch angle in the attitude parameter of the robot at the moment, taking the angle as the adjustment value of the pitch angle of the robot at the next moment, and continuously adjusting the attitude of the robot until the virtual slope angle, the body pitch angle and the actual slope angle are equal to each other.
As an alternative embodiment, a bionic robot body and a world coordinate system are built, the robot is modeled into a single rigid body, the force is applied at a contact point, and the specific process of building a robot dynamic model comprises the following steps:
building a four-footed robot body and a world coordinate system, enabling the model to model the robot into a single rigid body, stressing at contact points, and according to the ground reaction force borne by each leg, locating the contact point of each leg of the robot on the ground at the position of a body coordinate system, and determining the gravity and the ground reaction force borne by the robot;
and calculating the inertia tensor of the robot in a world coordinate system according to the relation between the angular momentum and the torque.
As an alternative embodiment, the specific process of calculating and tracking the trajectory of the leg of the bionic robot in the world coordinate system and calculating the joint moment of the leg comprises the following steps:
calculating the joint moment of the leg i by using a control law:
Figure GDA0003208151630000031
here Ji∈R3×3Is a foot-end jacobian matrix, Kp,Kd∈R3×3Is a diagonal positive proportional and derivative gain matrix,Rpi,Rvi∈R3is the position and velocity of the ith leg,Rpi,ref,R vi,ref∈R3a reference motion trajectory for the swing leg position and velocity; tau isi,ff∈R3For the feed forward torque, the following is obtained:
Figure GDA0003208151630000041
wherein Λi∈R3×3Is an inertial matrix of the operating space, ai,ref∈R3Is a reference acceleration, q, in a coordinate system of the bodyi∈R3Is a vector of the position of the joint,
Figure GDA0003208151630000042
is the moment created by the weight of the leg and the coriolis force.
As an alternative embodiment, the concrete process of determining the optimal control trajectory using the model predictive control in consideration of the joint moment during the ground force control includes:
converting the determined optimal control trajectory into a quadratic programming problem, and solving an optimal value of an objective function under a constraint condition:
Figure GDA0003208151630000043
wherein x isi+1,refIs the system reference track at time i +1, xi+1Is the system state at the (i + 1) th moment, and the constraint comprises:
xi+1＝Aixi+Biui,i＝0...k-1
Figure GDA0003208151630000044
Diui＝0,i＝0...k-1
wherein xiIs the system state at step i, uiIs a control input at step i, QiAnd RiIs a diagonal positive semi-definite matrix of weights, AiAnd BiRepresenting discrete time system dynamics, Ci,
Figure GDA0003208151630000045
c iInequality constraints representing control inputs, DiThe method comprises the steps of selecting a matrix of forces corresponding to the foot in the swing phase when the time step is i, and finding a control input sequence through optimization, wherein the control input sequence ensures that the robot system moves along xrefWith reference to the trajectory, a compromise is made between the control effect and the tracking progress.
As a further limitation, in the process of solving the optimized value of the objective function, the objective function is discretized by using an accurate discretization method, in the discretization process, a zero-order holder is used for sampling, sampling and discretizing are performed in a fixed time period, and the discretized objective function is converted into a quadratic programming problem to be solved.
As an alternative embodiment, the specific process of determining the coordinate mapping of the foot end position according to the attitude parameters of the robot and adjusting the gravity center position of the robot on the slope includes:
on the basis of a stable condition of a zero moment point, the center of mass of the robot is adjusted by adjusting the positions of the feet of the robot, and on the basis of dynamics, the coordinates of the positions of the feet of the robot are mapped according to the posture information of a trunk according to the relation between a coordinate system of a machine body, a world coordinate system and a track planning coordinate system and an inclined plane coordinate system, so that the adjustment adaptation of the positions of the feet of the slope is realized.
As an alternative embodiment, the specific process of setting a virtual ramp is as follows: a two-dimensional plane formed by the front support foot end and the rear support foot end when the bionic robot moves from a flat road surface to a slope is used as a virtual slope.
A body posture slope adaptive control system of a bionic robot comprises:
the dynamic model building module is configured to build a bionic robot body and a world coordinate system, model the robot into a single rigid body, bear force at a contact point and build a robot dynamic model;
the model prediction control module is configured to calculate and track the track of the leg of the bionic robot in a world coordinate system, calculate the joint moment of the leg, determine an optimal control track by using model prediction control in consideration of the joint moment in the ground force control process, and control the motion of the bionic robot according to the optimal control track;
the foot end position adjusting module is configured to determine coordinate mapping of the foot end position according to the attitude parameter of the robot at the moment and adjust the gravity center position of the robot on the slope;
and the slope attitude adjusting module is configured to set a virtual slope, calculate the angle of the virtual slope in the current state according to the trunk pitch angle in the attitude parameter of the robot at the moment, and continuously adjust the attitude of the robot by taking the angle as the adjustment value of the pitch angle of the robot at the next moment until the virtual slope angle, the trunk pitch angle and the actual slope angle are equal.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute a method of body pose slope adaptive control of a quadruped biomimetic robot.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the body posture slope adaptive control method of the four-footed bionic robot.
Compared with the prior art, the beneficial effect of this disclosure is:
according to the method, a control framework for adjusting the body posture of the robot slope is constructed, and the optimal path walking of the bionic robot is realized by utilizing model prediction control.
According to the attitude parameters of the robot during movement, coordinate mapping of a foot end position is obtained through a derived foot end track algorithm, and the gravity center position of the robot on a slope is adjusted; and then, by constructing a virtual slope, the self-adaptive adjustment of the trunk posture of the robot in the process of ascending is realized, so that the stability margin of the robot on the slope surface is enhanced, the motion space of foot ends is optimized, and the stable walking of the quadruped robot on the slope terrain is realized.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a schematic diagram of a robot body and a world coordinate system under dynamic modeling;
FIG. 2 is a schematic diagram of a model predictive control framework;
FIG. 3 is a schematic view of a robot centroid projection before foot end position adjustment;
FIG. 4 is a schematic view of a robot centroid projection after the foot end position adjustment;
FIG. 5 is a schematic diagram showing the positional relationship between coordinate systems in the adjustment of the foot end position;
fig. 6(a) and 6(b) are front and rear views of the robot trunk posture adjustment;
FIG. 7 is a flow chart of robot body pose slope adaptive control;
fig. 8 is a diagram of a robot body posture slope adaptive rectification.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiment provides a body posture slope self-adaptive control method applied to a four-footed bionic robot, which comprises the following steps:
firstly, a control frame for adjusting the body posture of the robot slope is required to be constructed; the method specifically comprises the following steps:
(1) establishing a simplified dynamic model:
a four-footed robot body and a world coordinate system are set up as shown in figure 1, so that the model models the robot into a single rigid body and forces are applied to contact points. Setting the ground reaction force f of each legi∈R3The contact point of the ith leg of the robot on the ground is positioned at the position r of the body coordinate systemi∈R3. The gravity and ground reaction force experienced by the robot can be given by the newton equation:
Figure GDA0003208151630000081
wherein p ∈ R3Is the position of the gravity center of the robot under a world coordinate system,
Figure GDA0003208151630000082
the acceleration of the robot in a world coordinate system is shown, and n represents the number of legs which are in contact with the ground in the motion process of the robot. m is the mass of the robot, g is the R3Is the acceleration of gravity.
According to the relation between the angular momentum and the torque, an Euler formula is obtained:
Figure GDA0003208151630000083
where I ∈ R3Is the inertia tensor of the robot under the world coordinate system, and omega belongs to R3Is the angular velocity of the robot in a world coordinate system.
1. Angular velocity dynamics estimation:
the directional vector of the robot is represented by the z-y-x Euler angle, i.e.
Figure GDA0003208151630000084
Where ψ is yaw, θ is pitch,
Figure GDA0003208151630000091
is a roll. These angles correspond to a series of rotations, so the conversion from body to world coordinates can be expressed as
Figure GDA0003208151630000092
Where R represents the rotation matrix of the body to world coordinates, RnAnd (α) represents a positive rotation of α about the n-axis. From the rate of change of these angles, the angular velocity in world coordinates can be found:
Figure GDA0003208151630000093
if (cos (θ) ≠ 0), the equation can be solved in reverse:
Figure GDA0003208151630000094
for small values of slewing and pitching
Figure GDA0003208151630000095
The formula (10) can be approximated as
Figure GDA0003208151630000096
Is equivalent to
Figure GDA0003208151630000097
Wherein formula (6) can be approximated as:
Figure GDA0003208151630000098
the inertia tensor I of the robot in the world coordinate system can be represented by a rotation matrix R and the inertia tensor in the body coordinate systemRI represents:
I＝RRIRT (9)
at small turning and pitch angles, the inertia tensor of the robot is:
Figure GDA0003208151630000101
2. simplification of the kinetics:
the state space equation of the quadruped robot system can be constructed by combining Newton and Euler equations:
Figure GDA0003208151630000102
wherein S isg＝[0 0 1]T，g＝[0 0 g]TAnd g represents the acceleration of gravity, and generally takes the value of 9.81 m/s.
By simplifying the expression (11), the dynamics can be changed into a simple state space form:
Figure GDA0003208151630000103
wherein A isc∈R13×13And Bc∈R13×3n. This form depends only on the deflection and the foot end position. If these can be calculated in advance, the dynamics become linearly time-varying. The method is suitable for convex model predictionAnd (5) measuring and controlling.
(2) MPC controller construction:
1. controlling the swinging leg:
the swing controller calculates and tracks the trajectory of the foot in a world coordinate system. The trajectory tracking controller calculates the joint moment using the sum of the feedback term and the feedforward term. Calculating the joint moment of the leg i by using a control law:
Figure GDA0003208151630000111
here Ji∈R3×3Is a foot-end jacobian matrix, Kp,Kd∈R3×3Is a diagonal positive proportional and derivative gain matrix,Rpi,Rvi∈R3is the position and velocity of the ith leg,Rpi,ref,R vi,ref∈R3is a reference motion trajectory for the swing leg position and velocity. Tau isi,ff∈R3For the feed forward torque, the following is obtained:
Figure GDA0003208151630000112
wherein Λi∈R3×3Is an inertial matrix of the operating space, ai,ref∈R3Is a reference acceleration, q, in a coordinate system of the bodyi∈R3Is a vector of the position of the joint,
Figure GDA0003208151630000113
is the moment created by the weight of the leg and the coriolis force.
In order to ensure high gain stability during the above-mentioned leg movements, the matrix KpAdjustments are required to ensure that the natural frequency of the closed loop system remains relatively constant under changes in the apparent mass of the leg. KpThe ith diagonal element in the ith axial direction of (1) is required to maintain a constant natural frequency ωiThis can be approximated by:
Figure GDA0003208151630000114
Λ hereini,iIs the ith, i term of the mass matrix, corresponding to the apparent mass of the leg along the ith axis.
2. Controlling the ground acting force:
during ground force control, the joint moments are as follows:
Figure GDA0003208151630000121
where R is a rotation matrix that transforms the body into world coordinates, J ∈ R3×3For the foot-end jacobian, f is the force vector calculated by the predictive controller in the world coordinate system.
Consider a standard form of MPC problem with step size k, xi+1,refIs the system reference track at time i +1, xi+1The system state at the (i + 1) th moment is converted into a quadratic programming problem, and the optimized value of the following equation is solved:
Figure GDA0003208151630000122
constraint xi+1＝Aixi+Biui,i＝0...k-1 (18)
Figure GDA0003208151630000123
Diui＝0,i＝0...k-1 (20)
Wherein xiIs the system state at step i, uiIs a control input at step i, QiAnd RiIs a diagonal positive semi-definite matrix of weights, AiAnd BiRepresenting discrete time system dynamics, Ci,
Figure GDA0003208151630000124
c iInequality constraints representing control inputs, DiIs a matrix of forces corresponding to the foot in the swing phase when the time step is selected to be i. The controller optimally finds a sequence of control inputs that will ensure that the system is along xrefWith reference to the trajectory, a compromise is made between the control effect and the tracking progress.
Solution of MPC
Force restraint:
the corresponding above formula (17) is combined with the friction cone to perform stress analysis, all the foot ends away from the ground are subjected to plantar force set as 0, and the 6 inequalities of each leg are constrained as follows:
fmin≤fz≤fmax
-μfz≤±fx≤μfz (21)
-μfz≤±fy≤μfz
generation of the reference trajectory:
and constructing a reference track by utilizing the expected motion behaviors of the robot. In the application we have designed, the reference trajectory is simple, containing only non-zero xy-velocities, xy-positions, z-positions, deflections and deflection rates. The other states (roll, pitch, yaw rate, pitch rate and z-velocity) are always set to 0, except for the yaw and xy-position determined by integration with the appropriate velocity. The reference trajectory is also used to determine the kinetic constraints and the foot end position.
And (3) system discretization:
solving an equation of the MPC according to the constructed MPC controller, wherein the solving process of the MPC needs to firstly discretize a state equation of the MPC and then solve an optimal value after discretization. According to the continuous system discretization method, the embodiment discretizes the formula (6) by adopting an accurate discretization method, wherein a zero-order retainer is used for sampling, and the discretization is carried out by assuming that the sampling and the discretization are carried out in a fixed and very small time period T, AcAnd BcDiscretization can be expressed as:
Figure GDA0003208151630000131
with the above simplification, the discretization equation in conjunction with the linear system can be in the form:
x[n+1]＝Adx[n]+Bd[n]u[n] (23)
expanding the discretized equation to k dimension, and converting the MPC of k dimension into a quadratic programming problem
Figure GDA0003208151630000141
The discretized equation is converted into a quadratic programming problem to be solved, and a quadratic gauge
Variable u in objective function of scratch questioniThe relationship is as follows:
X＝Aqpx0+BqpU (25)
wherein X represents the state at the next moment, AqpAnd BqpIs a matrix obtained by discretizing the formula (22), x0Is the initial state of the system, i.e. the state of the system at the last moment.
The objective function can be written as
J(U)＝(Aqpx0+BqpU-xref)TL(Aqpx0+BqpU-xref)+UTKU (26)
Where matrix L and matrix K are diagonal matrices.
Solving the formula (21) by utilizing the qpOASES open source library can obtain the optimal control input quantity U, namely the plantar force Fi. The constructed model predictive control structure is shown in fig. 2.
(3) Slope stability analysis:
when the quadruped robot walks on a slope with diagonal sprint gait, the position of the center of mass of the quadruped robot projected in the four-foot supporting surface of the robot along the gravity direction can be deviated to the negative gradient direction of the inclined surface, and the stability of the robot on the slope surface is influenced. In this case, the angle value of each leg joint in the robot gait plan is not only related to the foot end trajectory existing in the model predictive control, but also should be combined with the gradient of the terrain. Based on the stable condition of the zero moment point, the position of the foot end of the robot is adjusted to realize the adjustment of the mass center of the robot.
When the robot moves in a diagonal sprint gait on a slope, the initial state is shown in fig. 3. The center of mass is used as the stability reference of a zero moment point, the origin of a machine body coordinate system is used as the center of mass of the robot, the projection of the center of mass of the robot falls behind the diagonal line of the supporting foot before the foot end is adjusted, and if the slope inclination angle is large, the projection point falls outside the supporting polygon, so that the robot unstably topples.
And taking the intersection point of the two supporting diagonal lines as an original point as a stable circle with the radius of R, and when the center of mass of the robot is projected in the stable circle, the maximum distance between the projection point and the two diagonal supporting lines is smaller than the radius of the circle, namely S is smaller than R, and at the moment, the robot meets the zero moment point stability criterion. The center of mass of the robot is adjusted to make the projection point fall in the stable circle, as shown in fig. 4, the center of mass projection point can be ensured to be positioned in the support polygons of the two support diagonals at the same time.
(4) Expansion of foot end position adjustment:
the coordinate system is extended on the basis of dynamics as shown in fig. 5:
coordinate system sigma of machine bodyA: origin GAAt the center of gravity, X, of the robot bodyAThe axis points to the front of the robot movement, YAThe axis pointing to the left of the robot, ZAThe axis is vertical to the machine body and upwards.
World coordinate system ΣB：ZBAxis perpendicular to horizontal plane upwards, XBAxis XAVertical projection of the axis in the horizontal plane, YBFollows the right-hand spiral criterion.
Trajectory planning coordinate system sigmaM: its origin and ∑AThe original points are coincident, sigmaMByAAnd rotating the rotary die.
Coordinate system of inclined plane sigmaC: the origin is the projection of the gravity center of the robot on the inclined plane, sigmaCByMRotation and translationAnd (4) removing.
From the relationship between the coordinate systems, one can derive:
PHIPpTOE＝Ry(θad)Rx(ψad)(CHIPpTOE+CpHIP)+MPC-MPHIP (27)
whereinCHIPpTOEAs a coordinate system sigmaCThe position of the midfoot end relative to the hip joint,PHIPpTOEas a coordinate system sigmaMThe position of the midfoot end relative to the hip joint,CpHIPandMPHIPas a coordinate system sigmaCSum ΣMThe position of the mid hip joint relative to the origin,MPCas a coordinate system sigmaCIn a coordinate system ΣMThe position of (a). ThetaadAnd psiadAs a coordinate system sigmaMToCAnd adjusting angles around the y axis and the x axis respectively during rotation. Meanwhile, the position adjustment of the foot end needs to be combined with the posture of the trunk of the robot, the pitch angle value obtained in the next subsection is directly quoted, and the pitch angle value can be obtained through the relation between coordinate systems:
Figure GDA0003208151630000163
wherein the content of the first and second substances,AHIPpTOEas a coordinate system sigmaAThe position of the midfoot end relative to the hip joint,MPBas a coordinate system sigmaBIn a coordinate system ΣMIn the position of (a) in the first,APHIPas a coordinate system sigmaAThe position of the mid hip joint.
Figure GDA0003208151630000161
θrefAnd psirefRespectively represent coordinate system sigmaMToATwist, pitch and roll angles about the z, y and x axes during rotation.
When the quadruped robot climbs, the support position adjustment and the trunk posture adjustment are combined, the support position and the trunk posture parameters are set according to the posture feedback of the trunk, namely, the order is made
Figure GDA0003208151630000162
Where theta isIMUPitch angle, ψ, measured for an Inertial Measurement Unit (IMU) fixed to the trunkIMUIs the measured roll angle.
Then, the formula (1) and the formula (2) can be combined and simplified as the following formula
AHIPpTOE＝CHIPpTOE+Rx(-ψIMU)Ry(-θIMU)MPC (30)
Wherein the content of the first and second substances,MpC＝[0 0 -H]Tis to hold ∑COrigin at ΣMThe coordinate system in (1) is a constant vector, and H is the height of the center of mass when standing. The equation realizes coordinate mapping of the foot end position according to the trunk posture information, and the quadruped robot can realize adjustment and adaptation of the slope foot end position by using the mapping.
(5) Adjusting the slope posture:
the body posture slope adaptive algorithm of the invention provides a concept of a virtual slope in the body adjustment, and the virtual slope is a two-dimensional plane formed by the front support foot end and the rear support foot end when a four-legged robot moves from a flat road surface to the slope (at the moment, the front support foot falls on the slope, and the rear support foot is on the ground plane) as shown in fig. 7. In the process, the angle of the virtual slope changes in real time along with the advance of the robot, and the trunk of the robot is influenced by the virtual slope to pitch; at this time, the IMU transmits the measured trunk pitch angle in the current state as an input parameter to the adaptive adjustment algorithm to obtain the angle of the virtual slope in the current state, and then uses the angle as the adjustment value of the pitch angle of the robot at the next moment, so as to repeat the operation, when the angle of the virtual slope, the trunk pitch angle and the actual slope angle are equal, the algorithm cycle is finished, and the adaptive adjustment of the slope attitude is realized, as shown in fig. 6(a) and 6 (b).
The following two formulas are the slope attitude adjustment algorithm of the quadruped robot:
p＝(L+ΔF+ΔH)cosα (31)
Figure GDA0003208151630000171
β’＝α＝β (33)
in the formula (31), p is the projection of the front and rear foot ends on the horizontal ground, L is the length of the trunk of the robot, and deltaFThe displacement of the foot end of the front leg of the robot relative to the hip joint of the front leg is shown, and alpha is the pitch angle of the robot body measured by the IMU in real time; in the equation (32), β' is the angle of the virtual slope formed by the front and rear foot ends, and h is the height difference between the front and rear foot ends of the robot, which can be obtained by the state estimator.
Considering that the quadruped robot can generate attitude oscillation when climbing a slope with a diagonal sprint gait, an inertia link is added to reduce the influence of attitude disturbance on attitude adjustment, wherein a parameter k is related to joint force control, a state estimation value, a walking speed and ground material characteristics of the robot. Thus, equation (32) can be rewritten as
Figure GDA0003208151630000181
In order to compensate the influence of the uncertain factors, the quadruped robot is repeatedly walked in a flat terrain with the same material as the slope, and the result shows that the pitch angle error of the robot under the influence of the uncertain factors is between 0.7 and 1.2 degrees. And according to the error result, adjusting an inertia parameter k in a climbing experiment according to the actual situation. The adaptive rectification process on the quadruped robot slope is shown in fig. 8.
The following product examples are also provided:
a body posture slope adaptive control system of a bionic robot comprises:
the dynamic model building module is configured to build a bionic robot body and a world coordinate system, model the robot into a single rigid body, bear force at a contact point and build a robot dynamic model;
the model prediction control module is configured to calculate and track the track of the leg of the bionic robot in a world coordinate system, calculate the joint moment of the leg, determine an optimal control track by using model prediction control in consideration of the joint moment in the ground force control process, and control the motion of the bionic robot according to the optimal control track;
the foot end position adjusting module is configured to determine coordinate mapping of the foot end position according to the attitude parameter of the robot at the moment and adjust the gravity center position of the robot on the slope;
and the slope attitude adjusting module is configured to set a virtual slope, calculate the angle of the virtual slope in the current state according to the trunk pitch angle in the attitude parameter of the robot at the moment, and continuously adjust the attitude of the robot by taking the angle as the adjustment value of the pitch angle of the robot at the next moment until the virtual slope angle, the trunk pitch angle and the actual slope angle are equal.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute a method of body pose slope adaptive control of a quadruped biomimetic robot.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the body posture slope adaptive control method of the four-footed bionic robot.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims (9)

1. A self-adaptive control method for a body posture slope of a quadruped bionic robot is characterized by comprising the following steps: the method comprises the following steps:
building a bionic robot body and a world coordinate system, modeling the robot into a single rigid body, and building a robot dynamic model by stressing at a contact point;
calculating and tracking the track of the leg of the bionic robot in a world coordinate system, calculating the joint moment of the leg, considering the joint moment in the ground force control process, and determining the optimal control track by utilizing model prediction control;
controlling the motion of the bionic robot according to the obtained optimal control track;
acquiring attitude parameters of the bionic robot at the moment in the walking process, determining coordinate mapping of the foot end position, and adjusting the gravity center position of the robot on the slope; the specific process comprises the following steps:
on the basis of a stable condition of a zero moment point, the position of the foot end of the robot is adjusted to realize the adjustment of the mass center of the robot, and on the basis of dynamics, the coordinate mapping of the position of the foot end is carried out according to the posture information of a trunk according to the relation between a coordinate system of a machine body, a world coordinate system and a track planning coordinate system and an inclined plane coordinate system, so that the adjustment adaptation of the position of the foot end of a slope is realized; the method comprises the following specific steps:
coordinate system sigma of machine bodyA: origin GAAt the center of gravity, X, of the robot bodyAThe axis points to the front of the robot movement, YAThe axis pointing to the left of the robot, ZAThe shaft is vertical to the machine body and upwards;
world coordinate system ΣB：ZBAxis perpendicular to horizontal plane upwards, XBAxis XAVertical projection of the axis in the horizontal plane, YBFollows the right-handed screw criterion;
trajectory planning coordinate system sigmaM: its origin and ∑AThe original points are coincident, sigmaMByARotating the rotary drum;
coordinate system of inclined plane sigmaC: the origin is the projection of the gravity center of the robot on the inclined plane, sigmaCByMRotation and translation are obtained;
from the relationship between the coordinate systems:
PHIPpTOE＝Ry(θad)Rx(ψad)(CHIPpTOE+CpHIP)+MPC-MPHIP (1)
whereinCHIPpTOEAs a coordinate system sigmaCThe position of the midfoot end relative to the hip joint,PHIPpTOEas a coordinate system sigmaMThe position of the midfoot end relative to the hip joint,CpHIPandMPHIPas a coordinate system sigmaCSum ΣMThe position of the mid hip joint relative to the origin,MPCas a coordinate system sigmaCIn a coordinate system ΣMThe position of (a); thetaadAnd psiadAs a coordinate system sigmaMToCAdjusting angles around the y axis and the x axis respectively during rotation; from the relationship between the coordinate systems:
Figure FDA0003208151620000021
wherein the content of the first and second substances,AHIPpTOEas a coordinate system sigmaAThe position of the midfoot end relative to the hip joint,MPBas a coordinate system sigmaBIn a coordinate system ΣMIn the position of (a) in the first,APHIPas a coordinate system sigmaAThe position of the mid hip joint;
Figure FDA0003208151620000022
θrefand psirefRespectively represent coordinate system sigmaMToATorsion angles, pitch angles and roll angles around the z-axis, the y-axis and the x-axis during rotation;
when the quadruped robot climbs a slope, the supporting position is adjusted and the trunk is adjusted
The posture adjustment is combined, order
θad＝θref＝θIMU
ψad＝ψref＝ψIMU
Wherein, thetaIMUPitch angle, ψ, measured for an Inertial Measurement Unit (IMU) fixed to the trunkIMUIs the measured roll angle;
formula (1) and formula (2) are combined and simplified as:
AHIPpTOE＝CHIPpTOE+Rx(-ψIMU)Ry(-θIMU)MPC
wherein the content of the first and second substances,MpC＝[0 0 -H]Tis to hold ∑COrigin at ΣMThe coordinate system in (1) is a constant vector, H is the height of the center of mass when standing,CHIPPTOEas a coordinate system sigmaCThe position of the midfoot end relative to the hip joint;
setting a virtual slope, calculating the angle of the virtual slope in the current state according to the body pitch angle in the attitude parameter of the robot at the moment, taking the angle as the adjustment value of the pitch angle of the robot at the next moment, and continuously adjusting the attitude of the robot until the virtual slope angle, the body pitch angle and the actual slope angle are equal to each other, wherein the three specific algorithms are as follows:
p＝(L+ΔF+ΔH)cosα (31)
Figure FDA0003208151620000031
β’＝α＝β(33)
in the formula (31), p is the projection of the front and rear foot ends on the horizontal ground, L is the length of the trunk of the robot, and deltaFThe displacement of the foot end of the front leg of the robot relative to the hip joint of the front leg is shown, and alpha is the pitch angle of the robot body measured by the IMU in real time; in the formula (32), beta' is the angle of the virtual slope formed by the front and rear foot ends, and h is the angle of the robotThe height difference of the front foot end and the rear foot end is obtained by a state estimator;
wherein the parameter k is related to joint force control, state estimation value, walking speed and ground material characteristics of the robot; the formula (32) is rewritten as
Figure FDA0003208151620000032
2. The self-adaptive control method for the body posture slope of the quadruped bionic robot as claimed in claim 1, which is characterized in that: the method comprises the following steps of constructing a bionic robot body and a world coordinate system, modeling the robot into a single rigid body, stressing at a contact point, and establishing a robot dynamic model in the specific process of:
building a four-footed robot body and a world coordinate system, enabling the model to model the robot into a single rigid body, stressing at contact points, and according to the ground reaction force borne by each leg, locating the contact point of each leg of the robot on the ground at the position of a body coordinate system, and determining the gravity and the ground reaction force borne by the robot;
and calculating the inertia tensor of the robot in a world coordinate system according to the relation between the angular momentum and the torque.
3. The self-adaptive control method for the body posture slope of the quadruped bionic robot as claimed in claim 1, which is characterized in that: the method comprises the following specific processes of calculating and tracking the track of the leg of the bionic robot in a world coordinate system and calculating the joint moment of the leg, wherein the specific processes comprise the following steps:
calculating the joint moment of the leg i by using a control law:
Figure FDA0003208151620000041
here Ji∈R3×3Is a foot-end jacobian matrix, Kp,Kd∈R3×3Is a diagonal positive proportional and derivative gain matrix,Rpi, Rvi∈R3is the position and velocity of the ith leg,Rpi,ref,R vi,ref∈R3a reference motion trajectory for the swing leg position and velocity; tau isi,ff∈R3For the feed forward torque, the following is obtained:
Figure FDA0003208151620000042
wherein Λi∈R3×3Is an inertial matrix of the operating space, ai,ref∈R3Is a reference acceleration, q, in a coordinate system of the bodyi∈R3Is a vector of the position of the joint,
Figure FDA0003208151620000043
is the moment created by the weight of the leg and the coriolis force.
4. The self-adaptive control method for the body posture slope of the quadruped bionic robot as claimed in claim 1, which is characterized in that: the concrete process of determining the optimal control track by utilizing model prediction control by considering the joint moment in the ground force control process comprises the following steps:
converting the determined optimal control trajectory into a quadratic programming problem, and solving an optimal value of an objective function under a constraint condition:
Figure FDA0003208151620000051
wherein x isi+1,refIs the system reference track at time i +1, xi+1Is the system state at the (i + 1) th moment, and the constraint comprises:
xi+1＝Aixi+Biui,i＝0...k-1
Figure FDA0003208151620000052
Diui＝0,i＝0...k-1
wherein xiIs the system state at step i, uiIs a control input at step i, QiAnd RiIs a diagonal positive semi-definite matrix of weights, AiAnd BiRepresenting discrete time system dynamics, Ci,
Figure FDA0003208151620000053
c iInequality constraints representing control inputs, DiThe method comprises the steps of selecting a matrix of forces corresponding to the foot in the swing phase when the time step is i, and finding a control input sequence through optimization, wherein the control input sequence ensures that the robot system moves along xrefWith reference to the trajectory, a compromise is made between the control effect and the tracking progress.
5. The self-adaptive control method for the body posture slope of the quadruped bionic robot as claimed in claim 4, which is characterized in that: in the process of solving the optimized value of the objective function, the objective function is discretized by adopting an accurate discretization method, a zero-order retainer is used for sampling in the discretization process, sampling and discretization are carried out in a fixed time period, and the discretized objective function is converted into a quadratic programming problem to be solved.
6. The self-adaptive control method for the body posture slope of the quadruped bionic robot as claimed in claim 1, which is characterized in that: the specific process of setting a virtual slope is as follows: a two-dimensional plane formed by the front support foot end and the rear support foot end when the bionic robot moves from a flat road surface to a slope is used as a virtual slope.
7. An adaptive control system based on the body posture slope adaptive control method of the quadruped bionic robot as claimed in any one of claims 1 to 6, which is characterized in that: the method comprises the following steps:
the dynamic model building module is configured to build a bionic robot body and a world coordinate system, model the robot into a single rigid body, bear force at a contact point and build a robot dynamic model;
the model prediction control module is configured to calculate and track the track of the leg of the bionic robot in a world coordinate system, calculate the joint moment of the leg, determine an optimal control track by using model prediction control in consideration of the joint moment in the ground force control process, and control the motion of the bionic robot according to the optimal control track;
the foot end position adjusting module is configured to determine coordinate mapping of the foot end position according to the attitude parameter of the robot at the moment and adjust the gravity center position of the robot on the slope;
and the slope attitude adjusting module is configured to set a virtual slope, calculate the angle of the virtual slope in the current state according to the trunk pitch angle in the attitude parameter of the robot at the moment, and continuously adjust the attitude of the robot by taking the angle as the adjustment value of the pitch angle of the robot at the next moment until the virtual slope angle, the trunk pitch angle and the actual slope angle are equal.
8. A computer-readable storage medium characterized by: a plurality of instructions are stored, the instructions are suitable for being loaded by a processor of a terminal device and executing the body posture slope adaptive control method of the four-footed bionic robot as claimed in any one of claims 1-6.
9. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; the computer readable storage medium is used for storing a plurality of instructions which are suitable for being loaded by a processor and executing the body posture slope adaptive control method of the quadruped bionic robot as claimed in any one of claims 1-6.
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