CN109871018B - Control method of quadruped robot with waist degree of freedom - Google Patents
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Abstract
The invention discloses a control method of a quadruped robot with waist degree of freedom, and belongs to the field of bionic robots. Establishing a kinematics model and a dynamics model of the quadruped robot with waist freedom, calculating each gait parameter, matching with an inverse kinematics model for resolving to obtain an expected value of 13-dimensional motor rotation quantity of the quadruped robot, and respectively inputting the expected value into the dynamics model and a joint PD controller; the dynamic model calculates the real-time angles of 13 joints, the real-time angles are input into the dynamic model by combining the three-dimensional force data f of the sole of each leg, and the three-dimensional force data f is output as a torque vector set tau of each jointd(ii) a The joint PD controller feeds back data theta through the position of the motoractCalculating a moment vector τj. Collecting torque vector of each jointdAnd moment vector τjThe two are used as feedforward input of a dynamic model together to control the motor moment of the robot body, so that closed-loop control of an actuating mechanism is realized. The invention enables the robot to realize better control effect and control precision, and is easy to realize the positioning and repairing of the fault under the fault condition.
Description
Technical Field
The invention belongs to the field of bionic robots, relates to a robot control system and a control method, and particularly relates to a control method of a quadruped robot with waist freedom degree.
Background
The wheeled robot has good stability and high moving speed, but has poor adaptability to complex terrains, the wheeled and tracked moving tools can reach less than half of the land of the earth, and animals with leg structures can almost reach any place of the earth; based on the inspiration, the legged robot is developed rapidly, and the defect of poor terrain adaptability is overcome. Among legged robots, quadruped robots have been well developed due to their good stability, simpler structure and bionic characteristics. The most common structure of the quadruped robot is that each leg structure has 3 degrees of freedom, including 2 degrees of freedom of hip joints and 1 degree of freedom of knee joints, and the whole robot has 12 degrees of freedom, and an improved structure based on the structure has better performance.
The control method of the robot determines the performance of the robot; common control methods of a bionic robot are mainly classified into a model-based control method and a CPG (central pattern generator) -based control method. The control method based on the model comprises a control method based on a steady-state criterion, a control method based on a spring load inverted pendulum model, a control method based on a virtual model and a method of an inverse dynamic equation, and has the advantage of relatively accurate control. The CPG method simulates tissues of an animal generating rhythmic motion, and has a simple structure and strong robustness and adaptability. For the quadruped robot with the improved structure, the degree of freedom of a control object is higher, and the control is more complicated. Therefore, the proper method is selected to be applied to the quadruped robot with waist degree of freedom, and the movement effect with better performance can be realized.
Disclosure of Invention
The invention provides a control method of a quadruped robot with waist degree of freedom, which is simple, efficient and easy to realize, has high control precision, and designs a hardware control system for applying the control method to the robot.
The method comprises the following specific steps:
step one, establishing a kinematics model of a quadruped robot with waist degree of freedom;
the quadruped robot comprises 13 degrees of freedom and a two-layer controller;
the 13 degrees of freedom include 2 hip degrees of freedom and 1 knee degree of freedom for each leg, and one degree of freedom for the waist.
The two-layer controller comprises a motor controller on the top layer and a joint PD controller on the bottom layer;
the kinematic model comprises a positive kinematic model and an inverse kinematic model;
the positive kinematics model has the same value for each leg, taking the left front leg as an example:
wherein the content of the first and second substances,
for homogeneous coordinate transformation between the first hip joint and the base,
is a homogeneous coordinate transformation between the second hip joint and the first hip joint,
for homogeneous coordinate transformation between the second hip joint and the knee joint,
for the homogeneous coordinate transformation between the knee joint and the sole,
is a homogeneous coordinate transformation matrix between the tail end sole and the base;
then, the inverse kinematics model is solved on the basis of the positive kinematics model by a geometric method.
Step two, establishing a dynamic model of the quadruped robot with waist degree of freedom through a Newton-Euler method;
the kinetic model is as follows:
wherein m (q) represents an inertia matrix; q is a 19-dimensional state vector representing a 6-degree-of-freedom pedestal shapeAttitude, angle of 4 leg 12 joints, and angle of 1 waist joint.
Representing centripetal and coriolis forces, g (q) representing gravity,
is the transpose of the Jacobian matrix,
represents the external contact force, tau is a 13-dimensional joint moment vector, and S is a selection matrix.
Inputting the track instruction into a gait planning algorithm, and calculating specific gait parameters;
the gait parameters include: expected value x of base positionbaseplanAttitude expected value θbaseplanAnd expected value x of foot-drop point of legswingplanEtc.; aiming at different gait parameters, the gait parameter generation method is respectively used for obtaining the gait parameters.
Step four, inputting each gait parameter into the base controller and the leg controller respectively, and matching with an inverse kinematics model to carry out resolving to obtain an expected value theta of the 13-dimensional motor rotation quantity of the quadruped robotd；
First, the expected value x of the position of the base is determinedbaseplanAnd attitude expected value thetabaseplanThe actual position value x of the robot base is obtained by feedback in combination with inertial sensor integration and Vicon motion capture system joint calculation as input of the base controllerbaseactAnd three-dimensional actual attitude value theta of the base obtained by fusing data through the inertial sensorbaseactThe motor rotation amount theta of the base is output togetherposed；
Then, the expected value x of the foot-falling point of the leg is determinedswingplanThe feedback is used as the input of a leg controller, the integral fusion angle feedback of an inertial sensor and the joint calculation of a Vicon motion capture system are combined, and the feedback is obtained to obtain the position x of the actual foot-falling point of the robotfootactThe motor rotation amount theta of the leg portion is outputted togetherswingd。
Finally, vector augmentation fusion is carried out on the output data of the base controller and the output data of the leg controller to obtain an expected value theta of the rotation quantity of the 13-dimensional motord。
Step five, obtaining the expected value theta of the rotation quantity of the 13-dimensional motordRespectively inputting the dynamic model and the joint PD controller;
step six, calculating real-time angles of 13 joints by the dynamic model, inputting the real-time angles into the dynamic model by combining the collected sole three-dimensional force data f of each leg, and outputting the real-time angles to each joint moment vector set taud；
Step seven, the joint PD controller inputs the expected value theta of the rotation quantity of the 13-dimensional motordAnd motor position feedback data thetaactCalculating a moment vector τj。
Step eight, collecting torque vectors of all joints to form a TdAnd moment vector τjThe two are used as feedforward input of a dynamic model together to control the motor moment of the robot body, so that closed-loop control of an actuating mechanism is realized.
The invention has the advantages that:
1) the control method of the quadruped robot with the waist degree of freedom fully exerts the advantages of model control aiming at a novel quadruped robot structure with the waist degree of freedom. Compared with a virtual model control method and a CPG method, the robot control method combines a kinematics model and an inverse kinematics model, so that the robot can achieve better control effect and control precision.
2) The hardware architecture designed according to the method has the characteristics of modularization and distribution, the operation pressure of a top-layer processor is dispersed, and the positioning and repairing of faults under the fault condition are easier to realize.
Drawings
FIG. 1 is a schematic structural diagram of a quadruped robot with waist degree of freedom according to the present invention;
FIG. 2 is a diagram of the hardware architecture employed by the control method of the present invention;
FIG. 3 is a diagram of the overall control strategy of the control method of the present invention;
FIG. 4 is a flow chart of a control method of a quadruped robot with waist degree of freedom according to the present invention;
in the figure: 1-a rear base; 2-waist joint; 3-a front base; 4-thigh; 5-shank; 6-knee joint; 7-the first hip joint; 8-the second hip joint;
detailed description of the preferred embodiments
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The quadruped robot with waist degree of freedom designed by the invention comprises a rear base 1, a front base 3, a waist joint 2, thighs 4, calves 5, a knee joint 6, a first hip joint 7 and a second hip joint 8, as shown in figure 1;
the rear base 1 and the front base 3 are connected by a waist joint 2, and belong to a floating base system. Each leg has the same structure, taking the right front leg as an example, the rear base 3 and the thigh 4 are connected through two motor systems, namely a first hip joint 7 and a second hip joint 8, so that the hip joints have two degrees of freedom. The thigh 4 and the lower leg 5 are connected by a motor system of a knee joint 6. The end of the lower leg 5 is a rubber conical contact structure, has larger friction force and can be similar to point contact.
There are a total of 13 degrees of freedom in the structure, with each leg comprising 3 degrees of freedom and the waist joint 2 having one degree of freedom. Compared with the four-foot robot with the conventional structure, the four-foot robot with the structure increases a waist degree of freedom, has more diversified motion modes, and is convenient for realizing actions such as body lifting, standing and the like and jumping gait.
But the control object is more complicated due to the addition of one degree of freedom. In terms of hardware, as shown in fig. 2, the control system includes two layers: a top control system and a bottom control system; the top-layer control system comprises a microprocessor STM32F407, a wireless data transmission module, an inertial sensor module icm20602, a plantar force sensor, an angle sensor, a moment sensor and a flying steering engine SM80, wherein an arm microprocessor is arranged in the top-layer control system, and the top-layer control system has the functions of position feedback and moment feedback.
And the microprocessor STM32F407 is communicated with the wireless data transmission module, the inertial sensor, the moment sensor and the plantar force sensor through the SPI. The SPI is a full-duplex high-speed synchronous communication mode, only occupies 4 pins, and is suitable for high-speed communication between a microprocessor and a sensor.
The top layer control system comprises a specific control method which has a human-computer interaction function, receives human instructions and signals returned by the inertial sensor and the plantar force sensor, and sends control signals to the lower layer controller. The command signals reach the microprocessor through the wireless data transmission module, and the sensor data are fed back to the microprocessor STM32 through USART (synchronous/asynchronous serial receiver/transmitter) and SPI (serial peripheral interface). The number of control systems is the same as the number of actuators, and closed-loop control is performed on a single actuator. The input is the position and moment instruction of the top layer control system and the position of the sensor, and the moment feedback signal realizes the servo control of the motor through PD control.
The lower layer control system is 13 motor controllers and is communicated with the microprocessor through a USART. Because the number of USART pins of the control chip STM32 is limited, the invention adopts a serial bus to connect 13 lower layer control systems and the top layer control system, thereby avoiding the situation of insufficient pins. And each motor controller respectively controls each motor, and the control instruction is from the top layer controller. Each executing motor carries an angle sensor and a torque sensor, and position and torque data measured by the sensors are fed back to the microprocessor to realize servo control of the motor position and torque. The execution motor jointly controls the motion of the robot body, the robot can contact the ground to generate contact force in the motion process, the contact is regarded as point contact, the three-dimensional force of the sole can be measured through the force measuring platform or the torque sensor, and the three-dimensional force is fed back to the microprocessor and added into the top layer control system.
The control method of the quadruped robot with the waist degree of freedom, which is designed by the invention, adopts a method based on dynamics and inverse kinematics model control according to the idea of trajectory planning; firstly, a kinematic model and a dynamic model of the four-legged robot with 13 degrees of freedom are established, and the control of a base is decoupled from the control of legs. Inputting the track instruction into a control system, calculating according to a gait parameter generation algorithm to obtain specific gait parameters, calculating a real-time angle through inverse kinematics, inputting the angle and three-dimensional force of the sole into an inverse kinematics model, and performing real-time settlement on the moment of each joint. And further inputting the angle and torque data to the bottom controller to realize closed-loop control of the actuating mechanism. And finally, calculating the speed and the position by utilizing an inertial sensor through integral solution, measuring the absolute position by combining a Vicon motion capture system, and using the absolute position as a feedback input controller to realize the position closed-loop control of the quadruped robot with the waist along the base and the foot end of the track.
As shown in fig. 4, the specific steps are as follows:
step one, establishing a kinematics model of a quadruped robot with waist degree of freedom;
the quadruped robot comprises 13 degrees of freedom and a two-layer controller;
the 13 degrees of freedom include 2 hip degrees of freedom and 1 knee degree of freedom for each leg, and one degree of freedom for the waist.
The two-layer controller comprises a motor controller on the top layer and a joint PD controller on the bottom layer; in the control framework, the joint PD controller is positioned in a bottom-layer control system, and the rest controllers are positioned in a top-layer control system.
The kinematic model comprises a positive kinematic model and an inverse kinematic model;
the positive kinematics model has the same value for each leg, taking the left front leg as an example:
wherein the content of the first and second substances,
for homogeneous coordinate transformation between the first hip joint and the base,
is a homogeneous coordinate transformation between the second hip joint and the first hip joint,
for homogeneous coordinate transformation between the second hip joint and the knee joint,
for the homogeneous coordinate transformation between the knee joint and the sole,
is a homogeneous coordinate transformation matrix between the tail end sole and the base; the positive kinematics model of the robot can be obtained by obtaining the matrix, and then the inverse kinematics model is solved on the basis of the positive kinematics model by a geometric method.
Step two, establishing a dynamic model of the quadruped robot with waist degree of freedom through a Newton-Euler method;
the kinetic model is as follows:
wherein m (q) represents an inertia matrix; q is a 19-dimensional state vector representing the 6-degree-of-freedom base state, the angle of the 4 leg 12 joints, and the angle of the 1 waist joint.
Representing centripetal and coriolis forces, g (q) representing gravity,
is the transpose of the Jacobian matrix,
represents the external contact force, tau is a 13-dimensional joint moment vector, and S is a selection matrix.
Inputting the track instruction into a gait planning algorithm, and calculating specific gait parameters;
as shown in fig. 3, the gait parameters include: expected value x of base positionbaseplanAttitude anticipationValue thetabaseplanAnd expected value x of foot-drop point of legswingplanEtc.; the gait planning algorithm needs to be designed in advance according to different expected gaits (crawling gaits, diagonal gaits and jumping gaits), and the function is to convert the track instruction into expected values of the base position posture and the foot drop point. Aiming at different gait parameters, the gait parameter generation method is respectively used for obtaining the gait parameters.
Inputting each gait parameter into the base controller and the leg controller respectively, matching with an inverse kinematics model to carry out resolving, and decoupling the base control and the leg control; obtaining the expected value theta of the 13-dimensional motor rotation quantity of the quadruped robotd；
As shown in FIG. 3, first, the expected value x of the position of the susceptor is determinedbaseplanAnd attitude expected value thetabaseplanThe actual position value x of the robot base is obtained by feedback in combination with inertial sensor integration and Vicon motion capture system joint calculation as input of the base controllerbaseactAnd three-dimensional actual attitude value theta of the base obtained by fusing data through the inertial sensorbaseactThe motor rotation amount theta of the base is output togetherposed；
Then, the expected value x of the foot-falling point of the leg is determinedswingplanThe feedback is used as the input of a leg controller, the integral fusion angle feedback of an inertial sensor and the joint calculation of a Vicon motion capture system are combined, and the feedback is obtained to obtain the position x of the actual foot-falling point of the robotfootactThe motor rotation amount theta of the leg portion is outputted togetherswingd。
Finally, vector augmentation fusion is carried out on the output data of the base controller and the output data of the leg controller to obtain an expected value theta of the rotation quantity of the 13-dimensional motord。
Step five, obtaining the expected value theta of the rotation quantity of the 13-dimensional motordRespectively inputting the dynamic model and the joint PD controller;
as shown in fig. 3, the desired value θ of the rotation amount of the 13 d motordRespectively inputting two controllers, one is a dynamic controller, inputting 3-dimensional sole force acquisition data f of each leg, and outputting the data as moment vectors tau of each jointd. The other isA joint PD controller for inputting a desired value thetadAnd motor position feedback data thetaactCalculating a moment vector τj. Two output torque vectors taudAnd τjThe two input signals are used as feedforward input solved by an inverse dynamic model together to control the motor moment of the robot body.
Step six, calculating real-time angles of 13 joints by the dynamic model, inputting the real-time angles into the dynamic model by combining the collected sole three-dimensional force data f of each leg, and outputting the real-time angles to each joint moment vector set taud；
Step seven, the joint PD controller inputs the expected value theta of the rotation quantity of the 13-dimensional motordAnd motor position feedback data thetaactCalculating a moment vector τj。
Step eight, collecting torque vectors of all joints to form a TdAnd moment vector τjThe two are used as feedforward input of a dynamic model together to control the motor moment of the robot body, so that closed-loop control of an actuating mechanism is realized.
Claims (2)
1. A control method of a quadruped robot with waist degree of freedom is characterized by comprising the following specific steps:
step one, establishing a kinematics model of a quadruped robot with waist degree of freedom;
the quadruped robot comprises 13 degrees of freedom and a two-layer controller;
the 13 degrees of freedom include 2 hip joint degrees of freedom and 1 knee joint degree of freedom for each leg, and one degree of freedom for the waist;
the two-layer controller comprises a motor controller on the top layer and a joint PD controller on the bottom layer;
the kinematic model comprises a positive kinematic model and an inverse kinematic model;
step two, establishing a dynamic model of the quadruped robot with waist degree of freedom through a Newton-Euler method;
the kinetic model is as follows:
wherein m (q) represents an inertia matrix; q is a 19-dimensional state vector representing a 6-degree-of-freedom base state, angles of 4 leg 12 joints, and angles of 1 waist joint;
representing centripetal and coriolis forces, g (q) representing gravity,
is the transpose of the Jacobian matrix,
representing external contact force, wherein tau is a 13-dimensional joint moment vector, and S is a selection matrix;
inputting the track instruction into a gait planning algorithm, and calculating specific gait parameters;
the gait parameters include: expected value x of base positionbaseplanAttitude expected value θbaseplanAnd expected value x of foot-drop point of legswingplan(ii) a Aiming at different gait parameters, different gait parameter generation methods are respectively used for obtaining the gait parameters;
step four, inputting each gait parameter into the base controller and the leg controller respectively, and matching with an inverse kinematics model to carry out resolving to obtain an expected value theta of the 13-dimensional motor rotation quantity of the quadruped robotd；
First, the expected value x of the position of the base is determinedbaseplanAnd attitude expected value thetabaseplanThe actual position value x of the robot base is obtained by feedback in combination with inertial sensor integration and Vicon motion capture system joint calculation as input of the base controllerbaseactAnd three-dimensional actual attitude value theta of the base obtained by fusing data through the inertial sensorbaseactThe motor rotation amount theta of the base is output togetherposed；
Then, the expected value x of the foot-falling point of the leg is determinedswingplanAs an input to the leg controller,the actual foot-falling point position x of the robot obtained by feedback in combination with inertial sensor integral fusion angle feedback and Vicon motion capture system joint calculationfootactThe motor rotation amount theta of the leg portion is outputted togetherswingd；
Finally, vector augmentation fusion is carried out on the output data of the base controller and the output data of the leg controller to obtain an expected value theta of the rotation quantity of the 13-dimensional motord；
Step five, obtaining the expected value theta of the rotation quantity of the 13-dimensional motordRespectively inputting the dynamic model and the joint PD controller;
step six, calculating real-time angles of 13 joints by the dynamic model, inputting the real-time angles into the dynamic model by combining the collected sole three-dimensional force data f of each leg, and outputting the real-time angles to each joint moment vector set taud；
Step seven, the joint PD controller inputs the expected value theta of the rotation quantity of the 13-dimensional motordAnd motor position feedback data thetaactCalculating a moment vector τj；
Step eight, collecting torque vectors of all joints to form a TdAnd moment vector τjThe two are used as feedforward input of a dynamic model together to control the motor moment of the robot body, so that closed-loop control of an actuating mechanism is realized.
2. The method as claimed in claim 1, wherein the positive kinematics model in step one has the same leg values, taking the left front leg as an example:
wherein the content of the first and second substances,
for homogeneous coordinate transformation between the first hip joint and the base,
is a homogeneous coordinate transformation between the second hip joint and the first hip joint,
for homogeneous coordinate transformation between the second hip joint and the knee joint,
for the homogeneous coordinate transformation between the knee joint and the sole,
is a homogeneous coordinate transformation matrix between the tail end sole and the base;
then, the inverse kinematics model is solved on the basis of the positive kinematics model by a geometric method.
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| CN113220004A (en) * | 2021-04-15 | 2021-08-06 | 海南大熊软件科技有限公司 | Gait control method for quadruped robot, and computer-readable storage medium |
| CN113033027B (en) * | 2021-05-18 | 2021-11-02 | 追觅创新科技(苏州)有限公司 | Gait control method and device of intelligent robot, storage medium and electronic device |
| CN113031450B (en) * | 2021-05-18 | 2022-03-29 | 追觅创新科技(苏州)有限公司 | Feedforward control method and device for intelligent robot, storage medium and electronic device |
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| CN114115310B (en) * | 2021-11-19 | 2023-12-15 | 北京理工大学 | Motion control method and system for four-foot robot |
| CN114734445B (en) * | 2022-05-09 | 2024-06-28 | 中兵智能创新研究院有限公司 | Control method for dynamically adapting to load of four-foot robot |
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