CN111168672B - Robot motion planning method, system, computer device and storage medium - Google Patents
Robot motion planning method, system, computer device and storage medium Download PDFInfo
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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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/02—Sensing devices
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
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- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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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
- B25J9/1656—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
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Abstract
The application provides a robot motion planning method, a system, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring time-based step planning, and detecting the step information and phase information of the robot when the robot performs the time-based step planning; inputting the time base step planning into a Kalman filtering detector, and predicting an estimated contact state of the robot after the time base step planning is performed; inputting gait information and phase information into a Kalman filtering detector for detection to obtain the actual contact state of the robot; and triggering the event-based step planning of the robot motion according to the estimated contact state and the actual contact state. According to the method, the unknown complex environment is sensed according to the robot body, the basic gait plan of the event is triggered according to actual sensing and pre-measurement after sensing, and the correction gait is adjusted in time, so that the robot can self-adaptively adjust according to the body sensing to realize the autonomous walking on the complex unknown terrain, and the stable walking of the quadruped robot on the unknown terrain is guaranteed.
Description
Technical Field
The present application relates to the field of robot motion planning technologies, and in particular, to a robot motion planning method, system, computer device, and storage medium.
Background
The four-foot robot has natural advantages in the aspect of adapting to complex non-structural terrains due to the fact that the four-foot robot has a multi-joint and multi-degree-of-freedom leg supporting and moving mechanism.
The motion planning is a key for limiting the motion performance of the quadruped robot, and the core content of the motion planning is that the state phase and the body posture of four legs of the quadruped robot are planned in real time according to a motion target and the terrain environment where the robot is located, so that the quadruped robot can stably and continuously pass through the terrain where the quadruped robot is located on the basis of keeping balance.
However, for unknown complex location terrain, relying solely on time-based gait planning will not guarantee stable walking of a quadruped robot.
Disclosure of Invention
In view of the above, it is necessary to provide a robot motion planning method, system, computer device and storage medium for the above technical defects, especially the technical defect that time gait planning under unknown complex position terrain cannot guarantee stable walking of a quadruped robot.
A robot motion planning method comprises the following steps:
acquiring a time-based step planning, and detecting gait information and phase information of the robot when the robot executes the time-based step planning;
inputting the time base gait plan into a Kalman filtering detector, and predicting an estimated contact state of the robot after executing the time base gait plan;
inputting the gait information and the phase information into the Kalman filtering detector for detection to obtain the actual contact state of the robot;
and triggering the event-based step planning of the robot motion according to the estimated contact state and the actual contact state.
In one embodiment, the gait information includes contact points of the legs with the surrounding environment and foot end position information, and the phase information includes a body posture of the robot;
the step of detecting the gait information and the phase information of the robot when the robot executes the time-based gait planning comprises the following steps:
detecting a body attitude of the robot by an inertial measurement sensor of the robot; acquiring current change parameters of joint motors of the leg parts of the robot, acquiring contact information of the leg parts of the robot and the surrounding environment according to the current change parameters, and determining contact points of the leg parts and the surrounding environment according to the contact information; and detecting the position information of the foot end of the robot through an encoder of the joint motor.
In one embodiment, the kalman filter detector comprises a contact probability prediction model;
the step of inputting the time-based gait plan into a kalman filter detector to predict the estimated contact state of the robot after executing the time-based gait plan includes:
inputting the time-based gait plan into the contact probability prediction model, and predicting an estimated contact state of the robot after executing the time-based gait plan;
after the step of detecting the actual contact state of the robot, the method further comprises:
and updating the contact probability prediction model according to the actual contact state.
In one embodiment, the step of inputting the gait information and the phase information into the kalman filter detector for detection to obtain the actual contact state of the robot includes:
establishing a generalized momentum external force prediction model and a terrain height probability model according to contact points of legs and surrounding environment and foot end position information in the gait information and the body posture of the robot in the phase information, wherein the generalized momentum external force prediction model is used for describing the probability of a predicted external force value, and the terrain height probability model is used for describing the probability of a predicted terrain height; inputting the generalized momentum external force prediction model into a contact probability measurement model based on contact force to obtain the contact force of the robot; inputting the terrain height probability model into a contact probability measurement model based on height to obtain terrain height information; and obtaining the actual contact state of the robot according to the contact force of the robot and the terrain height information.
In one embodiment, the step of triggering event-based step planning based on the estimated contact state and the actual contact state includes:
when the actual contact state is consistent with the predicted contact state, carrying out lifting and placing switching of one leg; when the predicted contact state is earlier than the predicted contact state predicted by the time-based step state planning, ending the swing phase of the robot and entering a support phase of the robot; and when the predicted contact state lags behind the predicted contact state predicted by the time-based step planning, continuing the swinging phase of the robot until the contact state appears.
In one embodiment, after the step of triggering event-based step planning according to the estimated contact state and the actual contact state, the method further comprises:
and adjusting the self attitude and balance of the robot according to the ZMP stabilization criterion of the robot.
In one embodiment, after the step of obtaining the actual contact state of the robot according to the contact force of the robot and the terrain height information, the method further comprises:
correcting the time-based motion plan of the robot according to the contact force of the robot and the terrain height information; and calling the next time base step plan from the corrected time-based motion plan, executing the acquired time base step plan, and detecting the gait information and the phase information of the robot when the robot executes the time base step plan.
A robot motion planning system comprising:
the acquisition module is used for acquiring time-based step planning and detecting gait information and phase information of the robot when the robot executes the time-based step planning;
the prediction module is used for inputting the time base gait plan into a Kalman filtering detector and predicting an estimated contact state of the robot after the time base gait plan is executed;
the measuring module is used for inputting the gait information and the phase information into the Kalman filtering detector for detection to obtain the actual contact state of the robot;
and the triggering module is used for triggering the event-based step planning of the robot motion according to the estimated contact state and the actual contact state.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the robot motion planning method according to any of the embodiments when executing the computer program.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the robot motion planning method according to any of the embodiments described above.
According to the robot motion planning method, the system, the computer equipment and the storage medium, gait information and phase information after the robot performs the time-based step planning are detected, the contact state is predicted and calculated based on Kalman filtering, and the event-based step planning is triggered based on the contact event between the estimated contact state and the actual contact state so as to ensure that the robot stably walks; the gait can be adjusted or corrected in time according to the perception of the robot body on the unknown complex environment and the basic gait planning of the actual perception and the pre-measurement triggering event after the perception, so that the robot can realize the autonomous walking on the complex unknown terrain by self-adaptive adjustment according to the perception of the body, and the stable walking of the quadruped robot on the unknown terrain is guaranteed.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice.
Drawings
The foregoing and/or additional aspects and advantages will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is an implementation environment diagram of a robot motion planning method provided in an embodiment;
FIG. 2 is a flow diagram of a method for robot motion planning in one embodiment;
FIG. 3 is a schematic logic diagram of Kalman filter detector prediction and detection;
FIG. 4 is a flow diagram of Kalman filter detector detection and correction in one embodiment;
FIG. 5 is a diagram of trigger event based gait planning in one embodiment;
FIG. 6 is a schematic diagram of a system for planning the movement of a robot in an embodiment;
FIG. 7 is a diagram showing an internal configuration of a computer device according to an embodiment.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As shown in fig. 1, fig. 1 is a diagram of an implementation environment of a robot motion planning method provided in an embodiment, in which a robot 100 is included, the robot is composed of a body 110 and a plurality of legs 120, and joint motors are installed at the legs 121. The robot comprises a processor, the processor is connected with joint motors of all legs respectively, the processor obtains current and encoder data of the joint motors, the processor is further connected with an inertial measurement sensor, the processor is further connected with a memory, a Kalman filter detector is stored in the memory, and the Kalman filter detector comprises a contact probability prediction model for predicting a contact state, a contact probability measurement model for measuring the terrain height based on height and a contact probability measurement model for measuring an external force value based on contact force.
In an embodiment, as shown in fig. 2, fig. 2 is a flowchart of a robot motion planning method in an embodiment, where in this embodiment, a robot motion planning method is provided, and taking an example that the robot motion planning method is applied to the processor, the method may specifically include the following steps:
step S210: and acquiring the time-based step planning, and detecting the gait information and the phase information of the robot when the robot performs the time-based step planning.
And the gait planning based on time is based on time sequence.
Gait refers to the motion process of each leg of the robot according to a certain sequence and track. For example, translational gait refers to the robot always keeping the body translated when walking. The fixed point turning gait refers to the gait of the robot body rotating around a certain axis. Gait planning involves determining the onset and termination of the motion of the bearing phase, the swing phase. In order to maintain the stability of the body during the gait generation process, the robot is required to be ensured to be in a supporting phase state at least three feet during the walking process. Meanwhile, in order to ensure that the prototype has better stability in the walking process, the adjacent walking feet cannot be in the swing phase state at the same time, namely the adjacent feet of the robot cannot start to swing at the same time.
Actual parameters of the robot during executing gait planning are collected through a sensor and an electronic element on the robot, and gait information and phase information of the robot are detected from the actual parameters. The gait information may specifically include contact points of the leg with the surrounding environment and three-dimensional spatial position information of the foot end; the phase information may include a body attitude of the robot.
Specifically, in one embodiment, the gait information includes contact points of the legs with the surrounding environment and foot end position information, and the phase information includes a body posture of the robot. The step of detecting the gait information and the phase information of the robot during the execution time-based gait planning of the robot in the step S210 includes:
s211: the body attitude of the robot is detected by an inertial measurement sensor of the robot.
An Inertial Measurement Unit (IMU) is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object; generally, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, the accelerometers detect acceleration signals of an object in three independent axes of a carrier coordinate system, and the gyroscopes detect angular velocity signals of the carrier relative to a navigation coordinate system, and measure angular velocity and acceleration of the object in three-dimensional space, and then solve the attitude of the object.
An inertial measurement sensor provided in the robot detects an acceleration signal and an angular velocity signal of the robot, thereby detecting a body posture of the robot.
S212: the method comprises the steps of collecting current change parameters of joint motors of the legs of the robot, obtaining contact information of the legs of the robot and the surrounding environment according to the current change parameters, and determining contact points of the legs and the surrounding environment according to the contact information.
Collecting current change parameters of the joint motor, detecting contact information of the leg and the surrounding environment according to the current change parameters, and determining a contact point. And respectively detecting the contact information and the contact points of each leg through the current change in the joint motor of each leg.
For example, when the leg of the robot touches an object or the ground, the joint motor controls the brake of the leg, the current of the joint motor generates a change with corresponding characteristics, the contact of the leg of the robot can be determined according to the current change parameters of the joint motor, and a contact point is determined.
S213: and detecting the position information of the foot end of the robot through an encoder of the joint motor.
According to the control of the joint motor, parameters in an encoder of the joint motor are collected, particularly the lifting and swinging of the joint can be detected in the process from execution time-based gait planning to reaching of a contact point, and then the position and height of the leg end when the contact state is reached are determined, and further the position information of the leg end is determined.
The mode for detecting the gait information and the phase information of the robot utilizes the elements on the robot body to collect and detect the original parameters, so that the robot has body perception, does not need to rely on a sensor arranged in the external environment to detect the environment, and improves the self-adaptability of the robot.
Step S220: and inputting the time base step planning into a Kalman filtering detector, and predicting the estimated contact state of the robot after the time base step planning is performed.
The Kalman filter detector may predict the contact state based on a time-based gait plan. And inputting the time base gait plan into a Kalman filtering detector to obtain an estimated contact state of the robot after executing the time base gait plan. In one embodiment, as shown on the side of the contact state prediction in fig. 3, fig. 3 is a logic diagram of prediction and detection by a kalman filter detector, in which a contact probability prediction model predicts the contact state of a time-based gait plan.
Step S230: and inputting the gait information and the phase information into a Kalman filtering detector for detection to obtain the actual contact state of the robot.
The Kalman filtering detector is constructed based on a Kalman filter, and the influence of detection errors existing in gait information and phase information on an actual contact state can be reduced through the Kalman filter.
And determining the local terrain environment of the robot according to the gait information and the phase information detected by the robot and the actual contact state of the robot calculated by the Kalman filtering detector.
Further, in an embodiment, as shown in fig. 3 on the side of detecting the contact state, and as shown in fig. 4, fig. 4 is a flowchart of detection and correction of the kalman filter detector in an embodiment, and the step of inputting the gait information and the phase information into the kalman filter detector for detection in step S230 to obtain the actual contact state of the robot includes:
step S231: and establishing a generalized momentum external force prediction model and a terrain height probability model according to contact points of legs and surrounding environment and foot end position information in the gait information and the body posture of the robot in the phase information, wherein the generalized momentum external force prediction model is used for describing the probability of the predicted external force value, and the terrain height probability model is used for describing the probability of the predicted terrain height.
According to the contact point between the leg and the surrounding environment, the position information of the foot end and the body posture of the robot, the probability of the actual external force value can be predicted, a generalized momentum external force prediction model can be built, the probabilities of various terrain heights can be predicted, and a terrain height probability model can be built.
Step S232: and inputting the generalized momentum external force prediction model into a contact probability measurement model based on contact force to obtain the contact force of the robot.
And calculating the contact force of the robot by a contact probability measurement model based on the contact force in the Kalman filtering detector.
Step S233: and inputting the terrain height probability model into a contact probability measurement model based on the height to obtain the terrain height information.
And calculating terrain height information by a contact probability measurement model based on height in a Kalman filtering detector.
Step S234: and obtaining the actual contact state of the robot according to the contact force and the terrain height information of the robot.
The actual contact state of the robot is described by the contact force and the terrain height information of the robot, for example, information of the actual contact state of the robot is formed according to the contact force and the terrain height information of the robot.
Above-mentioned mode of detecting actual contact state relies on automobile body gesture, shank contact point and the foot end position information of the robot that the robot body detected, realizes the contact detection under robot shank and the environment of locating based on probability statistics and Kalman filtering, discerns local topography environmental characteristic adaptively for need not obtain any priori knowledge to the nature determination in the robot motion planning, can effectively adapt to the topography of unknown complicacy, promote the ability of robot self-adaptation walking.
The contact probability measurement model based on the contact force and the contact probability measurement model based on the height are respectively constructed based on a Kalman filter, the Kalman filter can reduce the measurement error generated by the resolution of an encoder and the accuracy of a generalized momentum external force prediction model depended by the current of a joint motor, and the Kalman filter realizes the correction of the contact state under various indirect measurements.
Step S240: and triggering event-based step planning of the robot motion according to the estimated contact state and the actual contact state.
The event-based gait planning is based on contact event gait planning, which is to maintain or change the current gait according to the current contact event. In the step, an event-based step planning of the robot motion is triggered according to the estimated contact state and the actual contact state, and the event-based step planning is used for maintaining the robot to walk stably. For example, when the actual contact state indicates that the robot is walking unstably, the current gait is corrected in time, and the self posture of the robot is adjusted to keep balance.
According to the robot motion planning method, gait information and phase information after the robot performs time-based gait planning are detected, the contact state is predicted and calculated based on Kalman filtering, and event-based gait planning is triggered based on the contact event between the estimated contact state and the actual contact state so as to ensure that the robot stably walks; the gait can be adjusted or corrected in time according to the perception of the robot body on the unknown complex environment and the basic gait planning of the actual perception and the pre-measurement triggering event after the perception, so that the robot can realize the autonomous walking on the complex unknown terrain by self-adaptive adjustment according to the perception of the body, and the stable walking of the quadruped robot on the unknown terrain is guaranteed.
In one embodiment, the step of triggering event-based step planning based on the estimated contact state and the actual contact state in step S240 includes:
s241: and when the actual contact state is consistent with the predicted contact state, carrying out lifting and placing switching of the single leg.
S242: and when the predicted contact state is earlier than the predicted contact state predicted by the time-based step state planning, ending the swing phase of the robot and entering the support phase of the robot.
S243: when the predicted contact state lags behind the predicted contact state predicted by the time-based gait plan, the swing phase of the robot is continued until the contact state occurs.
As shown in fig. 5, fig. 5 is a schematic diagram of triggering event-based gait planning in an embodiment, where the robot performs time-based gait planning, lifts the leg of the robot when a gait starts, predicts and estimates a contact state and detects an actual contact state through a kalman filter detector, determines whether a contact event is an early contact, a normal contact, or a late contact, and controls the lifted leg of the robot to land according to the corresponding event-based gait planning. After the lifting legs of the robot fall to the ground, the next gait is started.
According to the robot motion planning method, the time base gait planning is continuously maintained when the actual contact state is consistent with the predicted contact state, and the gait is corrected and adjusted in time when the actual contact state is inconsistent with the predicted contact state, so that the robot keeps stable and maintains the continuity of the gait, and the stable walking of the robot is realized.
In one embodiment, after the step of triggering event-based step planning based on the estimated contact state and the actual contact state, the method further comprises:
and adjusting the self attitude and balance of the robot according to the ZMP stabilization criterion of the robot.
In the ZMP (zero moment point) stabilization criterion, for a point on the ground, the horizontal component of the moment of gravity and inertial force to this point is zero. I.e. the forward, lateral overturning moment of the whole system for this point is zero. In this step, the robot is adjusted in posture based on the ZMP stabilization criterion to keep the robot balanced, for example, the posture of the leg joints and the body of the robot is adjusted.
In one embodiment, the kalman filter detector includes a contact probability prediction model;
in step S220, the step of inputting the time-based step planning into the kalman filter detector, and predicting the estimated contact state after the robot performs the time-based step planning includes:
and inputting the time base step planning into a contact probability prediction model, and predicting the estimated contact state of the robot after the time base step planning is performed.
After the step of detecting and obtaining the actual contact state of the robot, the method further comprises the following steps:
and updating the contact probability prediction model according to the actual contact state.
According to the robot motion planning method, the contact probability prediction model is updated according to the actual contact state, and the accuracy of the contact state prediction of the contact probability prediction model is improved.
In one embodiment, as shown in fig. 4, after the step of obtaining the actual contact state of the robot according to the contact force and the terrain height information of the robot in S234, the method further includes:
step S251: and correcting the time-based motion plan of the robot according to the contact force and the terrain height information of the robot.
And correcting the time motion plan in time according to the actual contact state corresponding to the executed gait, and updating the subsequent gait based on time.
Step S252: from the modified time-based motion plan, the next time-based gait plan is called, and step S210 is executed: and acquiring the time-based step planning and detecting the step information and the phase information of the robot when the robot performs the time-based step planning.
According to the robot motion planning method, the time motion planning is corrected according to the actual contact state, and the next gait planning which accords with the current environment is obtained for the robot to execute, so that the robot is kept to walk continuously.
In an embodiment, as shown in fig. 6, fig. 6 is a schematic structural diagram of a robot motion planning system in an embodiment, which may be applied to the processor shown in fig. 1, and the embodiment provides a robot motion planning system including an obtaining module 610, a predicting module 620, a measuring module 630, and a triggering module 640, where:
the obtaining module 610 is configured to obtain the time-based gait plan and detect gait information and phase information of the robot when the robot performs the time-based gait plan.
And the gait planning based on time is based on time sequence.
Gait refers to the motion process of each leg of the robot according to a certain sequence and track. For example, translational gait refers to the robot always keeping the body translated when walking. The fixed point turning gait refers to the gait of the robot body rotating around a certain axis. Gait planning involves determining the onset and termination of the motion of the bearing phase, the swing phase. In order to maintain the stability of the body during the gait generation process, the robot is required to be ensured to be in a supporting phase state at least three feet during the walking process. Meanwhile, in order to ensure that the prototype has better stability in the walking process, the adjacent walking feet cannot be in the swing phase state at the same time, namely the adjacent feet of the robot cannot start to swing at the same time.
The acquiring module 610 acquires actual parameters of the robot during executing gait planning through sensors and electronic elements on the robot, and detects gait information and phase information of the robot from the actual parameters. The gait information may specifically include contact points of the leg with the surrounding environment and three-dimensional spatial position information of the foot end; the phase information may include a body attitude of the robot.
Specifically, the obtaining module 610 is further configured to detect a body posture of the robot through an inertial measurement sensor of the robot; acquiring current change parameters of joint motors of the leg parts of the robot, acquiring contact information of the leg parts of the robot and the surrounding environment according to the current change parameters, and determining contact points of the leg parts and the surrounding environment according to the contact information; and detecting the position information of the foot end of the robot through an encoder of the joint motor.
An Inertial Measurement Unit (IMU) is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object; generally, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, the accelerometers detect acceleration signals of an object in three independent axes of a carrier coordinate system, and the gyroscopes detect angular velocity signals of the carrier relative to a navigation coordinate system, and measure angular velocity and acceleration of the object in three-dimensional space, and then solve the attitude of the object.
An inertial measurement sensor provided in the robot detects an acceleration signal and an angular velocity signal of the robot, thereby detecting a body posture of the robot.
Collecting current change parameters of the joint motor, detecting contact information of the leg and the surrounding environment according to the current change parameters, and determining a contact point. And respectively detecting the contact information and the contact points of each leg through the current change in the joint motor of each leg.
For example, when the leg of the robot touches an object or the ground, the joint motor controls the brake of the leg, the current of the joint motor generates a change with corresponding characteristics, the contact of the leg of the robot can be determined according to the current change parameters of the joint motor, and a contact point is determined.
According to the control of the joint motor, parameters in an encoder of the joint motor are collected, particularly the lifting and swinging of the joint can be detected in the process from execution time-based gait planning to reaching of a contact point, and then the position and height of the leg end when the contact state is reached are determined, and further the position information of the leg end is determined.
The acquisition module 610 acquires and detects original parameters by using elements on the robot body, so that the robot has body sensing, does not need to rely on a sensor arranged in an external environment to detect the environment, and improves the self-adaptability of the robot.
And the prediction module 620 is configured to input the time-based step planning into the kalman filter detector, and predict the estimated contact state of the robot after the time-based step planning is performed.
The Kalman filter detector may predict the contact state based on a time-based gait plan. And inputting the time base gait plan into a Kalman filtering detector to obtain an estimated contact state of the robot after executing the time base gait plan. In one embodiment, as shown on the side of the predicted contact state in FIG. 3, the contact probability prediction model in the Kalman filter detector predicts the contact state of the time-based gait plan.
Further, the prediction module 620 may be further configured to input the time-based step planning into the contact probability prediction model, and predict an estimated contact state after the robot performs the time-based step planning; and after the step of detecting and obtaining the actual contact state of the robot, updating the contact probability prediction model according to the actual contact state. And updating the contact probability prediction model according to the actual contact state, and improving the accuracy of the contact probability prediction model in contact state prediction.
And the measuring module 630 is configured to input the gait information and the phase information into the kalman filter detector for detection, so as to obtain an actual contact state of the robot.
The Kalman filtering detector is constructed based on a Kalman filter, and the influence of detection errors existing in gait information and phase information on an actual contact state can be reduced through the Kalman filter.
The measuring module 630 determines the local terrain environment of the robot according to the gait information and the phase information detected by the robot and the actual contact state of the robot calculated by the Kalman filtering detector.
Specifically, as shown in fig. 3 on the side of detecting the contact state, the measurement module 630 is further configured to establish a generalized external momentum force prediction model and a terrain height probability model according to the contact point of the leg with the surrounding environment and the foot end position information in the gait information and the body posture of the robot in the phase information, where the generalized external momentum force prediction model is used to describe the probability of the predicted external force value and the terrain height probability model is used to describe the probability of the predicted terrain height; inputting the generalized momentum external force prediction model into a contact probability measurement model based on contact force to obtain the contact force of the robot; inputting the terrain height probability model into a contact probability measurement model based on height to obtain terrain height information; and obtaining the actual contact state of the robot according to the contact force and the terrain height information of the robot.
According to the contact point between the leg and the surrounding environment, the position information of the foot end and the body posture of the robot, the probability of the actual external force value can be predicted, a generalized momentum external force prediction model can be built, the probabilities of various terrain heights can be predicted, and a terrain height probability model can be built. And calculating the contact force of the robot by a contact probability measurement model based on the contact force in the Kalman filtering detector. And calculating terrain height information by a contact probability measurement model based on height in a Kalman filtering detector.
The actual contact state of the robot is described by the contact force and the terrain height information of the robot, for example, information of the actual contact state of the robot is formed according to the contact force and the terrain height information of the robot.
Above-mentioned measuring module 630 relies on automobile body gesture, shank contact point and the foot end positional information of the robot that the robot body detected, realizes the contact detection under robot shank and the environment of locating based on probability statistics and kalman filter, discerns local topography environmental characteristic adaptively for need not obtain any priori knowledge to the nature determination in the robot motion planning, can effectively adapt to the topography of unknown complicacy, promote the ability of robot self-adaptation walking.
The contact probability measurement model based on the contact force and the contact probability measurement model based on the height are respectively constructed based on a Kalman filter, the Kalman filter can reduce the measurement error generated by the resolution of an encoder and the accuracy of a generalized momentum external force prediction model depended by the current of a joint motor, and the Kalman filter realizes the correction of the contact state under various indirect measurements.
Further, the measurement module 630 is further configured to modify the time-based motion plan of the robot according to the contact force and terrain height information of the robot; from the modified time-based motion plan, the next time-based gait plan is called and the acquisition module 610 is skipped to execute the acquisition of the time-based gait plan and to detect the gait information and the phase information of the robot when the robot executes the time-based gait plan.
The measurement module 630 modifies the time movement plan according to the actual contact state, and obtains the next gait plan conforming to the current environment for the robot to execute, so as to keep the robot walking continuously.
And a triggering module 640, configured to trigger event-based step planning of the robot motion according to the estimated contact state and the actual contact state.
The event-based gait planning is based on contact event gait planning, which is to maintain or change the current gait according to the current contact event. And the triggering module 640 is used for triggering the event-based step planning of the robot motion according to the estimated contact state and the actual contact state, wherein the event-based step planning is used for maintaining the robot to walk stably. For example, when the actual contact state indicates that the robot is walking unstably, the current gait is corrected in time, and the self posture of the robot is adjusted to keep balance.
Specifically, the triggering module 640 is further configured to perform single-leg lifting and releasing switching when the actual contact state is consistent with the predicted contact state; when the predicted contact state is earlier than the predicted contact state predicted by the time-based step state planning, ending the swing phase of the robot and entering the support phase of the robot; when the predicted contact state lags behind the predicted contact state predicted by the time-based gait plan, the swing phase of the robot is continued until the contact state occurs.
As shown in fig. 5, the robot performs time-based gait planning, lifts the leg when one gait starts, predicts and estimates the contact state and detects the actual contact state through the kalman filter detector, determines whether the contact event belongs to the advanced contact, the normal contact or the lagging contact, and controls the lifting leg of the robot to land according to the corresponding event-based gait planning. After the lifting legs of the robot fall to the ground, the next gait is started.
The triggering module 640 continues to maintain the time-based gait planning when the actual contact state is consistent with the predicted contact state, and corrects and adjusts the gait in time when the actual contact state is inconsistent with the predicted contact state, so that the robot keeps stable and maintains the continuity of the gait, and the robot stably walks.
Further, the triggering module 640 is also configured to adjust the robot's own pose and balance according to the robot ZMP stabilization criteria. In the ZMP (zero moment point) stabilization criterion, for a point on the ground, the horizontal component of the moment of gravity and inertial force to this point is zero. I.e. the forward, lateral overturning moment of the whole system for this point is zero. In this step, the robot is adjusted in posture based on the ZMP stabilization criterion to keep the robot balanced, for example, the posture of the leg joints and the body of the robot is adjusted.
The robot motion planning system predicts and calculates the contact state based on Kalman filtering by detecting the gait information and phase information of the robot after the robot performs the time-based gait planning, and triggers the event-based gait planning based on the contact event between the estimated contact state and the actual contact state to ensure the stable walking of the robot; the gait can be adjusted or corrected in time according to the perception of the robot body on the unknown complex environment and the basic gait planning of the actual perception and the pre-measurement triggering event after the perception, so that the robot can realize the autonomous walking on the complex unknown terrain by self-adaptive adjustment according to the perception of the body, and the stable walking of the quadruped robot on the unknown terrain is guaranteed.
For specific limitations of the robot motion planning system, reference may be made to the above limitations of the robot motion planning method, which are not described herein again. The modules in the robot motion planning system can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 7 is a schematic diagram of an internal structure of a computer device according to an embodiment, as shown in fig. 7. The computer device includes a processor, a non-volatile storage medium, a memory, and a network interface connected by a system bus. Wherein the non-volatile storage medium of the computer device stores an operating system, a database and a computer program, which, when executed by the processor, causes the processor to implement a method of robot motion planning. The processor of the computer device is used for providing calculation and control capability and supporting the operation of the whole computer device. The memory of the computer device may have stored therein a computer program which, when executed by the processor, causes the processor to perform a method of robot motion planning. The network interface of the computer device is used for connecting and communicating with the terminal. Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the present application proposes a computer device, which includes a memory, a processor and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps of the robot motion planning method in any of the above embodiments are implemented.
In one embodiment, a computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring a time-based step planning, and detecting gait information and phase information of the robot when the robot executes the time-based step planning; inputting the time base gait plan into a Kalman filtering detector, and predicting an estimated contact state of the robot after executing the time base gait plan; inputting the gait information and the phase information into the Kalman filtering detector for detection to obtain the actual contact state of the robot; and triggering the event-based step planning of the robot motion according to the estimated contact state and the actual contact state.
In one embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the steps of the robot motion planning method in any of the above embodiments.
In one embodiment, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of: acquiring a time-based step planning, and detecting gait information and phase information of the robot when the robot executes the time-based step planning; inputting the time base gait plan into a Kalman filtering detector, and predicting an estimated contact state of the robot after executing the time base gait plan; inputting the gait information and the phase information into the Kalman filtering detector for detection to obtain the actual contact state of the robot; and triggering the event-based step planning of the robot motion according to the estimated contact state and the actual contact state.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present application, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations should also be regarded as the protection scope of the present application.
Claims (9)
1. A robot motion planning method is characterized by comprising the following steps:
acquiring a time-based step planning, and detecting gait information and phase information of the robot when the robot executes the time-based step planning;
inputting the time base gait plan into a Kalman filtering detector, and predicting an estimated contact state of the robot after executing the time base gait plan;
inputting the gait information and the phase information into the Kalman filtering detector for detection to obtain the actual contact state of the robot;
triggering an event-based step planning of the robot motion according to the estimated contact state and the actual contact state;
the step of inputting the gait information and the phase information into the Kalman filtering detector for detection to obtain the actual contact state of the robot comprises the following steps: establishing a generalized momentum external force prediction model and a terrain height probability model according to contact points of legs and surrounding environment and foot end position information in the gait information and the body posture of the robot in the phase information, wherein the generalized momentum external force prediction model is used for describing the probability of a predicted external force value, and the terrain height probability model is used for describing the probability of a predicted terrain height; inputting the generalized momentum external force prediction model into a contact probability measurement model based on contact force to obtain the contact force of the robot; inputting the terrain height probability model into a contact probability measurement model based on height to obtain terrain height information; and obtaining the actual contact state of the robot according to the contact force of the robot and the terrain height information.
2. The robot motion planning method according to claim 1, wherein the gait information includes contact points of legs with the surrounding environment and foot end position information, and the phase information includes a body posture of the robot;
the step of detecting the gait information and the phase information of the robot when the robot executes the time-based gait planning comprises the following steps:
detecting a body attitude of the robot by an inertial measurement sensor of the robot;
acquiring current change parameters of joint motors of the leg parts of the robot, acquiring contact information of the leg parts of the robot and the surrounding environment according to the current change parameters, and determining contact points of the leg parts and the surrounding environment according to the contact information;
and detecting the position information of the foot end of the robot through an encoder of the joint motor.
3. The robot motion planning method of claim 1 wherein the kalman filter detector comprises a contact probability prediction model;
the step of inputting the time-based gait plan into a kalman filter detector to predict the estimated contact state of the robot after executing the time-based gait plan includes:
inputting the time-based gait plan into the contact probability prediction model, and predicting an estimated contact state of the robot after executing the time-based gait plan;
after the step of detecting the actual contact state of the robot, the method further comprises:
and updating the contact probability prediction model according to the actual contact state.
4. A robot motion planning method according to claim 1, wherein the step of triggering event-based step planning based on the estimated contact state and the actual contact state comprises:
when the actual contact state is consistent with the estimated contact state, carrying out single-leg lifting and placing switching;
when the actual contact state is ahead of the estimated contact state, ending the swing phase of the robot and entering a support phase of the robot;
when the actual contact state lags behind the estimated contact state, continuing the swing phase of the robot until the occurrence of the contact state.
5. The robot motion planning method of claim 1, further comprising, after the step of triggering event-based step planning based on the estimated contact state and actual contact state:
and adjusting the self attitude and balance of the robot according to the ZMP stabilization criterion of the robot.
6. The robot motion planning method according to claim 4, further comprising, after the step of obtaining the actual contact state of the robot from the contact force of the robot and the terrain height information:
correcting the time-based motion plan of the robot according to the contact force of the robot and the terrain height information;
and calling the next time base step plan from the corrected time-based motion plan, executing the acquired time base step plan, and detecting the gait information and the phase information of the robot when the robot executes the time base step plan.
7. A robot motion planning system, comprising:
the acquisition module is used for acquiring time-based step planning and detecting gait information and phase information of the robot when the robot executes the time-based step planning;
the prediction module is used for inputting the time base gait plan into a Kalman filtering detector and predicting an estimated contact state of the robot after the time base gait plan is executed;
the measuring module is used for inputting the gait information and the phase information into the Kalman filtering detector for detection to obtain the actual contact state of the robot;
the triggering module is used for triggering the event-based step planning of the robot motion according to the estimated contact state and the actual contact state;
wherein, the measurement module includes: establishing a generalized momentum external force prediction model and a terrain height probability model according to contact points of legs and surrounding environment and foot end position information in the gait information and the body posture of the robot in the phase information, wherein the generalized momentum external force prediction model is used for describing the probability of a predicted external force value, and the terrain height probability model is used for describing the probability of a predicted terrain height; inputting the generalized momentum external force prediction model into a contact probability measurement model based on contact force to obtain the contact force of the robot; inputting the terrain height probability model into a contact probability measurement model based on height to obtain terrain height information; and obtaining the actual contact state of the robot according to the contact force of the robot and the terrain height information.
8. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor realizes the steps of the robot motion planning method according to any of claims 1-6 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the robot motion planning method according to any one of claims 1 to 6.
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| CN112859851B (en) * | 2021-01-08 | 2023-02-21 | 广州视源电子科技股份有限公司 | Multi-legged robot control system and multi-legged robot |
| CN112478015B (en) * | 2021-02-03 | 2021-04-16 | 德鲁动力科技(成都)有限公司 | Four-footed robot foot end touchdown detection method and system |
| CN115157265A (en) * | 2022-08-03 | 2022-10-11 | 哈尔滨工业大学 | Crawling autonomous planning method for multi-arm robot in discontinuous environment |
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