Multi-limb exoskeleton walking balance control method, device and equipment and storage medium

Abstract

The invention relates to the technical field of exoskeleton robots, in particular to a multi-limb exoskeleton walking balance control method, device and equipment and a storage medium. The zero moment point coordinate of the wearer can be accurately calculated by acquiring the sensing information, the stable supporting polygonal area is determined, when the zero moment point is not in the stable supporting polygonal area, the balance auxiliary mode can be executed according to the phase state of the robot, the safety of the wearer is ensured, and the safety of the wearer is improved. According to the method, the phase state of the robot is obtained according to the walking state and the environment of the wearer in the sensing information, the balance state of the wearer is accurately judged, the robot can quickly make a balance strategy according to the gravity center change of the wearer, the calculation process is simple, the requirement for software and hardware of a control system is low, cost and technical difficulty are reduced, and the method is suitable for popularization and application. No matter in daily life or in a special working environment, reliable balance assistance can be provided for a wearer, and the safety and stability of actions are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of exoskeleton robots, and in particular to a multi-limb exoskeleton walking balance control method, device, equipment and storage medium. Background Art

[0002] As a wearable assistive tool, exoskeleton robots are increasingly being used to assist the disabled in walking. Considering factors such as the robot's size, weight and cost, most exoskeleton robots currently adopt a combination of active and passive forms, only assisting the wearer at specific joints, and it is difficult for the robot to maintain balance on its own. In addition, due to the deficiencies in physical function, perception, judgment and reaction abilities of the disabled, most exoskeleton robots are difficult to meet the wearer's walking balance requirements. Summary of the invention

[0003] In order to solve the above-mentioned shortcomings in the prior art, the present invention proposes a multi-limb exoskeleton walking balance control method, device, equipment and storage medium.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows: A multi-limb exoskeleton walking balance control method includes: obtaining sensor information from various sensors; calculating the overall zero moment point coordinates according to the sensor information; calculating the stable support polygon area according to the sensor information and the area definition method; judging whether the zero moment point coordinates are within the stable support polygon area; obtaining the phase state of the robot; when the zero moment point coordinates are not within the stable support polygon area, controlling the robot to execute a balance assistance mode according to the phase state. By obtaining the sensor information, the zero moment point coordinates of the wearer can be accurately calculated, and the stable support polygon area can be determined. When the zero moment point is not within the stable support polygon area, the balance assistance mode can be executed according to the robot phase state to ensure the safety of the wearer. The method obtains the robot phase state based on the wearer's walking state and the environment in the sensor information, and accurately judges the wearer's balance state, so that the robot can quickly make a balance strategy according to the wearer's center of gravity change. In addition, the calculation process is simple, the control system software and hardware requirements are low, and the cost and technical difficulty are reduced. Whether in daily life or special working environments, reliable balance assistance can be provided to the wearer to improve the safety and stability of the action.

[0005] Furthermore, the method of calculating the overall zero moment point coordinates based on the sensing information includes: obtaining the wearer's weight parameter from the sensing information to obtain a first weight parameter; converting the first weight parameter to each joint component according to the set parameter to obtain a second weight parameter; calculating the position coordinates of each joint component according to the sensing information; calculating the overall center of gravity coordinates according to the position coordinates and the second weight parameter; and calculating the overall zero moment point coordinates according to the sensing information, the position coordinates and the overall center of gravity coordinates.

[0006] The weight parameters of the wearer are accurately obtained from the sensor information to obtain the first weight parameter, and the first weight parameter is converted to each joint component according to the set parameters to form the second weight parameter, and the position coordinates of each joint component are obtained. The overall center of gravity coordinates are calculated according to the position coordinates and the second weight parameter, achieving the accuracy and scientificity of data acquisition and calculation, and can track the changes in the center of gravity of the wearer and the robot according to the individual differences of the wearer and different postures and activities; then, with the help of the position coordinates of each joint component, the overall center of gravity coordinates and sensor information, the zero torque point coordinates are obtained through scientific calculation. This process realizes the accurate grasp and effective use of relevant important parameters, and can provide a strong basis for the wearer's balance state assessment and movement posture adjustment during activities.

[0007] Furthermore, the stable support polygon area is calculated based on the sensor information and the area definition method, including: obtaining the coordinates of the robot's contact point with the ground from the sensor information to obtain the ground contact point coordinates; and calculating the stable support polygon area based on the ground contact point coordinates and the area definition method. By accurately determining the specific area related to the robot, a clear spatial range definition is provided for subsequent analysis. Secondly, this stable support polygon area can be used as an important reference. For example, when evaluating the stability of the robot, it can be compared with the actual support state, which helps to timely discover potential imbalance risks.

[0008] Furthermore, when the zero moment point coordinates are not within the stable support polygonal region, the robot is controlled to execute a balance assist mode according to the phase state, including: When the coordinates of the zero moment point are not within the stable support polygonal region, the phase state is judged as a type, the phase state includes a single-leg support phase and a double-leg support phase, and the balance assistance mode includes a single-leg support balance assistance mode and a double-leg support balance assistance mode; When the phase state is the single-leg support phase, the robot is controlled to execute the single-leg support balance assist mode; When the phase state is the double-leg support phase, the robot is controlled to execute the double-leg support balance assist mode.

[0009] By judging the relationship between the coordinates of the zero-torque point and the stable support polygon area, and judging the type of the phase state, the two states of single-leg support and double-leg support and the corresponding balance assistance modes are clarified. This can accurately control the robot to adopt the corresponding assistance mode in different situations, improve the robot's adaptability to different support states, and respond to possible imbalance risks in a timely manner, ensuring that the robot remains stable in various motion states, improving the safety and reliability of the robot's actions, and providing an effective balance assurance strategy for the actual application of the robot.

[0010] Furthermore, when the phase state is the single-leg support phase, the robot is controlled to execute the single-leg support balance assist mode, including predicting the overall center of gravity motion trajectory according to the single-leg support phase, position coordinates, overall center of gravity coordinates and kinematic model when the phase state is the single-leg support phase; calculating the velocity vector of the overall center of gravity according to the overall center of gravity motion trajectory to obtain a first velocity vector; converting the first velocity vector into a direction angle to obtain a first motion direction; obtaining the angle between the wearer's upper body and legs in the sensor information to obtain a first tilt angle; calculating the angle between the first motion direction and the ground to obtain a second angle; and judging whether the first tilt angle exceeds a preset first threshold ; Determine whether the second angle exceeds a preset second threshold; when the first tipping angle exceeds the first threshold and the second angle exceeds the second threshold, generate a judgment result that there is a risk of tipping to obtain a first judgment result; analyze the first judgment result; when there is a risk of tipping, determine whether the first tipping angle exceeds a preset third threshold; when the first tipping angle exceeds the third threshold, control the swing leg to fall, and calculate the first tipping direction based on the first tipping angle, and control the free limb to move in the first tipping direction; when the first tipping angle does not exceed the third threshold, calculate the second tipping direction based on the first tipping angle, and control the free limb to move in the second tipping direction. In the single-leg support phase, through a series of operations such as kinematic models, the overall center of gravity motion trajectory and related parameters, such as velocity vector, direction angle, etc., can be accurately predicted. The key angle can also be obtained. By comparing and judging with the preset threshold, the tipping risk results can be generated and analyzed in time; according to the relationship between the tipping angle and different thresholds, the swinging leg and free limb movements can be controlled in a targeted manner, which effectively improves the monitoring accuracy of the wearer's balance state, and can predict and accurately respond to tipping risks in advance, ensuring the safety and stability of the wearer's movements.

[0011] Furthermore, when the phase state is the double-leg support phase, the robot is controlled to execute the double-leg support balance assist mode, including: when the phase state is the double-leg support phase, the angle between the wearer's upper body and legs in the sensor information is obtained to obtain the second tilting angle; the third tilting direction is calculated according to the second tilting angle; it is determined whether the third tilting direction is the forward tilting direction, and a second judgment result is generated; when the third tilting direction is the forward tilting direction, the supporting leg is controlled to step forward; when the third tilting direction is not the forward tilting direction, the free limb is controlled to move to the rear side of the wearer for support; according to the second judgment result, a dynamic balance control instruction is generated to adjust the zero moment point coordinates within the support range. By obtaining the angle between the wearer's upper body and legs to obtain the second tilting angle and predicting the third tilting direction, it is possible to accurately determine whether to tilt forward and generate a second judgment result. Accordingly, when tilting forward, the supporting leg is controlled to step, and when not tilting forward, the rear side of the free limb is allowed to move for support, thereby achieving targeted balance control. Finally, dynamic balance control instructions are generated based on the judgment results, which effectively adjust the coordinates of the zero-torque point within the support range, greatly improving the wearer's balance stability and safety when supported by both legs.

[0012] Furthermore, the multi-limb exoskeleton walking balance control device comprises: A sensor information acquisition module is used to obtain sensor information from various sensors; The zero moment point coordinate calculation module is used to calculate the overall zero moment point coordinates according to the sensor information; the stable support polygon area calculation module is used to calculate the stable support polygon area according to the sensor information and the area definition method; the coordinate position judgment module is used to judge whether the zero moment point coordinates are within the stable support polygon area; the phase state acquisition module is used to obtain the phase state of the robot; the balance assistance mode execution module is used to control the robot to execute the balance assistance mode according to the phase state when the zero moment point coordinates are not within the stable support polygon area. The zero moment point coordinate calculation module obtains the overall zero moment point coordinates according to the sensor information; the stable support polygon area calculation module determines the stable support polygon area; the coordinate position judgment module can accurately judge the relationship between the zero moment point coordinates and the stable support polygon area; the robot phase state is mastered through the phase state acquisition module; the balance assistance mode execution module controls the robot to execute the balance assistance mode according to the phase state when the zero moment point is not within the area; the overall accurate judgment and rapid response to the wearer's balance state are achieved, the calculation is simple and the requirements are low, and it can provide reliable balance assistance for the wearer in a variety of environments, improving the safety and stability of the action.

[0013] Further, a multi-limb exoskeleton walking balance control device, the multi-limb exoskeleton walking balance control device comprises: a memory and at least one processor, the memory storing instructions; At least one of the processors calls the instructions in the memory to enable the multi-limb exoskeleton walking balance control device to perform each step of the multi-limb exoskeleton walking balance control method as described in any one of the above.

[0014] Furthermore, a computer-readable storage medium is provided, on which instructions are stored, wherein when the instructions are executed by a processor, each step of the multi-limb exoskeleton walking balance control method as described in any one of the above is implemented.

[0015] The beneficial effects of the multi-limb exoskeleton walking balance control method of the present invention are: By acquiring the sensor information, the coordinates of the wearer's zero moment point can be accurately calculated, and the stable support polygon area can be determined. When the zero moment point is not within the stable support polygon area, the balance assistance mode can be executed according to the robot's phase state to ensure the wearer's safety. This method derives the robot's phase state based on the wearer's walking state and the environment in the sensor information, and accurately judges the wearer's balance state, so that the robot can quickly make a balance strategy according to the wearer's center of gravity change. In addition, the calculation process is simple, and the control system hardware and software requirements are low, which reduces the cost and technical difficulty. Whether in daily life or special working environments, it can provide the wearer with reliable balance assistance and improve the safety and stability of actions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A first flow chart of a multi-limb exoskeleton walking balance control method provided by an embodiment of the present invention; Figure 2 A second flow chart of the multi-limb exoskeleton walking balance control method provided by an embodiment of the present invention; Figure 3 A third flow chart of the multi-limb exoskeleton walking balance control method provided by an embodiment of the present invention; Figure 4 A fourth flow chart of a multi-limb exoskeleton walking balance control method provided by an embodiment of the present invention; Figure 5 A fifth flow chart of the multi-limb exoskeleton walking balance control method provided by an embodiment of the present invention; Figure 6 A sixth flow chart of a multi-limb exoskeleton walking balance control method provided by an embodiment of the present invention; Figure 7 A schematic diagram of a structure of a multi-limb exoskeleton walking balance control device provided by an embodiment of the present invention; Figure 8 A schematic diagram of the structure of a multi-limb exoskeleton walking balance control device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the multi-limb exoskeleton walking balance control method in an embodiment of the present invention includes: A multi-limb exoskeleton walking balance control method, comprising: 101: Obtain sensor information from various sensors; 102: Calculate the overall zero moment point coordinates according to the sensing information; 103: Calculate the stable support polygonal area according to the sensing information and the area definition method; 104: Determine whether the coordinates of the zero moment point are within the stable support polygon area; 105: Get the phase state of the robot; 106: When the zero moment point coordinates are not within the stable support polygon area, the robot is controlled to execute the balance assist mode according to the phase state.

[0020] In this embodiment, the sensor types involved include vision sensors, force and torque sensors, pressure sensors, joint angle sensors, ground contact sensors, speed sensors, weighing sensors, acceleration sensors, odometers (encoders), torque sensors, force and torque sensors, rotary transformers, photoelectric encoders, Hall sensors, position and posture sensors, etc.

[0021] By acquiring the sensor information, the coordinates of the wearer's zero moment point can be accurately calculated, and the stable support polygon area can be determined. When the zero moment point is not within the stable support polygon area, the balance assistance mode can be executed according to the robot's phase state to ensure the wearer's safety. This method derives the robot's phase state based on the wearer's walking state and the environment in the sensor information, and accurately judges the wearer's balance state, so that the robot can quickly make a balance strategy according to the wearer's center of gravity change. In addition, the calculation process is simple, and the control system hardware and software requirements are low, which reduces the cost and technical difficulty. Whether in daily life or special working environments, it can provide the wearer with reliable balance assistance and improve the safety and stability of actions.

[0022] See also Figure 2 The second embodiment of the multi-limb exoskeleton walking balance control method in the embodiment of the present invention includes: 201: Obtaining a weight parameter of the wearer from the sensing information to obtain a first weight parameter; 202: converting the first weight parameter to each joint component according to the set parameter to obtain a second weight parameter; 203: Calculate the position coordinates of each joint component according to the sensor information; 204: Calculate the coordinates of the overall center of gravity according to the position coordinates and the second weight parameter; 205: Calculate the overall zero moment point coordinates according to the sensor information, the position coordinates and the overall center of gravity coordinates.

[0023] In this embodiment, the formula type for calculating the overall center of gravity coordinates is a center of gravity calculation formula, such as: ,in, is the weight parameter of the wearer’s body, is the position coordinate of each joint component, is the second weight parameter, is the coordinate of the center of gravity of the whole body. The coordinate of the zero moment point can be calculated by using the kinematic equation, including the force balance equation, such as , in is the overall mass parameter of the robot and the wearer calculated through sensor information, is the acceleration vector of gravity, is the coordinate of the wearer's overall center of gravity, , is the coordinate of the overall zero moment point.

[0024] The weight parameters of the wearer are accurately obtained from the sensor information to obtain the first weight parameter, and the first weight parameter is converted to each joint component according to the set parameters to form the second weight parameter, and the position coordinates of each joint component are obtained. The overall center of gravity coordinates are calculated according to the position coordinates and the second weight parameter, achieving the accuracy and scientificity of data acquisition and calculation, and can track the changes in the center of gravity of the wearer and the robot according to the individual differences of the wearer and different postures and activities; then, with the help of the position coordinates of each joint component, the overall center of gravity coordinates and sensor information, the zero torque point coordinates are obtained through scientific calculation. This process realizes the accurate grasp and effective use of relevant important parameters, and can provide a strong basis for the wearer's balance state assessment and movement posture adjustment during activities.

[0025] See also Figure 3 , a third embodiment of the multi-limb exoskeleton walking balance control method in the embodiment of the present invention includes: 301: Obtaining the coordinates of the contact point between the robot and the ground from the sensor information to obtain the coordinates of the contact point on the ground; 302: A stable support polygonal area is calculated based on the coordinates of the ground contact point and the area definition method.

[0026] In this implementation, the region definition methods used in the calculation of the stable supporting polygon region include convex hull algorithm, concave polygon generation algorithm and definition method based on geometric rules, etc. For example, the definition method based on geometric rules used below, for a convex polygon, its internal region can be represented by a set of linear inequalities; taking a simple two-dimensional convex polygon as an example, let its vertex coordinates be , ..., , for each side of the polygon, we can find the equation of the corresponding line (in, , , is the coefficient determined by the coordinates of two points on the line), and then based on the principle that the line divides the plane into two regions, by judging which side of the line the point is on, an inequality is constructed. If for one side, the corresponding line equation is , then the points inside the convex polygon satisfy (or , depending on which side of the line the point is on), for the entire convex polygon, its interior area can be expressed by a set of such inequalities.

[0027] By accurately determining the specific areas related to the robot, a clear spatial scope is provided for subsequent analysis. Secondly, this stable support polygon area can be used as an important reference. For example, when evaluating the stability of the robot, it can be compared with the actual support status, which helps to detect potential imbalance risks in a timely manner.

[0028] See also Figure 4 The fourth embodiment of the multi-limb exoskeleton walking balance control method in the embodiment of the present invention includes: 401: When the zero moment point coordinates are not within the stable support polygonal region, the phase state is judged as a type, the phase state includes a single-leg support phase and a double-leg support phase, and the balance assist mode includes a single-leg support balance assist mode and a double-leg support balance assist mode; 402: When the phase state is the single-leg support phase, the robot is controlled to execute the single-leg support balance assist mode; 403: When the phase state is the double-leg support phase, the robot is controlled to execute the double-leg support balance assist mode.

[0029] In this embodiment, the single-leg support balance assistance mode is a mode in which the robot's multiple assistive limbs assist the wearer in maintaining balance in a single-leg support state; The double-leg support balance assist mode is a mode in which the robot's multiple assistive limbs assist the wearer in maintaining balance in a double-leg support state.

[0030] By judging the relationship between the coordinates of the zero-torque point and the stable support polygon area, and judging the type of the phase state, the two states of single-leg support and double-leg support and the corresponding balance assistance modes are clarified. This can accurately control the robot to adopt the corresponding assistance mode in different situations, improve the robot's adaptability to different support states, and respond to possible imbalance risks in a timely manner, ensuring that the robot remains stable in various motion states, improving the safety and reliability of the robot's actions, and providing an effective balance assurance strategy for the actual application of the robot.

[0031] See also Figure 5 The fifth embodiment of the multi-limb exoskeleton walking balance control method in the embodiment of the present invention includes: 501: When the phase state is the single-leg support phase, predict the overall center of gravity motion trajectory according to the single-leg support phase, the position coordinates, the overall center of gravity coordinates and the kinematic model; 502: Calculate the velocity vector of the entire center of gravity according to the entire center of gravity motion trajectory to obtain a first velocity vector; 503: Convert the first velocity vector into a direction angle to obtain a first motion direction; 504: Obtaining the angle between the upper body and legs of the wearer in the sensing information to obtain a first tilting angle; 505: Calculate the angle between the first movement direction and the ground to obtain a second angle; 506: Determine whether the first tilting angle exceeds a preset first threshold; 507: Determine whether the second angle exceeds a preset second threshold; 508: When the first tipping angle exceeds the first threshold and the second angle exceeds the second threshold, a judgment result that there is a tipping risk is generated to obtain a first judgment result; 509: Analyze the first judgment result; 510: When there is a risk of tipping, determine whether the first tipping angle exceeds a preset third threshold; 511: When the first tilting angle exceeds a third threshold, the swinging leg is controlled to fall, and a first tilting direction is calculated according to the first tilting angle, and the free limb is controlled to move toward the first tilting direction; 512: When the first tilting angle does not exceed the third threshold, a second tilting direction is calculated according to the first tilting angle, and the free limb is controlled to move toward the second tilting direction.

[0032] In this embodiment, the motion trajectory of the overall center of gravity is predicted by adopting a kinematic model. Specifically, the kinematic models that can be adopted include a particle motion model, a rigid body motion model, a multi-rigid body system model, and an inverted pendulum model. The velocity vector of the overall center of gravity is calculated by adopting a center of gravity motion speed calculation formula. The center of gravity motion speed calculation formula is: ,in, and , are two adjacent points on the trajectory of the overall center of gravity, and is the time point corresponding to two adjacent points; the first velocity vector can be converted into a direction angle through the conversion formula to obtain the movement direction of the overall center of gravity. The conversion formula is , (depending on and The positive or negative situation determines the quadrant in which the correction is made), where: is the overall center of gravity coordinate.

[0033] In the single-leg support phase, through a series of operations such as kinematic models, the overall center of gravity motion trajectory and related parameters, such as velocity vector, direction angle, etc., can be accurately predicted, and key angles can also be obtained. By comparing and judging with the preset threshold, the tipping risk results can be generated and analyzed in a timely manner; according to the relationship between the tipping angle and different thresholds, the swinging leg and free limb movements can be controlled in a targeted manner, which effectively improves the monitoring accuracy of the wearer's balance state, can predict and accurately respond to tipping risks in advance, and ensure the safety and stability of the wearer's actions.

[0034] See also Figure 6The sixth embodiment of the multi-limb exoskeleton walking balance control method in the embodiment of the present invention includes: 601: When the phase state is the double-leg support phase, obtain the angle between the upper body and legs of the wearer in the sensing information to obtain a second tilting angle; 602: Calculate a third tilting direction according to the second tilting angle; 603: Determine whether the third dumping direction is a forward dumping direction, and generate a second determination result; 604: When the third tilting direction is the forward tilting direction, controlling the supporting leg to step forward; 605: When the third tilting direction is not the forward tilting direction, controlling the free limb to move toward the rear side of the wearer for support; 606: Generate a dynamic balance control instruction according to the second judgment result to adjust the zero moment point coordinates within the support range.

[0035] In this embodiment, the third tilting direction can be calculated by using a kinematic formula, where the kinematic formula includes a trigonometric function-related formula and a rigid body rotation kinematic formula.

[0036] By obtaining the angle between the wearer's upper body and legs to obtain the second tilting angle and predicting the third tilting direction, it can accurately determine whether to tilt forward and generate a second judgment result. Based on this, the supporting leg is controlled to step when tilting forward, and the back of the free limb is moved to support when not tilting forward, achieving targeted balance control. Finally, a dynamic balance control instruction is generated based on the judgment result, which effectively adjusts the zero torque point coordinates within the support range, greatly improving the balance stability and safety of the wearer in the double-leg support state.

[0037] The above describes the multi-limb exoskeleton walking balance control method in the embodiment of the present invention. The following describes the multi-limb exoskeleton walking balance control device in the embodiment of the present invention. Figure 7 In one embodiment of the present invention, a multi-limb exoskeleton walking balance control device includes: A multi-limb exoskeleton walking balance control device, comprising: A sensor information acquisition module 1 is used to acquire sensor information from various sensors; a zero moment point coordinate calculation module 2 is used to calculate the overall zero moment point coordinates based on the sensor information and the center of gravity coordinates; a stable support polygon area calculation module 3 is used to calculate the stable support polygon area based on the sensor information and the area definition method; a coordinate position judgment module 4 is used to judge whether the zero moment point coordinates are within the stable support polygon area; a phase state acquisition module 5 is used to obtain the phase state of the robot; a balance assist mode execution module 6 is used to control the robot to execute the balance assist mode according to the phase state when the zero moment point coordinates are not within the stable support polygon area.

[0038] The zero-moment point coordinate calculation module 2 obtains the overall zero-moment point coordinates based on the sensor information; the stable support polygon area calculation module 3 determines the stable support polygon area; the coordinate position judgment module 4 can accurately judge the relationship between the zero-moment point coordinates and the stable support polygon area; the robot phase state is grasped by the phase state acquisition module 5; the balance assistance mode execution module 6 controls the robot to execute the balance assistance mode according to the phase state when the zero-moment point is not in the area; the overall accurate judgment and rapid response to the wearer's balance state are achieved, the calculation is simple and the requirements are low, and it can provide the wearer with reliable balance assistance in a variety of environments, thereby improving the safety and stability of the movement.

[0039] Figure 8 6 is a schematic diagram of the structure of a multi-limb exoskeleton walking balance control device provided by an embodiment of the present invention. The multi-limb exoskeleton walking balance control device 600 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 613 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 633 or data 632. Among them, the memory 620 and the storage medium 630 can be short-term storage or permanent storage. The program stored in the storage medium 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the multi-limb exoskeleton walking balance control device 600. Furthermore, the processor 610 may be configured to communicate with the storage medium 630, and execute a series of instruction operations in the storage medium 630 on the multi-limb exoskeleton walking balance control device 600 to implement the steps of the multi-limb exoskeleton walking balance control method provided by the above-mentioned method embodiments.

[0040] The multi-limb exoskeleton walking balance control device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input and output interfaces 660, and / or one or more operating systems 631, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 8 The structure of the multi-limb exoskeleton walking balance control device shown does not constitute a limitation of the multi-limb exoskeleton walking balance control device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0041] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the multi-limb exoskeleton walking balance control method.

[0042] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-limb exoskeleton walking balance control method, characterized in that: include: Obtain sensor information from various sensors; The coordinates of the overall zero moment point are calculated based on the sensing information; The stable support polygonal area is calculated based on the sensing information and the area definition method; Determine whether the coordinates of the zero moment point are within the stable support polygon area; Get the robot's phase state; When the coordinates of the zero-torque point are not within the stable support polygon, the robot is controlled to execute the balance assist mode according to the phase state.

2. The multi-limb exoskeleton walking balance control method according to claim 1, characterized in that: Said include: The calculating of the overall zero moment point coordinates according to the sensing information includes: Acquiring a weight parameter of the wearer from the sensing information to obtain a first weight parameter; Converting the first weight parameter to each joint component according to the set parameter to obtain a second weight parameter; Calculate the position coordinates of each joint component based on the sensor information; Calculate the overall center of gravity coordinates according to the position coordinates and the second weight parameter; The coordinates of the overall zero moment point are calculated based on the sensor information, position coordinates and the overall center of gravity coordinates.

3. The multi-limb exoskeleton walking balance control method according to claim 1, characterized in that: The stable supporting polygonal area is calculated based on the sensing information and the area definition method, including: Acquire the coordinates of the contact point between the robot and the ground from the sensor information to obtain the coordinates of the contact point on the ground; The stable support polygonal area is calculated based on the coordinates of the ground contact point and the area definition method.

4. The multi-limb exoskeleton walking balance control method as claimed in claim 2, characterized in that: When the zero moment point coordinates are not within the stable support polygonal region, controlling the robot to execute a balance assist mode according to the phase state includes: When the coordinates of the zero moment point are not within the stable support polygonal region, the phase state is judged as a type, the phase state includes a single-leg support phase and a double-leg support phase, and the balance assistance mode includes a single-leg support balance assistance mode and a double-leg support balance assistance mode; When the phase state is the single-leg support phase, the robot is controlled to execute the single-leg support balance assist mode; When the phase state is the double-leg support phase, the robot is controlled to execute the double-leg support balance assist mode.

5. The multi-limb exoskeleton walking balance control method as claimed in claim 4, characterized in that: When the phase state is the single-leg support phase, controlling the robot to execute the single-leg support balance assisting mode includes: When the phase state is the single-leg support phase, the overall center of gravity motion trajectory is predicted according to the single-leg support phase, position coordinates, overall center of gravity coordinates and kinematic model; Calculate the velocity vector of the entire center of gravity according to the center of gravity motion trajectory to obtain a first velocity vector; Convert the first velocity vector into a direction angle to obtain a first motion direction; Obtaining the angle between the upper body and legs of the wearer in the sensor information to obtain a first tilting angle; Calculating the angle between the first moving direction and the ground to obtain a second angle; Determining whether the first tilting angle exceeds a preset first threshold; Determining whether the second angle exceeds a preset second threshold; When the first tipping angle exceeds a first threshold value and the second angle exceeds a second threshold value, a judgment result that there is a tipping risk is generated to obtain a first judgment result; Performing result analysis on the first judgment result; When there is a risk of tipping, determining whether the first tipping angle exceeds a preset third threshold; When the first tilting angle exceeds a third threshold, the swinging leg is controlled to fall, and a first tilting direction is calculated according to the first tilting angle, and the free limb is controlled to move toward the first tilting direction; When the first tipping angle does not exceed the third threshold, the second tipping direction is calculated according to the first tipping angle, and the free limb is controlled to move in the second tipping direction.

6. The multi-limb exoskeleton walking balance control method as claimed in claim 4, characterized in that: When the phase state is the double-leg support phase, controlling the robot to execute the double-leg support balance assisting mode includes: When the phase state is the double-leg support phase, the angle between the wearer's upper body and legs in the sensing information is obtained to obtain a second tilting angle; A third tilting direction is obtained by calculating according to the second tilting angle; determining whether the third dumping direction is a forward dumping direction, and generating a second determination result; When the third tilting direction is the forward tilting direction, control the supporting leg to step forward; When the third tilting direction is not the forward tilting direction, the free limb is controlled to move toward the rear side of the wearer for support; A dynamic balance control instruction is generated according to the second judgment result to adjust the zero moment point coordinates within the support range.

7. A multi-limb exoskeleton walking balance control device, characterized in that: include: A sensor information acquisition module is used to obtain sensor information from various sensors; A zero moment point coordinate calculation module is used to calculate the overall zero moment point coordinates based on the sensor information; A stable support polygon area calculation module is used to calculate the stable support polygon area according to the sensing information and the area definition method; A coordinate position judgment module is used to judge whether the coordinates of the zero moment point are within the stable support polygon area; A phase state acquisition module is used to acquire the phase state of the robot; The balance assist mode execution module is used to control the robot to execute the balance assist mode according to the phase state when the coordinates of the zero moment point are not within the stable support polygon area.

8. A multi-limb exoskeleton walking balance control device, characterized in that: The multi-limb exoskeleton walking balance control device comprises: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors calls the instructions in the memory to enable the multi-limb exoskeleton walking balance control device to perform each step of the multi-limb exoskeleton walking balance control method as described in any one of claims 1-6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, each step of the multi-limb exoskeleton walking balance control method as described in any one of claims 1-6 is implemented.

Images