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

Abstract

The present application relates to the technical field of exoskeleton robots, in particular to a multi-limb exoskeleton walking balance control method, device, equipment and storage medium. The present application can accurately calculate the zero moment point coordinates of the wearer by acquiring sensing information, and determine a stable support polygon area. When the zero moment point is not in 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 phase state of the robot according to the walking state of the wearer and the environment in the sensing information, and accurately judges the balance state of the wearer, so that the robot can quickly make a balance strategy according to the change of the center of gravity of the wearer. Moreover, the calculation process is simple, the requirements of the control system hardware and software are low, the cost and technical difficulty are reduced, and reliable balance assistance can be provided for the wearer in daily life or special working environment, to improve the safety and stability of action.

Description

Technical Field

[0001] This invention relates to the field of exoskeleton robot technology, specifically to a method, device, equipment, and storage medium for controlling the walking balance of a multi-limb exoskeleton. Background Technology

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

[0003] To address the shortcomings of the prior art, this invention proposes a multi-limb exoskeleton walking balance control method, device, equipment, and storage medium.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A multi-limb exoskeleton walking balance control method includes: acquiring sensor information from various sensors; calculating the overall zero-moment point coordinates based on the sensor information; calculating a stable support polygon region based on the sensor information and a region definition method; determining whether the zero-moment point coordinates are within the stable support polygon region; acquiring the robot's phase state; and controlling the robot to execute a balance assistance mode based on the phase state when the zero-moment point coordinates are not within the stable support polygon region. By acquiring sensor information, the wearer's zero-moment point coordinates can be accurately calculated, and the stable support polygon region can be determined. When the zero-moment point is not within the stable support polygon region, the robot can execute a balance assistance mode based on its phase state, ensuring the wearer's safety. This method derives the robot's phase state based on the wearer's walking state and the surrounding environment from the sensor information, and accurately judges the wearer's balance state. This allows the robot to quickly make balance strategies based on changes in the wearer's center of gravity. Furthermore, the calculation process is simple, with low requirements for the control system's hardware and software, reducing costs and technical difficulty. Whether in daily life or special working environments, it can provide reliable balance assistance for wearers, improving the safety and stability of their movements.

[0006] Furthermore, the step of calculating the overall zero-moment point coordinates based on the sensing information includes: obtaining the wearer's weight parameters from the sensing information to obtain a first weight parameter; converting the first weight parameter to each joint component according to a set parameter to obtain a second weight parameter; calculating the position coordinates of each joint component based on the sensing information; calculating the overall center of gravity coordinates based on the position coordinates and the second weight parameter; and calculating the overall zero-moment point coordinates based on the sensing information, position coordinates, and overall center of gravity coordinates.

[0007] The robot accurately acquires the wearer's weight parameters from sensor information to obtain a first weight parameter. This first weight parameter is then converted to each joint component according to predefined parameters to form a second weight parameter. The position coordinates of each joint component are then obtained. Based on these position coordinates and the second weight parameter, the overall center of gravity coordinates are calculated. This process ensures the accuracy and scientific rigor of data acquisition and calculation, enabling the tracking of the wearer's and robot's center of gravity changes based on individual wearer differences, postures, and dynamic activities. Next, using the position coordinates of each joint component, the overall center of gravity coordinates, and sensor information, the coordinates of the zero-torque point are scientifically calculated. This process achieves precise control and effective utilization of relevant important parameters, providing strong support for assessing the wearer's balance and adjusting their posture during activities.

[0008] Furthermore, the step of calculating the stable support polygonal region based on sensor information and a region delineation method includes: 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 polygonal region based on the ground contact point coordinates and the region delineation method. By accurately determining the specific region related to the robot, a clear spatial range is provided for subsequent analysis. Secondly, this stable support polygonal region can serve as an important reference; for example, when assessing the robot's stability, its relationship with the actual support state can be compared, which helps to promptly identify potential imbalance risks.

[0009] Furthermore, when the coordinates of the zero-torque point are not within the stable support polygon area, controlling the robot to execute a balance-assisted mode based on the phase state includes:

[0010] When the zero torque point coordinates are not within the stable support polygon area, the phase state is judged by type. The phase state includes single-leg support phase and double-leg support phase. The balance assist mode includes single-leg support balance assist mode and double-leg support balance assist mode.

[0011] When the phase state is the single-leg support phase, control the robot to execute the single-leg support balance assistance mode;

[0012] When the phase state is the two-legged support phase, control the robot to execute the two-legged support balance assistance mode.

[0013] By determining the relationship between the zero-torque point coordinates and the stable support polygon region, and by classifying the phase state, two states—single-leg support phase and double-leg support phase—and their corresponding balance assistance modes were identified. This allows for precise control of the robot to adopt the appropriate assistance mode under different circumstances, improving the robot's adaptability to different support states. It can promptly address potential imbalance risks, ensure the robot remains stable under various motion states, and enhance the safety and reliability of robot operations. This provides an effective balance assurance strategy for the practical application of robots.

[0014] Furthermore, when the phase state is a single-leg support phase, controlling the robot to execute the single-leg support balance assistance mode includes: predicting the overall center of gravity trajectory based on the single-leg support phase, position coordinates, overall center of gravity coordinates, and kinematic model; calculating the velocity vector of the overall center of gravity based on the overall center of gravity trajectory to obtain a first velocity vector; converting the first velocity vector into a direction angle to obtain a first direction of motion; acquiring the angle between the wearer's upper body and legs from the sensing information to obtain a first tilt angle; calculating the angle between the first direction of motion and the ground to obtain a second angle; and determining whether the first tilt angle exceeds a preset first threshold. The system determines whether the second included angle exceeds a preset second threshold. When both the first tilting angle and the second included angle exceed the second threshold, a judgment result indicating a risk of tilting is generated, resulting in a first judgment result. The first judgment result is then analyzed. If a risk of tilting exists, the system determines whether the first tilting angle exceeds a preset third threshold. When the first tilting angle exceeds the third threshold, the system controls the swing leg to fall and calculates the first tilting direction based on the first tilting angle, controlling the free limb to move in the first tilting direction. When the first tilting angle does not exceed the third threshold, the system calculates the second tilting direction based on the first tilting angle and controls the free limb to move in the second tilting direction. In the single-leg support phase, through a series of operations such as kinematic modeling, the overall center of gravity movement trajectory and related parameters, such as velocity vector and direction angle, can be accurately predicted. It can also obtain key angles and, by comparing them with preset thresholds, generate and analyze the tilt risk results in a timely manner. Based on the relationship between the tilt angle and different thresholds, the swing leg and free limb movements can be controlled in a targeted manner. This effectively improves the monitoring accuracy of the wearer's balance status, can predict and accurately respond to tilt risks in advance, and ensure the safety and stability of the wearer's actions.

[0015] Furthermore, when the phase state is the two-legged support phase, controlling the robot to execute the two-legged support balance assistance mode includes: when the phase state is the two-legged support phase, acquiring the angle between the wearer's upper body and legs from the sensor information to obtain a second tilt angle; calculating a third tilt direction based on the second tilt angle; determining whether the third tilt direction is a forward tilt direction and generating a second judgment result; when the third tilt direction is a forward tilt direction, controlling the supporting leg to step forward; when the third tilt direction is not a forward tilt direction, controlling the free limb to move towards the wearer's rear for support; generating a dynamic balance control command based on the second judgment result to adjust the zero torque point coordinates within the support range. By acquiring the angle between the wearer's upper body and legs to obtain the second tilt angle and predicting the third tilt direction, it is possible to accurately determine whether to tilt forward and generate a second judgment result. Accordingly, controlling the supporting leg to step forward when tilting forward and allowing the free limb to move towards the rear for support when not tilting forward, targeted balance control is achieved. Finally, based on the judgment results, dynamic balance control commands are generated, which effectively adjusts the zero torque point coordinates within the support range, greatly improving the wearer's balance stability and safety when supported by both legs.

[0016] Furthermore, the multi-limb exoskeleton walking balance control device includes:

[0017] The sensor information acquisition module is used to acquire sensor information from various sensors;

[0018] The system comprises four modules: a zero-moment point coordinate calculation module, a stable support polygon region calculation module, a coordinate position determination module, a phase state acquisition module, and a balance assist mode execution module. The module calculates the overall zero-moment point coordinates based on sensor information. The stable support polygon region calculation module determines the stable support polygon region. The coordinate position determination module accurately determines the relationship between the zero-moment point coordinates and the stable support polygon region. The phase state acquisition module monitors the robot's phase state. The balance assist mode execution module controls the robot to execute a balance assist mode based on the phase state when the zero-moment point is outside the region. Overall, the system achieves accurate judgment and rapid response to the wearer's balance state. It is computationally simple, has low requirements, and can provide reliable balance assistance to wearers in diverse environments, improving safety and stability.

[0019] Furthermore, a multi-limb exoskeleton walking balance control device includes: a memory and at least one processor, wherein the memory stores instructions;

[0020] At least one of the processors invokes the instructions in the memory to cause the multi-limb exoskeleton walking balance control device to perform the steps of the multi-limb exoskeleton walking balance control method as described in any of the above statements.

[0021] Furthermore, a computer-readable storage medium storing instructions, characterized in that, when executed by a processor, the instructions implement the various steps of the multi-limb exoskeleton walking balance control method as described in any one of the above descriptions.

[0022] The beneficial effects of the multi-limb exoskeleton walking balance control method of the present invention are as follows:

[0023] By acquiring 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 robot can execute a balance assistance mode based on 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 from the sensor information, and accurately judges the wearer's balance state. This allows the robot to quickly make balance strategies based on changes in the wearer's center of gravity. Moreover, the calculation process is simple, and the requirements for the control system's hardware and software are low, reducing costs and technical difficulties. Whether in daily life or in special working environments, it can provide reliable balance assistance for the wearer, improving the safety and stability of movement. Attached Figure Description

[0024] Figure 1 This is a first flowchart of a multi-limb exoskeleton walking balance control method provided in an embodiment of the present invention;

[0025] Figure 2 This is a second flowchart of the multi-limb exoskeleton walking balance control method provided in an embodiment of the present invention;

[0026] Figure 3 This is a third flowchart of the multi-limb exoskeleton walking balance control method provided in the embodiments of the present invention;

[0027] Figure 4 This is a fourth flowchart of the multi-limb exoskeleton walking balance control method provided in the embodiments of the present invention;

[0028] Figure 5 The fifth flowchart of the multi-limb exoskeleton walking balance control method provided in the embodiments of the present invention;

[0029] Figure 6 The sixth flowchart of the multi-limb exoskeleton walking balance control method provided in the embodiments of the present invention;

[0030] Figure 7A schematic diagram of a multi-limb exoskeleton walking balance control device provided in an embodiment of the present invention;

[0031] Figure 8 This is a schematic diagram of the structure of the multi-limb exoskeleton walking balance control device provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the multi-limb exoskeleton walking balance control method of the present invention includes:

[0035] Multi-limb exoskeleton walking balance control methods include:

[0036] 101: Acquire sensing information from various sensors;

[0037] 102: The coordinates of the overall zero-torque point are calculated based on the sensor information;

[0038] 103: The stable supporting polygonal region is calculated based on sensor information and region delineation methods;

[0039] 104: Determine whether the coordinates of the zero-moment point are within the stable support polygon region;

[0040] 105: Obtain the robot's phase state;

[0041] 106: When the coordinates of the zero torque point are not within the stable support polygon area, the robot is controlled to execute the balance assistance mode according to the phase state.

[0042] In this embodiment, the types of sensors 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 effect sensors, position and attitude sensors, etc.

[0043] By acquiring 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 robot can execute a balance assistance mode based on 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 from the sensor information, and accurately judges the wearer's balance state. This allows the robot to quickly make balance strategies based on changes in the wearer's center of gravity. Moreover, the calculation process is simple, and the requirements for the control system's hardware and software are low, reducing costs and technical difficulties. Whether in daily life or in special working environments, it can provide reliable balance assistance for the wearer, improving the safety and stability of movement.

[0044] Please see Figure 2 The second embodiment of the multi-limb exoskeleton walking balance control method of the present invention includes:

[0045] 201: Obtain the wearer's weight parameters from the sensor information to obtain the first weight parameter;

[0046] 202: Based on the set parameters, the first weight parameter is converted to each joint component to obtain the second weight parameter;

[0047] 203: Calculate the position coordinates of each joint component based on the sensor information;

[0048] 204: Calculate the overall center of gravity coordinates based on the position coordinates and the second weight parameter;

[0049] 205: Calculate the coordinates of the zero torque point of the whole system based on the sensor information, position coordinates, and overall center of gravity coordinates.

[0050] In this embodiment, the formula type for calculating the overall centroid coordinates is a centroid calculation formula, such as: ,in, The wearer's body weight parameter. These are the position coordinates of each joint component. This is the second weight parameter. The coordinates of the overall center of gravity and the zero-moment point can be calculated using kinematic equations, including those based on force equilibrium equations, such as... , in The overall mass parameters of the robot and the wearer are calculated using sensor information. Let gravitational acceleration vector be the vector. For the wearer's overall center of gravity coordinates, , The coordinates of the zero-torque point for the whole system.

[0051] The robot accurately acquires the wearer's weight parameters from sensor information to obtain a first weight parameter. This first weight parameter is then converted to each joint component according to predefined parameters to form a second weight parameter. The position coordinates of each joint component are then obtained. Based on these position coordinates and the second weight parameter, the overall center of gravity coordinates are calculated. This process ensures the accuracy and scientific rigor of data acquisition and calculation, enabling the tracking of the wearer's and robot's center of gravity changes based on individual wearer differences, postures, and dynamic activities. Next, using the position coordinates of each joint component, the overall center of gravity coordinates, and sensor information, the coordinates of the zero-torque point are scientifically calculated. This process achieves precise control and effective utilization of relevant important parameters, providing strong support for assessing the wearer's balance and adjusting their posture during activities.

[0052] Please see Figure 3 The third embodiment of the multi-limb exoskeleton walking balance control method of the present invention includes:

[0053] 301: Obtain the coordinates of the robot's contact point with the ground from the sensor information to obtain the ground contact point coordinates;

[0054] 302: The stable support polygonal region is calculated based on the ground contact point coordinates and the region delineation method.

[0055] In this implementation, the region delimitation methods used in calculating the stable supporting polygon region include convex hull algorithms, concave polygon generation algorithms, and geometric rule-based delimitation methods. For example, the geometric rule-based delimitation method used below allows a set of linear inequalities to represent the internal region of a convex polygon. Taking a simple two-dimensional convex polygon as an example, let its vertex coordinates be... , ..., For each side of the polygon, we can find its corresponding straight line equation. (in, , , It is based on the coefficients determined by the coordinates of two points on the line. Then, based on the principle that a line divides a plane into two regions, by determining which side of the line a point lies on, an inequality is constructed. If for a given side, the equation of the corresponding line is... Then the points inside the convex polygon satisfy (or (The specific value depends on which side of the line the point is on). For the entire convex polygon, its interior region can be represented by a set of such inequalities.

[0056] By accurately identifying specific areas related to the robot, a clear spatial range is defined for subsequent analysis. Secondly, this stable support polygonal area can serve as an important reference. For example, when assessing the stability of the robot, its relationship with the actual support state can be compared, which helps to identify potential imbalance risks in a timely manner.

[0057] Please see Figure 4 The fourth embodiment of the multi-limb exoskeleton walking balance control method of the present invention includes:

[0058] 401: When the zero torque point coordinates are not within the stable support polygon area, the phase state is judged. The phase state includes single-leg support phase and double-leg support phase. The balance assistance mode includes single-leg support balance assistance mode and double-leg support balance assistance mode.

[0059] 402: When the phase state is the single-leg support phase, control the robot to execute the single-leg support balance assistance mode;

[0060] 403: When the phase state is the two-legged support phase, control the robot to execute the two-legged support balance assistance mode.

[0061] In this embodiment, the single-leg support balance assistance mode is a mode in which the robot's multiple auxiliary limbs assist the wearer in maintaining balance in a single-leg support state;

[0062] The bipedal support balance assist mode is a mode in which the robot's multiple assistive limbs help the wearer maintain balance in a bipedal support state.

[0063] By determining the relationship between the zero-torque point coordinates and the stable support polygon region, and by classifying the phase state, two states—single-leg support phase and double-leg support phase—and their corresponding balance assistance modes were identified. This allows for precise control of the robot to adopt the appropriate assistance mode under different circumstances, improving the robot's adaptability to different support states. It can promptly address potential imbalance risks, ensure the robot remains stable under various motion states, and enhance the safety and reliability of robot operations. This provides an effective balance assurance strategy for the practical application of robots.

[0064] Please see Figure 5 The fifth embodiment of the multi-limb exoskeleton walking balance control method of the present invention includes:

[0065] 501: When the phase state is the single-leg support phase, predict the trajectory of the overall center of gravity movement based on the single-leg support phase, position coordinates, overall center of gravity coordinates, and kinematic model;

[0066] 502: Calculate the velocity vector of the overall center of gravity based on the overall center of gravity's trajectory to obtain the first velocity vector;

[0067] 503: Convert the first velocity vector into a direction angle to obtain the first direction of motion;

[0068] 504: Obtain the angle between the wearer's upper body and legs from the sensor information to obtain the first tilt angle;

[0069] 505: Calculate the angle between the first direction of motion and the ground to obtain the second angle;

[0070] 506: Determine whether the first tilting angle exceeds the preset first threshold;

[0071] 507: Determine whether the second included angle exceeds the preset second threshold;

[0072] 508: When the first tilting angle exceeds the first threshold and the second included angle exceeds the second threshold, a judgment result indicating a risk of tilting is generated to obtain the first judgment result;

[0073] 509: Analyze the results of the first judgment;

[0074] 510: When there is a risk of tipping over, determine whether the first tipping angle exceeds the preset third threshold;

[0075] 511: When the first tilting angle exceeds the third threshold, control the swing leg to fall, and calculate the first tilting direction based on the first tilting angle, and control the free limb to move in the first tilting direction;

[0076] 512: When the first tilting angle does not exceed the third threshold, the second tilting direction is calculated based on the first tilting angle, and the free limb is controlled to move in the second tilting direction.

[0077] In this embodiment, the overall center of gravity trajectory is predicted using a kinematic model. Specific kinematic models that can be used include point mass motion models, rigid body motion models, multi-rigid-body system models, and inverted pendulum models. The velocity vector of the overall center of gravity is calculated using the center of gravity velocity calculation formula, which is: ,in, and , which are two adjacent points on the overall trajectory of the center of gravity. and These are the time points corresponding to two adjacent points; the first velocity vector can be converted into a direction angle using a conversion formula to obtain the direction of motion of the overall center of gravity. The conversion formula is as follows: (subject to change) and (The positive or negative case determines the quadrant in which it belongs and is then corrected), where... The coordinates are the overall center of gravity.

[0078] In a single-leg support phase, through a series of operations including kinematic modeling, the overall center of gravity trajectory and related parameters, such as velocity vector and direction angle, can be accurately predicted, and key angles can also be obtained. By comparing with preset thresholds, the tilt risk results can be generated and analyzed in a timely manner; based on the relationship between the tilt angle and different thresholds, the swing leg and free limb movements can be controlled in a targeted manner. This effectively improves the monitoring accuracy of the wearer's balance status, allows for early prediction and precise response to tilt risks, and ensures the safety and stability of the wearer's movements.

[0079] Please see Figure 6 The sixth embodiment of the multi-limb exoskeleton walking balance control method of the present invention includes:

[0080] 601: When the phase state is the double-leg support phase, obtain the angle between the wearer's upper body and legs from the sensing information to obtain the second tilt angle;

[0081] 602: The third tilting direction is calculated based on the second tilting angle;

[0082] 603: Determine whether the third tilting direction is a forward tilting direction, and generate a second judgment result;

[0083] 604: When the third tilting direction is forward tilting, control the supporting leg to step forward;

[0084] 605: When the third tilting direction is not the forward tilting direction, control the free limbs to move towards the wearer's rear for support;

[0085] 606: Generate dynamic balance control commands based on the second judgment result to adjust the zero torque point coordinates within the support range.

[0086] In this embodiment, the third tilting direction can be calculated using kinematic formulas, which include trigonometric function formulas and rigid body rotation kinematic formulas.

[0087] By obtaining the second tilt angle from the angle between the wearer's upper body and legs and predicting the third tilt direction, the system can accurately determine whether to tilt forward, generating a second judgment result. Based on this, when tilting forward, the supporting leg is controlled to step; when not tilting forward, the free limbs are moved backward for support, achieving targeted balance control. Finally, dynamic balance control commands are generated based on the judgment result, effectively adjusting the zero-torque point coordinates within the support range, greatly improving the wearer's balance stability and safety in a two-legged support state.

[0088] The above describes the multi-limb exoskeleton walking balance control method in the embodiments of the present invention. The following describes the multi-limb exoskeleton walking balance control device in the embodiments of the present invention. Please refer to [link / reference]. Figure 7 One embodiment of the multi-limb exoskeleton walking balance control device of the present invention includes:

[0089] A multi-limb exoskeleton walking balance control device, including:

[0090] The sensor information acquisition module 1 is used to acquire sensor information from various sensors; the 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; the stable support polygon region calculation module 3 is used to calculate the stable support polygon region based on the sensor information and the region definition method; the coordinate position judgment module 4 is used to determine whether the zero-moment point coordinates are within the stable support polygon region; the phase state acquisition module 5 is used to acquire the phase state of the robot; and the balance assist mode execution module 6 is used to control the robot to execute the balance assist mode based on the phase state when the zero-moment point coordinates are not within the stable support polygon region.

[0091] The zero-moment point coordinate calculation module 2 calculates the overall zero-moment point coordinates based on sensor information; the stable support polygon region calculation module 3 determines the stable support polygon region; the coordinate position judgment module 4 can accurately determine the relationship between the zero-moment point coordinates and the stable support polygon region; the phase state acquisition module 5 grasps the robot's phase state; the balance assist mode execution module 6 controls the robot to execute the balance assist mode based on the phase state when the zero-moment point is not in the region; the whole system realizes accurate judgment and rapid response of the wearer's balance state, with simple calculation and low requirements, and can provide reliable balance assistance for the wearer in various environments, improving the safety and stability of movement.

[0092] Figure 8This is a schematic diagram of the structure of a multi-limb exoskeleton walking balance control device 600 provided in an embodiment of the present invention. The multi-limb exoskeleton walking balance control device 600 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 613 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. The memory 620 and storage media 630 can be temporary or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the multi-limb exoskeleton walking balance control device 600. Furthermore, the processor 610 may be configured to communicate with the storage media 630 and execute the series of instruction operations in the storage media 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 in the above-described method embodiments.

[0093] The multi-limb exoskeleton walking and 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 / output interfaces 660, and / or one or more operating systems 631, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 8 The structure of the multi-limb exoskeleton walking balance control device shown does not constitute a limitation on the multi-limb exoskeleton walking balance control device. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0094] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the multi-limb exoskeleton walking balance control method.

[0095] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling walking balance using a multi-limb exoskeleton, characterized in that, include: Acquire sensing information from various sensors; The coordinates of the overall zero-torque point are calculated based on the sensor information; The stable supporting polygonal region is calculated based on sensor information and region delineation methods; Determine whether the coordinates of the zero-moment point are within the stable support polygon region; Obtain the robot's phase state; When the zero-torque point coordinates are not within the stable support polygon area, the robot is controlled to execute the balance assistance mode according to the phase state. When the zero torque point coordinates are not within the stable support polygon area, the phase state is judged by type. The phase state includes single-leg support phase and double-leg support phase. The balance assistance mode includes single-leg support balance assistance mode and double-leg support balance assistance mode. When the phase state is the single-leg support phase, control the robot to execute the single-leg support balance assistance mode; When the phase state is the two-legged support phase, control the robot to execute the two-legged support balance assistance mode; When the phase state is the single-leg support phase, the trajectory of the overall center of gravity is predicted based on the single-leg support phase, position coordinates, overall center of gravity coordinates, and kinematic model. The velocity vector of the overall center of gravity is calculated based on the trajectory of the center of gravity movement to obtain the first velocity vector; The first velocity vector is converted into a direction angle to obtain the first direction of motion; The angle between the wearer's upper body and legs is obtained from the sensor information to determine the first tilt angle; Calculate the angle between the first direction of motion and the ground to obtain the second angle; Determine whether the first tilting angle exceeds a preset first threshold; Determine whether the second included angle exceeds a preset second threshold; When the first tilting angle exceeds the first threshold and the second included angle exceeds the second threshold, a judgment result indicating a risk of tilting is generated to obtain the first judgment result; Analyze the results of the first judgment; When there is a risk of tipping over, determine whether the first tipping angle exceeds a preset third threshold. When the first tilting angle exceeds the third threshold, control the swing leg to fall, and calculate the first tilting direction based on the first tilting angle, and control the free limb to move in the first tilting direction; If the first tilting angle does not exceed the third threshold, the second tilting direction is calculated based on the first tilting angle, and the free limb is controlled to move in the second tilting direction.

2. The multi-limb exoskeleton walking balance control method as described in claim 1, characterized in that, The calculation of the overall zero-torque point coordinates based on the sensor information includes: The wearer's weight parameters are obtained from the sensor information to obtain the first weight parameter; The first weight parameter is converted to each joint component according to the set parameters to obtain the second weight parameter; Calculate the position coordinates of each joint component based on the sensor information; Calculate the overall center of gravity coordinates based on the position coordinates and the second weight parameter; The coordinates of the zero torque point of the whole are calculated based on the sensor information, position coordinates, and overall center of gravity coordinates.

3. The multi-limb exoskeleton walking balance control method as described in claim 1, characterized in that, The process of calculating the stable supporting polygon region based on sensor information and region delineation methods includes: Obtain the coordinates of the robot's contact point with the ground from the sensor information to obtain the ground contact point coordinates; The stable support polygon region is calculated based on the ground contact point coordinates and the region delineation method.

4. The multi-limb exoskeleton walking balance control method as described in claim 1, characterized in that, When the phase state is the two-legged support phase, controlling the robot to execute the two-legged support balance assistance mode includes: When the phase state is the double-leg support phase, the angle between the wearer's upper body and legs is obtained from the sensing information to obtain the second tilt angle; The third tilting direction is calculated based on the second tilting angle; Determine whether the third tilting direction is a forward tilting direction, and generate a second judgment result; When the third tilting direction is forward tilting, control the supporting leg to step forward; When the third tilting direction is not the forward tilting direction, control the free limbs to move towards the wearer's rear for support; Based on the second judgment result, a dynamic balance control command is generated to adjust the zero torque point coordinates within the support range.

5. A multi-limb exoskeleton walking balance control device, characterized in that, include: The sensor information acquisition module is used to acquire sensor information from various sensors; The zero-moment point coordinate calculation module is used to calculate the overall zero-moment point coordinates based on sensor information. The stable support polygon region calculation module is used to calculate the stable support polygon region based on sensor information and region delineation methods. The coordinate position determination module is used to determine whether the coordinates of the zero torque point are within the stable support polygon area; The 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 based on the phase state when the zero torque point coordinates are not within the stable support polygon area. The specific steps include: When the zero torque point coordinates are not within the stable support polygon area, the phase state is judged by type. The phase state includes single-leg support phase and double-leg support phase. The balance assistance mode includes single-leg support balance assistance mode and 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 assistance mode, specifically including the following steps: When the phase state is the single-leg support phase, the trajectory of the overall center of gravity is predicted based on the single-leg support phase, position coordinates, overall center of gravity coordinates, and kinematic model. The velocity vector of the overall center of gravity is calculated based on the trajectory of the center of gravity movement to obtain the first velocity vector; The first velocity vector is converted into a direction angle to obtain the first direction of motion; The angle between the wearer's upper body and legs is obtained from the sensor information to determine the first tilt angle; Calculate the angle between the first direction of motion and the ground to obtain the second angle; Determine whether the first tilting angle exceeds a preset first threshold; Determine whether the second included angle exceeds a preset second threshold; When the first tilting angle exceeds the first threshold and the second included angle exceeds the second threshold, a judgment result indicating a risk of tilting is generated to obtain the first judgment result; Analyze the results of the first judgment; When there is a risk of tipping over, determine whether the first tipping angle exceeds a preset third threshold. When the first tilting angle exceeds the third threshold, control the swing leg to fall, and calculate the first tilting direction based on the first tilting angle, and control the free limb to move in the first tilting direction; If the first tilting angle does not exceed the third threshold, the second tilting direction is calculated based on the first tilting angle, and the free limb is controlled to move in the second tilting direction. When the phase state is the two-legged support phase, control the robot to execute the two-legged support balance assistance mode.

6. A multi-limb exoskeleton walking balance control device, characterized in that, The multi-limb exoskeleton walking balance control device includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the multi-limb exoskeleton walking balance control device to perform the steps of the multi-limb exoskeleton walking balance control method as described in any one of claims 1-4.

7. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the multi-limb exoskeleton walking balance control method as described in any one of claims 1-4.

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