Exoskeleton safe walking control method, device and equipment and storage medium

Abstract

The invention relates to the technical field of exoskeleton robots, in particular to an exoskeleton safe walking control method, device and equipment and a storage medium. According to the exoskeleton safe walking control method, by analyzing the phase state of the robot, an appropriate motion assisting mode can be accurately provided for a wearer according to different states, when the robot is in the swing phase, a power assisting mode is executed, powerful assistance is provided for the wearer in the free walking stage, the burden of the wearer is relieved, and the safety of the wearer is improved. For obstacles encountered during free walking, the robot generates a motion track to guide a wearer to walk and actively avoid the obstacles, so that accidents such as collision caused by the fact that the wearer does not perceive the obstacles in time are avoided, and the walking safety is greatly improved; the wearer does not need to pay attention to the surrounding environment all the time to ensure safety.

Description

Technical Field

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

[0002] With the aging trend of the population, more and more elderly people cannot control the free movement of their lower limbs due to weakened physical functions. The emergence of exoskeleton robots provides a new idea to solve this problem. In order to assist the elderly to walk freely, exoskeleton robots currently assist the wearer by calculating the joint torque through sensors in various parts of the body or actively assist the wearer through pre-made trajectories. In the former method, the wearer is in an active control role and the exoskeleton robot follows the wearer's movement. In the latter method, the wearer is in a passive obedient role and the wearer follows the movement of the exoskeleton robot. Both of the above two typical control methods have unavoidable safety problems, such as being tripped by obstacles or steps under the feet. The first method cannot actively avoid obstacles encountered during walking, and walking safety mainly depends on the wearer himself. The second method can adjust the walking trajectory appropriately after adding appropriate sensors, but the adjustment process limits the freedom of movement of the wearer. Summary of the invention

[0003] In order to solve the above-mentioned shortcomings in the prior art, the present invention proposes an exoskeleton safe walking 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 method for controlling safe walking of an exoskeleton comprises: obtaining a phase state of a robot and determining the type of the phase state; when the phase state is a swing phase, controlling the robot to execute a power-assist mode; during the robot executing the power-assist mode, obtaining obstacle information in the environment and analyzing the obstacle information to obtain a first analysis result; and controlling the robot to execute an active mode or maintain the power-assist mode according to the first analysis result.

[0005] By analyzing the phase state of the robot, it can accurately provide the wearer with appropriate motion assistance modes according to different states. When in the swing phase, the power-assist mode is executed to provide strong assistance to the wearer during the free walking stage, reduce the wearer's burden, and improve the efficiency and comfort of exercise. For obstacles encountered during free walking, the robot generates motion trajectories to guide the wearer to walk and actively avoid them, preventing accidents such as collisions caused by the wearer's failure to detect obstacles in time. This greatly improves the safety of walking, and the wearer does not need to pay attention to the surrounding environment at all times to ensure safety.

[0006] Furthermore, the obtaining of the phase state of the robot and determining the type of the phase state include: obtaining first sensing information of each sensor and first working parameters of each joint component; and performing motion analysis on the robot according to the first sensing information and the first working parameters to obtain the phase state.

[0007] By collecting data from different sensors and joint components, the robot can be monitored more comprehensively; by analyzing data from multiple angles and obtaining the phase state of the robot, the accuracy of the data analysis results can be improved.

[0008] Furthermore, the analysis of the phase state of the robot and the determination of the type of the phase state include: the phase state includes a swing phase and a support phase; when the phase state is a support phase, support working parameters are obtained based on a preset dynamic model and the support phase calculation, and the working state of each joint component is adjusted according to the support working parameters so that the robot executes the support mode.

[0009] In support mode, the robot is able to work better with the wearer and provide the necessary support, thereby enhancing the human-machine interaction experience.

[0010] Furthermore, when the phase state is the swing phase, the robot is controlled to execute the power-assist mode, including: When the phase state is the swing phase, the swing working parameters are obtained based on the dynamic model and the swing phase calculation; and the working state of each joint component is adjusted according to the swing working parameters, so that the robot executes the power-assist mode.

[0011] When executing the power-assist mode, the robot can adjust the working state of each joint component according to the swing working parameters, thereby providing the wearer with appropriate auxiliary force. This not only reduces the wearer's burden, but also improves the comfort and efficiency of walking.

[0012] Furthermore, during the robot's execution of the power-assist mode, obstacle information in the environment is obtained, and the obstacle information is analyzed to obtain a first analysis result, including: performing data analysis on the first sensor information to obtain obstacle information; the obstacle information includes obstacle image data, the robot's first position coordinates and the obstacle's second position coordinates relative to the robot; analyzing the obstacle image data to obtain the obstacle type, obstacle height and obstacle size; calculating the distance between the robot end and the obstacle based on the first position coordinates and the second position coordinates to obtain a first distance; determining whether the first distance is less than or equal to a preset first threshold to obtain a distance determination result; and generating the first analysis result based on the obstacle image data analysis result, the first distance and the distance determination result.

[0013] Through data analysis of the first sensor information, obstacles can be fully identified and the distance to the obstacles can be accurately measured, thereby improving the accuracy of obstacle avoidance and path planning; the robot can flexibly adjust its walking strategy based on real-time distance judgment results and obstacle characteristics, thereby enhancing its adaptability to different environments.

[0014] Further, the controlling the robot to execute the active mode or maintain the assist mode according to the first analysis result includes: analyzing the first analysis result and determining the comparison result of the distance determination result; when the first distance is less than or equal to the first threshold, planning the walking trajectory in Cartesian space according to the first distance, obstacle type, obstacle height, obstacle size and a preset maximum leg lift height of the robot; The gait working parameters are calculated based on the dynamic model, walking trajectory and swing phase. The working state of each joint component is adjusted according to the gait working parameters to control the robot to execute the active mode.

[0015] By analyzing the distance judgment results, the robot can accurately sense the distance from obstacles, avoid collision risks in advance, and ensure the safety of itself and its surroundings. When the distance is less than the threshold, the walking trajectory is planned by comprehensively considering various obstacle factors, which improves the ability to pass in complex environments. Based on the calculation of gait working parameters and adjustment of joint states based on dynamic models, precise motion control is achieved, making the robot's movements more in line with mechanical principles and smooth and natural. At the same time, the robot's autonomous decision-making and execution capabilities are improved, and it can flexibly respond to environmental changes and better adapt to various complex scenarios.

[0016] Furthermore, after the gait working parameters are calculated based on the dynamic model, walking trajectory and swing phase, and the working state of each joint component is adjusted according to the gait working parameters to control the robot to execute the active mode or maintain the power-assisted mode, it also includes: acquiring the sensor data of each sensor again to obtain the second sensor information; calculating the first force of the wearer according to the second sensor information; calculating the stiffness adjustment working parameters based on the dynamic model, the first force and the swing phase, and adjusting the working state of each joint component according to the stiffness adjustment working parameters.

[0017] First, by acquiring the sensor data again, the state information of the interaction between the wearer and the robot can be fully and accurately grasped. The wearer's force is calculated based on the sensor information, and its movement intention can be accurately quantified. Based on multiple factors, the stiffness adjustment parameters are calculated and the joint state is adjusted to achieve personalized adaptation, taking into account stability and flexibility, so that the robot's movements are accurately matched to the wearer, and a certain amount of autonomous adjustment space is reserved for the wearer. This enhances human-machine collaboration, improves the wearer's user experience and satisfaction, and at the same time improves the quality and efficiency of assistance, ensuring safety and convenience.

[0018] Furthermore, after obtaining the stiffness adjustment working parameters based on the dynamic model, the first force and the swing phase calculation and adjusting the working state of each joint component according to the stiffness adjustment working parameters, it also includes: acquiring the sensor data of each sensor again to obtain the third sensor information; acquiring the third position coordinates of the robot according to the third sensor information; analyzing whether the robot has completed the walking stroke according to the first position coordinates, the third position coordinates and the walking trajectory to obtain a second analysis result; and updating the phase state or generating optimization instructions according to the second analysis result.

[0019] The third sensor information is obtained by collecting sensor data again, and then the third position coordinates of the robot are determined. Combined with the previous position coordinates and the preset walking trajectory, the robot is analyzed to see whether it has completed the walking journey as planned, and the second analysis result is obtained. Based on this result, the system can update the phase state or generate optimization instructions in a timely manner to ensure the accuracy and efficiency of the robot's walking, and improve the flexibility and response speed of the overall navigation and control.

[0020] Further, the updating of the phase state or generating of the optimization instruction according to the second analysis result includes: the second analysis result includes the robot's completed walking stroke and the robot's uncompleted walking stroke; when the robot has completed the walking stroke, the swing working parameters are calculated based on the dynamic model and the swing phase, and the working state of each joint component is adjusted according to the swing working parameters, so that the robot executes the power-assisted mode; When the robot has not completed the walking stroke, a walking trajectory optimization instruction is generated.

[0021] By analyzing the robot's walking stroke, the robot's current state can be clearly identified, so that targeted measures can be taken. When the robot has completed its walking stroke, the swing working parameters are calculated based on the dynamic model and the swing phase, so that the robot can execute the power-assisted mode, which helps to quickly adjust the state after completing the stroke and prepare for the next action, improving the overall operating efficiency, and can accurately adjust the joint components according to the actual situation to better meet the needs of different task stages.

[0022] When the robot has not completed its walking journey, it generates walking trajectory optimization instructions. This allows the robot to continuously adjust and optimize the path during the walking process, avoid potential obstacles, and improve walking accuracy and safety. At the same time, the trajectory is dynamically optimized according to the actual situation, which can adapt to different environmental changes and task requirements, and improve the robot's autonomous decision-making ability and work performance.

[0023] Furthermore, an exoskeleton safe walking control device includes: a phase state analysis module, used to obtain the phase state of the robot and determine the type of the phase state; an assist mode control module, used to control the robot to execute the assist mode; an obstacle information analysis module, used to obtain obstacle information in the environment and analyze the obstacle information to obtain a first analysis result; a mode control module, used to control the robot to execute the active mode or maintain the assist mode according to the first analysis result.

[0024] The phase state analysis module can accurately determine the phase state of the robot and provide the wearer with a suitable motion assistance mode. The power-assistance mode control module executes the power-assistance mode in the swing phase to reduce the wearer's burden and improve motion efficiency and comfort. The obstacle information analysis module and the mode control module are combined to control the robot to execute active mode avoidance when encountering obstacles, avoid collision accidents, improve walking safety, and allow the wearer to focus on their own actions without having to pay attention to the surrounding environment at all times, thereby improving the overall motion experience and safety.

[0025] Further, an exoskeleton safe walking control device is provided, the exoskeleton safe walking control device comprising: a memory and at least one processor, the memory storing instructions; Furthermore, at least one of the processors calls the instructions in the memory so that the exoskeleton safe walking control device executes the various steps of the above-mentioned exoskeleton safe walking control method based on obstacle perception.

[0026] Furthermore, a computer-readable storage medium is provided, on which instructions are stored, wherein when the instructions are executed by a processor, the various steps of the exoskeleton safe walking control method as described above are implemented.

[0027] The beneficial effects of the exoskeleton safe walking control method of the present invention are: By analyzing the phase state of the robot, the present invention can accurately provide the wearer with a suitable motion assistance mode according to different states. When in the swing phase, the power-assist mode is executed to provide powerful assistance to the wearer in the free walking stage, reduce the burden on the wearer, and improve the efficiency and comfort of movement. For obstacles encountered during free walking, the robot generates a motion trajectory to guide the wearer to walk and actively avoids them, thereby preventing the wearer from colliding with the obstacles due to failure to notice the obstacles in time. The safety of walking is greatly improved, and the wearer does not need to pay attention to the surrounding environment at all times to ensure safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 A first flow chart of a method for controlling safe walking of an exoskeleton provided by an embodiment of the present invention; Figure 2 A second flow chart of an exoskeleton safe walking control method provided by an embodiment of the present invention; Figure 3 A third flow chart of an exoskeleton safe walking control method provided by an embodiment of the present invention; Figure 4 A fourth flow chart of an exoskeleton safe walking control method provided by an embodiment of the present invention; Figure 5 A fifth flow chart of an exoskeleton safe walking control method provided by an embodiment of the present invention; Figure 6 A sixth flow chart of an exoskeleton safe walking control method provided by an embodiment of the present invention; Figure 7 A seventh flow chart of an exoskeleton safe walking control method provided by an embodiment of the present invention; Figure 8 An eighth flow chart of an exoskeleton safe walking control method provided by an embodiment of the present invention; Fig. 9 A ninth flow chart of an exoskeleton safe walking control method provided by an embodiment of the present invention; Fig.10 A schematic diagram of the structure of an exoskeleton safe walking control device provided by an embodiment of the present invention; Fig.11 A schematic diagram of the structure of an exoskeleton safe walking control device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] 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.

[0030] 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.

[0031] like Figure 1-11 As shown, the present invention provides an exoskeleton safe walking control method, system, device and storage medium.

[0032] To solve the above technical problems, the technical solution adopted by the present invention is as follows. Figure 1 , an embodiment of an exoskeleton safe walking control method in an embodiment of the present invention includes: 101. Obtain the phase state of the robot and determine the type of the phase state; 102. When the phase state is the swing phase, the robot is controlled to execute the power-assist mode; 103. When the robot is in the power-assist mode, obtain obstacle information in the environment, and analyze the obstacle information to obtain a first analysis result; In this embodiment, obstacle recognition and perception technology is used to realize the perception function of the exoskeleton robot for complex walking environments. The sensor types involved include visual sensors and obstacle distance perception sensors. The visual sensors are used to capture obstacle image data, and the obstacle distance perception sensors are used to detect whether there are obstacles around the exoskeleton robot and accurately measure the distance between the exoskeleton robot and the obstacles.

[0033] 104. Control the robot to execute the active mode or maintain the power-assisted mode according to the first analysis result; such intelligent decision-making improves the efficiency and safety of walking; The power-assist mode is a mode in which the robot assists the wearer in exercising; In this embodiment, the power-assisted mode mainly realizes gravity compensation for the wearer's swinging legs. The robot mainly shows flexible following, and the wearer only needs a small force to change its movement position. The power-assisted mode enables the exoskeleton robot to assist the wearer's movement and reduce the wearer's physical exertion, which is a significant advantage, especially for individuals with limited mobility or physical strength. The active mode is a mode in which the robot actively plans the motion trajectory and guides the wearer to walk according to the motion trajectory; In this embodiment, in the active mode, it is mainly realized on the basis of gravity compensation, and also has a trajectory constraint effect. At this time, the rigidity of the robot is relatively large, and the wearer needs a large force to change the planned motion trajectory.

[0034] By analyzing the phase state of the robot, it can accurately provide the wearer with appropriate motion assistance modes according to different states. When in the swing phase, the power-assist mode is executed to provide strong assistance to the wearer during the free walking stage, reduce the wearer's burden, and improve the efficiency and comfort of exercise. For obstacles encountered during free walking, the robot generates motion trajectories to guide the wearer to walk and actively avoid them, preventing accidents such as collisions caused by the wearer's failure to detect obstacles in time. This greatly improves the safety of walking, and the wearer does not need to pay attention to the surrounding environment at all times to ensure safety.

[0035] See also Figure 2 A second embodiment of an exoskeleton safe walking control method in an embodiment of the present invention includes: 201. Acquire first sensing information of each sensor and first working parameters of each joint component; 202. Perform motion analysis on the robot according to the first sensing information and the first working parameter to obtain a phase state.

[0036] In this embodiment, the first sensing information includes the touchdown coordinates, touchdown force, acceleration value, joint movement angle, walking distance, walking speed, torque at the joint, magnitude and direction of torque, motor rotor position, obstacle image data, exoskeleton robot posture and other information; the types of working parameters of each joint component that need to be adjusted include torque parameters, power parameters, speed parameters and position parameters; the types of joint components include motor drivers and drive motors; the types of sensors used to detect the phase state include: touchdown sensors, speed sensors, acceleration sensors, odometers (encoders), torque sensors, force and torque sensors, rotary transformers, photoelectric encoders, Hall sensors, position and posture sensors, visual sensors and obstacle distance perception sensors; the motion analysis process includes data collection and organization (collecting the first sensing information and the first working parameters), establishing a dynamic model (Newton- Dynamic methods such as Euler equations or Lagrange equations), kinematic analysis based on dynamic models (velocity analysis and acceleration analysis of the robot's end effector, establishing algebraic equations of joint angles and end effector positions through trigonometric functions, and then solving these equations to obtain joint angles, predicting the robot's motion trajectory through integration methods, analyzing the force and torque balance relationship in the dynamic model, as well as concepts such as the robot's center of mass position and zero moment point (ZMP), evaluating the stability point of the robot's motion, etc.), state estimation and identification (using state estimation methods such as Kalman filtering and particle filtering, combined with the first sensor information and the first working parameter, to estimate and predict the robot's motion state), and comprehensively judging the robot's phase state based on the kinematic analysis results and the state estimation and identification results.

[0037] By collecting data from different sensors and joint components, the robot can be monitored more comprehensively; by analyzing data from multiple angles and obtaining the phase state of the robot, the accuracy of the data analysis results can be improved.

[0038] See also Figure 3 A third embodiment of an exoskeleton safe walking control method in an embodiment of the present invention includes: 301. The phase state includes a swing phase and a support phase; 302. When the phase state is the support phase, the support working parameters are calculated based on the preset dynamic model and the phase state, and the working state of each joint component is adjusted according to the support working parameters, so that the robot executes the support mode.

[0039] In this embodiment, when the robot phase state is the support phase, the robot is used to provide auxiliary support function; the dynamic models involved in calculating the support working parameters include Newton-Euler equations, Lagrange equations, Kane equations, etc. The dynamic model is a mathematical model used in robotics to describe the motion behavior of a robot under the action of forces and torques. It includes the mass and inertia characteristics of the robot, and the dynamic model can analyze these characteristics to obtain analysis results on how these characteristics affect the motion of the robot. In this scheme, the role of the dynamic model is mainly reflected in the control of the robot's motion and path planning.

[0040] In support mode, the robot is able to work better with the wearer and provide the necessary support, thereby enhancing the human-machine interaction experience.

[0041] See also Figure 4 A fourth embodiment of an exoskeleton safe walking control method in an embodiment of the present invention includes: When the phase state is the swing phase, the swing working parameters are calculated based on the dynamic model and the phase state; and the working state of each joint component is adjusted according to the swing working parameters, so that the robot performs the power-assisted mode. In this embodiment, the dynamic models involved in the swing working parameter calculation process include the Lagrangian dynamic model, the virtual model control method, the variable-length flexible biped robot gait switching control model, the segmented SLIP model, etc.; the swing working parameters include the angle, speed, acceleration, etc. of the joints; the principle of calculating the swing working parameters based on the dynamic model and the phase state includes establishing a dynamic equation, determining the dynamic parameters that need special attention at this stage according to the phase state (swing phase) of the robot, and calculating the swing working parameters such as the angle, speed, acceleration, etc. of the joints according to the dynamic equation and the phase state; By adjusting the working state of each joint component using the swing working parameters, the robot can realize the gravity compensation function for the wearer's swing leg movement; when executing the power-assist mode, the robot can adjust the working state of each joint component according to the swing working parameters, thereby providing the wearer with appropriate auxiliary force. This not only reduces the wearer's burden, but also improves the comfort and efficiency of walking.

[0042] See also Figure 5 A fifth embodiment of an exoskeleton safe walking control method in an embodiment of the present invention includes: 501. Perform data analysis on the first sensing information to obtain obstacle information; the obstacle information includes obstacle image data, a first position coordinate of the robot, and a second position coordinate of the obstacle relative to the robot; 502. Analyze the obstacle image data to obtain the obstacle type, obstacle height and obstacle size; 503. Calculate the distance between the robot end and the obstacle according to the first position coordinate and the second position coordinate to obtain a first distance; 504. Determine whether the first distance is less than or equal to a preset first threshold to obtain a distance determination result; 505. Generate a first analysis result according to the obstacle image data analysis result, the first distance, and the distance determination result.

[0043] In this embodiment, by performing data analysis on the first sensing information, the robot can obtain detailed information such as image data and position coordinates of the obstacle, thereby realizing comprehensive recognition of the obstacle; Using the first position coordinates of the robot and the second position coordinates of the obstacle, the actual distance between the robot end and the obstacle can be calculated, providing accurate measurement data for obstacle avoidance and path planning; By determining whether the first distance is less than or equal to a preset first threshold, the robot can evaluate the proximity to the obstacle in real time; Based on the obstacle image data analysis results, the first distance and the distance determination results, the robot can generate a comprehensive first analysis result to provide comprehensive information support for subsequent decision-making and actions; Through data analysis of the first sensor information, obstacles can be fully identified and the distance to the obstacles can be accurately measured, thereby improving the accuracy of obstacle avoidance and path planning; the robot can flexibly adjust its walking strategy based on real-time distance judgment results and obstacle characteristics, thereby enhancing its adaptability to different environments.

[0044] See also Figure 6 A sixth embodiment of an exoskeleton safe walking control method in an embodiment of the present invention includes: 601. Analyze the first analysis result to determine the comparison result of the distance determination result; 602. When the first distance is less than or equal to the first threshold, a walking trajectory is planned in Cartesian space according to the first distance, obstacle type, obstacle height, obstacle size, and a preset maximum leg lift height of the robot; 603. Gait working parameters are calculated based on the dynamic model, walking trajectory and phase state, and the working state of each joint component is adjusted according to the gait working parameters to control the robot to execute the active mode.

[0045] In this embodiment, obstacles are divided into crossable obstacles and non-crossable obstacles according to whether they can be crossed. Obstacles that can be crossed are also defined as crossable obstacles. According to the type of obstacle, the planned walking trajectory includes a crossing trajectory and a detour trajectory; In this embodiment, the dynamic models involved in the gait working parameter calculation process include: Lagrangian dynamic model, linear inverted pendulum (LIP) dynamic model, DCM (Divergent Component of Motion) based dynamic model and robot gait parameter optimization model; After planning the walking trajectory in Cartesian space, it needs to be converted into a motion description in joint space, because the dynamic model is mainly based on the motion and force of the joints. First, the kinematic model of the robot must be determined, and the trajectory points in Cartesian space (such as foot position, body center of gravity position, etc.) must be converted into joint angles through inverse kinematics. For example, for a simple planar bipedal robot, the target position of the foot in the Cartesian coordinate system and the geometric dimensions of the robot's legs (link length, etc.) are known, and the angles of the knee and hip joints can be calculated using trigonometric functions and other methods; Adjust the dynamic calculations based on the type, height and size of the obstacle, as well as the robot's maximum leg lift height. If a taller obstacle is encountered, the robot needs to lift its legs a greater height, which will change the trajectory and speed of the legs; For example, when encountering a step obstacle with a height of , the robot's maximum leg-lifting height is h. During the leg-lifting process, the leg's motion trajectory is no longer a simple plane arc. It is necessary to recalculate the kinematic and dynamic parameters of the leg when crossing the step based on the geometric shape of the step and the robot's leg-lifting requirements.

[0046] Assume that the knee angle changes from 1 changes to 2. Based on the kinematic relationship, the linear velocity and angular velocity changes of the legs can be calculated, and then substituted into the dynamic model (such as Lagrange equation) to calculate the changes in the torque and force of the joints in the process of crossing the steps, so as to obtain the gait working parameters; Through the above steps, taking into account the walking trajectory, phase state and obstacle factors, the dynamic model is used to calculate the gait working parameters such as force, torque, speed (angular velocity and linear velocity), position (joint angle and linear position) of each joint at different times. These parameters will serve as the basis for controlling the working state of the joint components to realize the active walking mode of the robot in a complex environment. For example, during the entire walking process, a series of torque values, angular velocity values ​​and other parameters will be obtained for the hip joint, and the output of the hip joint motor will be adjusted according to these parameters so that the robot can walk according to the planned trajectory; By analyzing the distance judgment results, the robot can accurately sense the distance from obstacles, avoid collision risks in advance, and ensure the safety of itself and its surroundings. When the distance is less than the threshold, the walking trajectory is planned by comprehensively considering various obstacle factors, which improves the ability to pass in complex environments. Based on the calculation of gait working parameters and adjustment of joint states based on dynamic models, precise motion control is achieved, making the robot's movements more in line with mechanical principles and smooth and natural. At the same time, the robot's autonomous decision-making and execution capabilities are improved, and it can flexibly respond to environmental changes and better adapt to various complex scenarios.

[0047] See also Figure 7 A seventh embodiment of an exoskeleton safe walking control method in an embodiment of the present invention includes: 701. Acquire sensing data of each sensor again to obtain second sensing information; 702. Calculate a first force acting on the wearer according to the second sensing information; 703. Calculate stiffness adjustment working parameters based on the dynamic model, the first force and the phase state, and adjust the working state of each joint component according to the stiffness adjustment working parameters.

[0048] In this embodiment, the robot has different stiffnesses in different motion modes. In the assist mode, the robot has a smaller stiffness and flexibly follows the wearer, which mainly assists the wearer to swing his legs more easily. The wearer only needs a small force to change his motion position. When the robot executes the active mode, the robot has a trajectory constraint function on the basis of gravity compensation. In this mode, the robot has a larger stiffness, which ensures the robot's dominant advantage in guiding the wearer to move along the motion trajectory. The wearer can also change the stiffness of the robot through human-machine interaction force. Therefore, in order to ensure the safety of the wearer's movement, the wearer needs a larger force to change the robot's motion trajectory. The stiffness of the robot can also be adjusted according to the wearer's instructions, including key commands and voice commands, to adapt to the different needs of wearers. This method uses the switching of different motion modes of the robot to obtain different motion stiffness, thereby achieving different control requirements and ultimately assisting the wearer to complete the purpose of safe walking; the types of sensors involved in the stiffness adjustment process include human-machine force interaction sensors.

[0049] First, by acquiring the sensor data again, the state information of the interaction between the wearer and the robot can be fully and accurately grasped. The wearer's force is calculated based on the sensor information, and its movement intention can be accurately quantified. Based on multiple factors, the stiffness adjustment parameters are calculated and the joint state is adjusted to achieve personalized adaptation, taking into account stability and flexibility, so that the robot's movements are accurately matched to the wearer, and a certain amount of autonomous adjustment space is reserved for the wearer. This enhances human-machine collaboration, improves the wearer's user experience and satisfaction, and at the same time improves the quality and efficiency of assistance, ensuring safety and convenience.

[0050] See also Figure 8 The eighth embodiment of an exoskeleton safe walking control method in the embodiment of the present invention includes: After calculating the stiffness adjustment working parameters based on the dynamic model, the first force and the phase state, and adjusting the working state of each joint component according to the stiffness adjustment working parameters, the method further includes: 801. Acquire sensing data of each sensor again to obtain third sensing information; 802. Acquire a third position coordinate of the robot according to the third sensing information; 803. Analyze whether the robot has completed the walking stroke according to the first position coordinates, the third position coordinates and the walking trajectory to obtain a second analysis result; 804. Update the phase state or generate an optimization instruction according to the second analysis result.

[0051] In this embodiment, the third sensor information is obtained by collecting sensor data again, and then the third position coordinates of the robot are determined. Combined with the previous position coordinates and the preset walking trajectory, it is analyzed whether the robot completes the walking journey as planned to obtain a second analysis result. Based on this result, the system can update the phase state or generate optimization instructions in a timely manner to ensure the accuracy and efficiency of the robot's walking and improve the flexibility and response speed of the overall navigation and control.

[0052] See also Figure 9 A ninth embodiment of an exoskeleton safe walking control method in an embodiment of the present invention includes: 901. The second analysis result includes the walking distance that the robot has completed and the walking distance that the robot has not completed; 902. When the robot has completed the walking stroke, a swing working parameter is calculated based on the dynamic model and the phase state, and the working state of each joint component is adjusted according to the swing working parameter, so that the robot executes the power-assisted mode; 903. When the robot has not completed the walking stroke, a walking trajectory optimization instruction is generated.

[0053] By analyzing the robot's walking stroke, the robot's current state can be clearly identified, so that targeted measures can be taken. When the robot has completed its walking stroke, the swing working parameters are calculated based on the dynamic model and phase state, so that the robot can execute the power-assisted mode, which helps to quickly adjust the state after completing the stroke and prepare for the next action, improving the overall operating efficiency, and can accurately adjust the joint components according to the actual situation to better meet the needs of different task stages.

[0054] When the robot has not completed its walking journey, it generates walking trajectory optimization instructions. This allows the robot to continuously adjust and optimize the path during the walking process, avoid potential obstacles, and improve walking accuracy and safety. At the same time, the trajectory is dynamically optimized according to the actual situation, which can adapt to different environmental changes and task requirements, and improve the robot's autonomous decision-making ability and work performance.

[0055] The above describes an exoskeleton safe walking control method in an embodiment of the present invention. The following describes an exoskeleton safe walking control device in an embodiment of the present invention. Figure 10 In one embodiment of the present invention, an exoskeleton safe walking control device includes: Phase state analysis module 1, used to obtain the phase state of the robot and determine the type of the phase state; The power-assist mode control module 2 is used to control the robot to execute the power-assist mode; The obstacle information analysis module 3 is used to obtain obstacle information in the environment and analyze the obstacle information to obtain a first analysis result; The mode control module 4 is used to control the robot to execute the active mode or maintain the assist mode according to the first analysis result.

[0056] In this embodiment, the phase state analysis module 4 can accurately determine the phase state of the robot and provide the wearer with a suitable motion assistance mode. The power-assistance mode control module 2 executes the power-assistance mode in the swing phase to reduce the burden on the wearer and improve the efficiency and comfort of the movement. The obstacle information analysis module 3 and the mode control module 4 are combined to control the robot to execute the active mode avoidance when encountering obstacles, avoid collision accidents, improve walking safety, and allow the wearer to focus on their own actions without having to pay attention to the surrounding environment all the time, thereby improving the overall sports experience and safety.

[0057] above Figure 10 An exoskeleton safe walking control device in an embodiment of the present invention is described in detail from the perspective of modular functional entities. The following describes an exoskeleton safe walking control device in an embodiment of the present invention in detail from the perspective of hardware processing.

[0058] Figure 10The first is a schematic diagram of the structure of an exoskeleton safe walking control device provided by an embodiment of the present invention. The exoskeleton safe walking control device 100 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 110 (for example, one or more processors) and a memory 120, and one or more storage media 130 (for example, one or more mass storage devices) storing application programs 133 or data 132. Among them, the memory 120 and the storage medium 130 can be short-term storage or permanent storage. The program stored in the storage medium 130 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the exoskeleton safe walking control device 100. Furthermore, the processor 110 can be configured to communicate with the storage medium 130, and execute a series of instruction operations in the storage medium 130 on the exoskeleton safe walking control device 100 to implement the steps of the exoskeleton safe walking control method provided by the above-mentioned method embodiments.

[0059] The exoskeleton safe walking control device 100 may also include one or more power supplies 140, one or more wired or wireless network interfaces 150, one or more input and output interfaces 160, and / or one or more operating systems 131, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 10 The structure of the exoskeleton safe walking control device shown does not constitute a limitation of the exoskeleton safe walking 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.

[0060] 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 exoskeleton safe walking control method.

[0061] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0062] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0063] 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 method for controlling safe walking of an exoskeleton, characterized in that: include: Obtain the phase state of the robot and determine the type of the phase state; When the phase state is the swing phase, the robot is controlled to execute the power-assist mode; When the robot is in the power-assist mode, obstacle information in the environment is acquired, and the obstacle information is analyzed to obtain a first analysis result; The robot is controlled to execute the active mode or maintain the assist mode according to the first analysis result.

2. The exoskeleton safe walking control method according to claim 1, characterized in that: The obtaining of the phase state of the robot and determining the type of the phase state include: Acquire first sensing information of each sensor and first working parameters of each joint component; The robot is subjected to motion analysis according to the first sensing information and the first working parameter to obtain a phase state.

3. The exoskeleton safe walking control method according to claim 2, characterized in that: After analyzing the phase state of the robot and determining the type of the phase state, the method further comprises: The phase state includes a swing phase and a support phase; When the phase state is the supporting phase, the supporting working parameters are obtained based on the preset dynamic model and the supporting phase calculation, and the working state of each joint component is adjusted according to the supporting working parameters so that the robot executes the supporting mode.

4. The exoskeleton safe walking control method according to claim 3, characterized in that: When the phase state is the swing phase, the robot is controlled to execute the power-assist mode, including: When the phase state is the swing phase, the swing working parameters are calculated based on the dynamic model and the swing phase; The working state of each joint component is adjusted according to the swing working parameters so that the robot can execute the power-assist mode.

5. The exoskeleton safe walking control method according to claim 2, characterized in that: The process of obtaining obstacle information in the environment and analyzing the obstacle information to obtain a first analysis result during the robot executing the power-assist mode includes: Performing data analysis on the first sensing information to obtain obstacle information; the obstacle information includes obstacle image data, a first position coordinate of the robot, and a second position coordinate of the obstacle relative to the robot; Analyze obstacle image data to obtain obstacle type, obstacle height and obstacle size; Calculating the distance between the robot end and the obstacle according to the first position coordinate and the second position coordinate to obtain a first distance; Determine whether the first distance is less than or equal to a preset first threshold to obtain a distance determination result; A first analysis result is generated according to the obstacle image data analysis result, the first distance and the distance determination result. The method for controlling safe walking of an exoskeleton according to claim 5 is characterized in that: The controlling the robot to execute the active mode or to keep executing the assisting mode according to the first analysis result comprises: Performing result analysis on the first analysis result to determine the comparison result of the distance determination result; When the first distance is less than or equal to the first threshold, a walking trajectory is planned in Cartesian space according to the first distance, obstacle type, obstacle height, obstacle size, and a preset maximum leg lift height of the robot; The gait working parameters are calculated based on the dynamic model, walking trajectory and swing phase. The working state of each joint component is adjusted according to the gait working parameters to control the robot to execute the active mode. Acquire the sensing data of each sensor again to obtain second sensing information; Calculating a first force applied by the wearer according to the second sensing information; The stiffness adjustment working parameters are calculated based on the dynamic model, the first force and the swing phase, and the working state of each joint component is adjusted according to the stiffness adjustment working parameters. Acquire the sensing data of each sensor again to obtain third sensing information; Acquire a third position coordinate of the robot according to the third sensor information; Analyze whether the robot has completed the walking stroke according to the first position coordinates, the third position coordinates and the walking trajectory to obtain a second analysis result; According to the second analysis result, the phase state is updated or an optimization instruction is generated. The second analysis result includes the walking distance that the robot has completed and the walking distance that the robot has not completed; When the robot has completed the walking stroke, the swing working parameters are calculated based on the dynamic model and the swing phase, and the working state of each joint component is adjusted according to the swing working parameters, so that the robot executes the power-assisted mode; When the robot has not completed the walking stroke, a walking trajectory optimization instruction is generated. The phase state analysis module is used to obtain the phase state of the robot and determine the type of the phase state; A power-assist mode control module, used to control the robot to execute the power-assist mode; The obstacle information analysis module is used to obtain obstacle information in the environment and analyze the obstacle information to obtain a first analysis result; The mode control module is used to control the robot to execute the active mode or maintain the power-assisted mode according to the first analysis result. At least one of the processors calls the instructions in the memory so that the exoskeleton safe walking control device executes each step of the exoskeleton safe walking control method based on obstacle perception as described in any one of claims 1-9.

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