Robot operation state monitoring method and system

Abstract

The invention discloses a robot operation state monitoring method and system, and relates to the technical field of robots, and the method comprises the steps: detecting whether a to-be-detected patient reasonably wears a walking aid robot or not, and outputting a wearing completion result after determining that the to-be-detected patient reasonably wears the walking aid robot; acquiring patient information, and determining an obstacle detection area based on the patient information; whether obstacles exist in the obstacle detection area or not is detected, if not, a good road condition result is output, if yes, obstacle crossing or obstacle bypassing is judged, and if yes, the force exerting condition of muscles of the legs of the to-be-detected patient is detected when the to-be-detected patient crosses the crossing obstacles; and judging whether the muscle force of the to-be-tested patient can span successfully or not, if not, applying driving force to the to-be-tested patient based on the walking aid robot to help the to-be-tested patient to complete the crossing, and outputting a crossing completion result after the to-be-tested patient crosses the crossing obstacle. The method has the effect of improving the monitoring accuracy of the running state of the robot.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a method and system for monitoring the operating status of a robot. Background Art

[0002] At present, robots are a kind of machine device that can automatically perform work tasks, and have the ability of perception, planning, action and coordination. With the tremendous development of social science and technology, robots are widely used in various fields as a highly flexible automation equipment. Therefore, it is very important to monitor the operation status of robots.

[0003] The existing robot operation status monitoring method refers to monitoring the connection status and data collection of the walking robot, judging whether the walking robot can communicate with the system normally, and collecting the operation status parameters of the walking robot such as the operation speed and the distance from obstacles in all directions. However, the existing robot operation status monitoring method does not take into account the driving force required by the walking robot when the patient encounters an obstacle that cannot be crossed by the patient's own muscle force when using the walking robot. As a result, when the patient is using the walking robot, the walking robot provides driving force to help the patient cross the obstacle, but the driving force provided is too large or too small, and the patient is at risk of falling. The accuracy of monitoring the operation status of the walking robot is low, and there is room for improvement. Summary of the invention

[0004] In order to improve the accuracy of robot operation status monitoring, the present application provides a robot operation status monitoring method and system.

[0005] In the first aspect, the present application provides a method for monitoring the operation status of a robot, which adopts the following technical solution:

[0006] A method for monitoring the operation status of a robot comprises the following steps:

[0007] Detect whether the patient to be tested is wearing the walking-assist robot properly, and output the wearing completion result after confirming that the patient to be tested is wearing the walking-assist robot properly;

[0008] Acquire patient information, and determine an obstacle detection area based on the patient information;

[0009] Detect whether there is an obstacle in the obstacle detection area. If not, output a good road condition result. If so, determine whether to cross the obstacle or bypass the obstacle. If it is to cross the obstacle, detect the force of each muscle of the patient's leg when the patient crosses the obstacle, and determine whether the patient's own muscle force can successfully cross the obstacle. If not, apply driving force to the patient based on the walking robot to help the patient complete the crossing. When the patient crosses the obstacle, output the crossing completion result;

[0010] If it is a detour obstacle, the detour route of the patient to be tested is planned to obtain the planned path information, and the patient to be tested walks based on the planned path information. When the patient to be tested bypasses the detour obstacle, a detour success result is output;

[0011] When receiving a successful crossing result or a successful detour result, it outputs a successful obstacle avoidance result; when receiving a good road condition result or a successful obstacle avoidance result, it outputs a good walking state result;

[0012] When the patient is using the walking robot, the contact influence of the walking robot on the patient to be tested is detected to obtain a contact influence coefficient M, based on which it is determined whether the walking robot needs to be adjusted, and a good human-machine contact result is output;

[0013] When the wearing completion result and the good human-machine contact result are received, the wearing contact state good result is output; when the wearing contact state good result and the walking state good result are received, the running state good result is output.

[0014] Preferably, after the patient to be tested wears the walking-assisting robot, the battery power of the walking-assisting robot is detected to obtain the machine power information;

[0015] The usage time of the walking-assisting robot is determined based on the machine power information to obtain an estimated usable time, and the estimated usable time is compared with a preset usable time threshold. If the estimated usable time is greater than or equal to the preset usable time threshold, a sufficient power result is output;

[0016] Detect whether the motion data acquisition sensor unit of the patient to be tested fits the limb of the patient to be tested, and if so, output a successful fitting result of the motion acquisition sensor;

[0017] Detect whether the environmental data acquisition sensor unit of the patient to be tested is blocked, and if not blocked, output the normal result of the environmental sensor;

[0018] When receiving the result of sufficient power, successful fitting of the motion acquisition sensor and normal result of the environmental sensor, it means that the patient to be tested wears the walking-assist robot properly and the wearing completion result is output.

[0019] Preferably, patient information is obtained, wherein the patient information includes patient age information, patient disease information, and patient walking speed information, and the patient disease information includes patient disease type information and patient disease severity information;

[0020] The age influence coefficient AN is obtained based on the patient's age information;

[0021] Acquire a disease type walking influence data set, wherein the disease type walking influence data set includes information of each disease type and a type influence degree corresponding to each disease type;

[0022] Based on the disease type walking impact data set, find the type impact degree corresponding to the disease type information of the patient to be tested to obtain the disease type impact coefficient AL;

[0023] Based on the patient's disease severity information, the disease severity influence coefficient AC is obtained;

[0024] According to the disease type influence coefficient AL and the disease degree influence coefficient AC, the self-disease influence coefficient AH of the patient to be tested is calculated based on the self-disease function AH=ε1×AL+ε2×AC, where ε1 and ε2 are proportional factors and are both greater than 0;

[0025] Based on the patient's own walking speed information, the patient's own walking influence coefficient AX is obtained;

[0026] According to the age influence coefficient AN, the disease influence coefficient AH and the walking influence coefficient AX, the patient's own influence coefficient S is calculated based on the obstacle detection function S = a1 × AN + a2 × AH + a3 × AX, where a1, a2, and a3 are proportional factors and are all greater than 0;

[0027] The obstacle detection area is determined based on the patient's own influence coefficient S, wherein the larger the patient's own influence coefficient S is, the larger the obstacle detection area is.

[0028] Preferably, it is detected whether there is an obstacle in the obstacle area, and if there is no obstacle, a good road condition result is output; if there is an obstacle, it is determined whether the patient to be tested needs to cross or detour when passing the obstacle, and if the patient to be tested needs to cross, obstacle crossing information is output, and the obstacle to be crossed is recorded as a crossed obstacle; if the patient to be tested needs to detour, obstacle detour information is output, and the obstacle to be detour is recorded as a detour obstacle;

[0029] When the patient to be tested walks to the side of the obstacle, the distance between the patient to be tested and the obstacle is detected to obtain the obstacle difference distance value P;

[0030] Detect the height from the current standing level of the patient to the top of the obstacle to obtain the obstacle height difference value h1, obtain the obstacle safety height difference value h2 and store it;

[0031] Based on the obstacle distance difference value P, the obstacle height difference value h1 and the obstacle safety height difference value h2, the slope function i1=(h1+h2) / P×100% is used to calculate the slope that the patient to be tested needs to cross when crossing the obstacle to obtain the required crossing slope i1;

[0032] Based on the electromyographic sensors pre-installed on the leg muscles of the patient to be tested, the force of each muscle of the leg of the patient to be tested is detected to obtain muscle force information, wherein the muscle force information includes the position of the force-generating muscles and the force degree of each force-generating muscle;

[0033] Obtaining leg muscle force sample data of the patient to be tested and sample data of leg lifting height of the patient to be tested under different leg muscle force conditions when walking, calculating the slope that the patient to be tested can cross to obtain multiple characteristic crossing slope sample data based on the leg muscle force sample data of the patient to be tested and the sample data of leg lifting height of the patient to be tested under different leg muscle force conditions when walking, and establishing a characteristic muscle force walking vector model based on the leg muscle force sample data of the patient to be tested, the leg lifting height sample data and the characteristic crossing slope sample data;

[0034] According to the position of the force-generating muscles and the force degree of each force-generating muscle, the slope that the patient can cross by exerting muscle force on the leg when crossing the obstacle is analyzed based on the characteristic muscle force walking vector model to obtain the patient's ability slope i2;

[0035] Compare the patient's ability slope i2 with the crossing demand slope i1. If the patient's ability slope i2 is greater than the crossing demand slope i1, no adjustment is required. If the patient's ability slope i2 is less than or equal to the crossing demand slope i1, calculate the difference between the patient's ability slope i2 and the crossing demand slope i1 to obtain the slope difference i. Based on the characteristic muscle force walking vector model, analyze the difference in leg muscle force when the patient needs to increase the slope to the slope difference i when crossing the domain.

[0036] The human legs are driven based on the driver on the walking robot, wherein the driving force of the driver on the human legs is the difference in the degree of force exerted by the leg muscles. When the patient to be tested crosses the obstacle, the crossing completion result is output.

[0037] Preferably, if there is a bypass obstacle, the degree of obstruction of the bypass obstacle on the current walking path of the patient to be tested is detected to obtain bypass obstacle obstruction degree information;

[0038] Presetting a bypass obstacle occlusion degree threshold based on the volume of the patient to be tested, wherein the larger the volume of the patient to be tested, the smaller the bypass obstacle occlusion degree threshold;

[0039] The bypass obstacle occlusion degree information is compared with a preset bypass obstacle occlusion degree threshold. If the bypass obstacle occlusion degree information is less than the preset bypass obstacle occlusion degree threshold, the patient to be tested bypasses the bypass obstacle on the current walking path and moves forward. When the patient to be tested bypasses the bypass obstacle and continues to walk on the current walking path, a bypass success result is output;

[0040] If the bypass obstacle occlusion degree information is greater than or equal to the preset bypass obstacle occlusion degree threshold, the walking path of the patient to be tested needs to be replanned to obtain the planned path information, and the bypass success result is output after the patient to be tested walks based on the planned path information.

[0041] Preferably, a logic OR gate judgment module is obtained and a signal connection link is established between the logic OR gate judgment module and the walking-assisting robot, wherein the logic OR gate judgment module includes a first logic OR gate and a second logic OR gate;

[0042] When the first logic OR gate receives the crossing success result or the detour success result, it outputs an obstacle avoidance success result;

[0043] When the second logic OR gate receives the good road condition result or the successful obstacle avoidance result, it outputs a good walking state result.

[0044] Preferably, the human-machine contact information is obtained by detecting in real time the contact between the walking-assist robot and the patient's body when the patient is wearing the walking-assist robot, wherein the human-machine contact information includes the human-machine contact strength and the human-machine contact duration;

[0045] The contact force influence coefficient BL is obtained based on the human-machine contact force, wherein the greater the human-machine contact force, the greater the contact force influence coefficient BL;

[0046] The contact time influence coefficient BC is obtained based on the human-machine contact time, wherein the longer the human-machine contact time, the greater the contact time influence coefficient BC;

[0047] According to the contact force influence coefficient BL and the contact duration influence coefficient BC, the contact influence coefficient M is calculated based on the human-machine contact function M=λ1×BC+λ2×BL, where λ1 and λ2 are proportional factors and are both greater than 0;

[0048] The contact influence coefficient M is compared with the preset contact influence threshold N. If the contact influence coefficient M is greater than or equal to the preset contact influence threshold N, a vibration massage operation is performed on the body part of the patient that is in contact with the walking-assist robot and a good human-machine contact result is output; if the contact influence coefficient M is less than the preset contact influence threshold N, no adjustment is required and a good human-machine contact result is output.

[0049] Preferably, a logic AND gate judgment module is obtained and a signal connection link is established between the logic AND gate judgment module and the walking-assisting robot, wherein the logic AND gate judgment module includes a first logic AND gate and a second logic AND gate;

[0050] When the first logic AND gate receives the wearing completion result and the good human-machine contact result, it outputs a good wearing contact state result;

[0051] When the second logic AND gate receives the good wearing contact state result and the good walking state result, it outputs the good running state result.

[0052] In the second aspect, the present application provides a robot operation status monitoring system, which adopts the following technical solution:

[0053] A robot operation status monitoring system, comprising:

[0054] The wearing completion confirmation module is used to detect whether the patient to be tested is wearing the walking robot properly, and output the wearing completion result after confirming that the patient to be tested is wearing the walking robot properly;

[0055] an obstacle detection area determination module, configured to obtain patient information and determine an obstacle detection area based on the patient information;

[0056] The obstacle crossing analysis module is used to detect whether there is an obstacle in the obstacle detection area. If not, a good road condition result is output. If so, it is determined whether the obstacle is crossed or bypassed. If it is an obstacle crossing, the force of each muscle of the patient's leg is detected when the patient crosses the obstacle, and it is determined whether the patient's own muscle force can successfully cross the obstacle. If not, the walking robot applies a driving force to the patient to help the patient complete the crossing. When the patient crosses the obstacle, the crossing completion result is output;

[0057] The bypass obstacle analysis module is configured to plan a bypass route for the patient to be tested to obtain planned path information if there is a bypass obstacle, and the patient to be tested walks based on the planned path information, and outputs a bypass success result when the patient to be tested bypasses the bypass obstacle;

[0058] A walking state judgment module is configured to output an obstacle avoidance success result after receiving a crossing success result or a detour success result, and to output a walking state good result after receiving a road condition good result or an obstacle avoidance success result;

[0059] The human-machine contact analysis module is used to detect the contact influence of the walking robot on the patient to be tested when the patient is using the walking robot, obtain the contact influence coefficient M, determine whether the walking robot needs to be adjusted based on the contact influence coefficient M, and output a good human-machine contact result;

[0060] The running status judgment module is configured to output a good wearing contact status result after receiving a wearing completion result and a good human-machine contact result, and to output a good running status result after receiving a good wearing contact status result and a good walking status result.

[0061] In summary, the present application includes at least one of the following beneficial technical effects:

[0062] 1. The age influence coefficient AN is obtained through the patient's age information, the disease type influence coefficient AL is obtained through the patient's disease type information, the disease degree influence coefficient AC is obtained through the patient's disease severity information, the self-disease influence coefficient AH of the patient to be tested is obtained according to the disease type influence coefficient AL and the disease severity influence coefficient AC, the self-walking influence coefficient AX of the patient to be tested is obtained through the patient's own walking speed information, the patient's own influence coefficient S is obtained by combining the age influence coefficient AN, the self-disease influence coefficient AH and the self-walking influence coefficient AX, the obstacle detection area of ​​the walking robot is determined according to the patient's age, the patient's disease type, the patient's disease severity and the patient's own walking speed, thereby improving the accuracy of obstacle detection when the patient uses the walking robot, thereby improving the accuracy of the robot's operating status monitoring;

[0063] 2. By detecting the distance between the patient to be tested and the obstacle to be crossed, that is, the obstacle difference distance value P, the height from the current standing horizontal plane of the patient to be tested to the top of the obstacle to be crossed is detected to obtain the obstacle difference height value h1, and the obstacle safety height difference value h2 is preset. When the patient to be tested crosses the obstacle with the obstacle difference height value h1, the patient to be tested may touch the top of the obstacle to be crossed, thereby increasing the risk of falling and colliding. Therefore, the obstacle safety height difference value h2 is superimposed for calculation to ensure that the patient to be tested can completely cross the obstacle before crossing the obstacle, thereby improving the safety of the patient to be tested in crossing the obstacle. According to the obstacle difference distance value P , the obstacle height difference h1 and the obstacle safety height difference h2 are used to calculate the required crossing slope i1, and the electromyographic sensor is used to detect the force of each muscle in the leg of the patient to be tested, so as to calculate the slope that the patient to be tested can cross by exerting muscle force on the legs when crossing the obstacle, that is, the patient's ability slope i2. The patient's ability slope i2 is compared with the required crossing slope i1 to determine whether the patient to be tested can cross the obstacle. If not, the driver installed in the walking-assist robot applies driving force to the patient's legs to help the patient to cross the obstacle, thereby further improving the accuracy of the robot's operating status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a schematic diagram of the flow of the robot operation status monitoring method mainly embodied in this embodiment;

[0065] Figure 2 This is a schematic diagram of the modules of the robot operation status monitoring system mainly embodied in this embodiment.

[0066] Figure numerals: 1. Wearing completion confirmation module; 2. Obstacle detection area determination module; 3. Obstacle crossing analysis module; 4. Obstacle bypass analysis module; 5. Walking state judgment module; 6. Human-machine contact analysis module; 7. Operation state judgment module. DETAILED DESCRIPTION

[0067] The present application is further described in detail below in conjunction with the accompanying drawings.

[0068] The embodiment of the present application discloses a method for monitoring the operating status of a robot.

[0069] A method for monitoring the operation status of a robot comprises the following steps:

[0070] Reference Figure 1 Step S1, detects whether the patient to be tested wears the walking robot properly, and outputs the wearing completion result after confirming that the patient to be tested wears the walking robot properly. Step S1 specifically includes the following sub-steps:

[0071] Step S11, after the patient to be tested wears the walking-assisting robot, the battery power of the walking-assisting robot is detected to obtain the machine power information.

[0072] Step S12, based on the machine power information, determine the usage time of the walking-assist robot to obtain the estimated usable time, compare the estimated usable time with the preset usable time threshold, and output a sufficient power result if the estimated usable time is greater than or equal to the preset usable time threshold.

[0073] Step S13, detecting whether the motion data acquisition sensor unit of the patient to be tested fits the limb of the patient to be tested. If so, outputting a successful fitting result of the motion acquisition sensor. The motion data acquisition sensor unit refers to a force sensor, an angle sensor, an acceleration sensor, etc.

[0074] Step S14, detect whether the environmental data acquisition sensor unit of the patient to be tested is blocked. If it is not blocked, output the normal result of the environmental sensor. The environmental data acquisition sensor unit refers to laser radar, camera, millimeter wave radar, ultrasonic sensor, infrared sensor, sound sensor, etc.

[0075] Step S15, when receiving the result of sufficient power, the result of successful fitting of the motion acquisition sensor, and the result of normal environmental sensor, it means that the patient to be tested wears the walking-assist robot properly, and the wearing completion result is output.

[0076] Reference Figure 1 , step S2, obtaining patient information, and determining the obstacle detection area based on the patient information. Step S2 specifically includes the following sub-steps:

[0077] Step S21, obtaining patient information, the patient information includes patient age information, patient disease information and patient walking speed information, the patient disease information includes patient disease type information and patient disease severity information.

[0078] Step S22, obtaining an age influence coefficient AN based on the patient's age information, wherein the older the patient to be tested is, the greater the age influence coefficient AN is.

[0079] Step S23, obtaining a disease type walking influence data set, where the disease type walking influence data set includes information of each disease type and a type influence degree corresponding to each disease type.

[0080] Step S24, based on the disease type walking impact data set, the type impact degree corresponding to the disease type information of the patient to be tested is searched to obtain the disease type impact coefficient AL. For example, if the disease type information of the patient to be tested is hemiplegia, then the disease type impact coefficient AL is 5.

[0081] Step S25, based on the patient's disease severity information, the disease severity influence coefficient AC is obtained. Among them, the more serious the disease severity of the patient to be tested, the greater the disease severity influence coefficient AC. In actual application, the disease type information of the patient to be tested is hemiplegia, and hemiplegia is divided into six stages, from level one (completely no movement) to level six (close to normal). Among them, if the patient's disease severity information is level one, the disease severity influence coefficient is 5, if the patient's disease severity information is level two, the disease severity influence coefficient AC is 4, if the patient's disease severity information is level three, the disease severity influence coefficient is 3, if the patient's disease severity information is level four, the disease severity influence coefficient is 2, if the patient's disease severity information is level five, the disease severity influence coefficient is 1, and if the patient's disease severity information is level six, the disease severity influence coefficient is close to 0.

[0082] Step S26, according to the disease type influence coefficient AL and the disease degree influence coefficient AC, based on the self-disease function AH=ε1×AL+ε2×AC, the self-disease influence coefficient AH of the patient to be tested is calculated, wherein ε1 and ε2 are proportional factors and are both greater than 0. For example, the disease type information of the patient to be tested is hemiplegia, the disease degree information of the patient is level 2, ε1 is 1, ε2 is 0.5, then the self-disease influence coefficient AH of the patient to be tested is 1×5+0.5×4=7.

[0083] Step S27, obtaining the self-walking influence coefficient AX of the patient to be tested based on the patient's own walking speed information, wherein the faster the patient's own walking speed is, the smaller the self-walking speed influence coefficient is.

[0084] Step S28, according to the age influence coefficient AN, the disease influence coefficient AH and the walking influence coefficient AX, the patient's own influence coefficient S is calculated based on the obstacle detection function S = a1 × AN + a2 × AH + a3 × AX, where a1, a2, and a3 are proportional factors and are all greater than 0. For example, if the patient's age information of the patient to be tested is 60 years old, the age influence coefficient is 5, the disease type information of the patient to be tested is hemiplegia, the patient's disease degree information is level 2, the disease influence coefficient AH is 7, the patient's own walking speed information is 0.4m / s, the walking influence coefficient AX is 4, a1 is 2, a2 ​​is 2, a3 is 1, and the patient's own influence coefficient S = 2 × 5 + 2 × 7 + 1 × 4 = 28.

[0085] Step S29, determining the obstacle detection area based on the patient's own influence coefficient S, wherein the larger the patient's own influence coefficient S is, the larger the obstacle detection area is.

[0086] In actual use, when the patient uses the walking robot, the size of the obstacle detection area of ​​the walking robot is fixed. In the embodiment of the present application, the age influence coefficient AN is obtained through the patient's age information, the disease type influence coefficient AL is obtained through the patient's disease type information, and the disease degree influence coefficient AC is obtained through the patient's disease severity information. The patient's own disease influence coefficient AH is obtained according to the disease type influence coefficient AL and the disease severity influence coefficient AC, and the patient's own walking influence coefficient AX is obtained according to the patient's own walking speed information. The patient's own influence coefficient S is obtained by combining the age influence coefficient AN, the patient's own disease influence coefficient AH and the self-walking influence coefficient AX. The obstacle detection area of ​​the walking robot is determined according to the patient's age, the patient's disease type, the patient's disease severity and the patient's own walking speed, which improves the accuracy of obstacle detection when the patient uses the walking robot, thereby improving the accuracy of the robot's operating status monitoring.

[0087] Reference Figure 1 , step S3, detect whether there is an obstacle in the obstacle detection area, if not, output the result of good road condition, if yes, determine whether to cross the obstacle or bypass the obstacle, if it is to cross the obstacle, detect the strength of each muscle of the patient's leg when the patient crosses the obstacle, and determine whether the patient's own muscle strength can successfully cross the obstacle, if not, apply driving force to the patient based on the walking robot to help the patient to complete the crossing, and output the result of crossing completion after the patient crosses the obstacle. Step S3 specifically includes the following sub-steps:

[0088] Step S31, detect whether there is an obstacle in the obstacle area. If there is no obstacle, output the result of good road condition. If there is an obstacle, determine whether the patient to be tested needs to cross or detour when passing the obstacle. If the patient to be tested needs to cross, output the obstacle crossing information, and record the obstacle to be crossed as the crossed obstacle. If the patient to be tested needs to detour, output the obstacle detour information, and record the obstacle to be detour as the detour obstacle.

[0089] Step S32: When the patient to be tested walks to the side of the obstacle, the distance between the patient to be tested and the obstacle is detected to obtain the obstacle difference distance value P.

[0090] Step S33, detecting the height from the current standing level of the patient to the top of the obstacle to obtain the obstacle height difference value h1, obtaining the obstacle safety height difference value h2 and storing it.

[0091] Step S34, based on the obstacle distance difference value P, the obstacle height difference value h1 and the obstacle safety height difference value h2, the slope function i1=(h1+h2) / P×100% is used to calculate the slope that the patient needs to cross when crossing the obstacle to obtain the required crossing slope i1.

[0092] Step S35, based on the electromyographic sensors pre-installed on the leg muscles of the patient to be tested, the force of each muscle of the leg of the patient to be tested is detected to obtain muscle force information, and the muscle force information includes the position of the force-exerting muscles and the force degree of each force-exerting muscle.

[0093] Step S36, obtaining the leg muscle force sample data of the patient to be tested and the leg lifting height sample data of the patient to be tested under different leg muscle force conditions when walking, based on the leg muscle force sample data of the patient to be tested and the leg lifting height sample data of the patient to be tested under different leg muscle force conditions when walking, calculating the slope that the patient to be tested can cross to obtain multiple characteristic crossing slope sample data, and establishing a characteristic muscle force walking vector model based on the leg muscle force sample data of the patient to be tested, the leg lifting height sample data and the characteristic crossing slope sample data.

[0094] Step S37, according to the position of the force muscles and the force degree of each force muscle, based on the characteristic muscle force walking vector model, analyze the slope that the patient to be tested can cross by exerting muscle force on the legs when crossing an obstacle to obtain the patient's ability slope i2.

[0095] Step S38, compare the patient's ability slope i2 with the crossing requirement slope i1. If the patient's ability slope i2 is greater than the crossing requirement slope i1, no adjustment is required. If the patient's ability slope i2 is less than or equal to the crossing requirement slope i1, calculate the difference between the patient's ability slope i2 and the crossing requirement slope i1 to obtain the slope difference i, and analyze the difference in leg muscle force when the patient needs to increase the slope to the slope difference i when crossing the domain based on the characteristic muscle force walking vector model.

[0096] Step S39, driving the human legs based on the driver on the walking robot, wherein the driving force of the driver on the human legs is the difference in the strength of the leg muscles. When the patient to be tested crosses the obstacle, the crossing completion result is output.

[0097] In actual application, by detecting the distance between the patient to be tested and the obstacle to be crossed, that is, the obstacle difference distance value P, the height from the current standing horizontal plane of the patient to be tested to the top of the obstacle to be crossed is detected to obtain the obstacle difference height value h1, and the obstacle safety height difference value h2 is preset. When the patient to be tested crosses the obstacle with the obstacle difference height value h1, the patient to be tested may touch the top of the obstacle to be crossed, thereby increasing the risk of falling and collision. Therefore, the obstacle safety height difference value h2 is superimposed for calculation to ensure that the patient to be tested can completely cross the obstacle before crossing the obstacle, thereby improving the safety of the patient to be tested in crossing the obstacle. The required crossing slope i1 is calculated based on the distance value P, the obstacle height difference h1 and the obstacle safety height difference h2. The force exerted by each muscle of the patient's leg is detected based on the electromyographic sensor, so as to calculate the slope that the patient can cross by exerting muscle force on the legs when crossing the obstacle, that is, the patient's ability slope i2. The patient's ability slope i2 is compared with the required crossing slope i1 to determine whether the patient can cross the obstacle. If not, the driver installed in the walking-assist robot applies driving force to the patient's legs to help the patient cross the obstacle, thereby improving the accuracy of the robot's operating status monitoring.

[0098] Reference Figure 1 In step S4, if the obstacle is bypassed, the bypass route of the patient to be tested is planned to obtain the planned path information, and the patient to be tested walks based on the planned path information. When the patient to be tested bypasses the obstacle, the bypass success result is output. Step S4 specifically includes the following sub-steps:

[0099] Step S41: If it is a bypass obstacle, the degree of obstruction of the bypass obstacle on the current walking path of the patient to be tested is detected to obtain bypass obstacle obstruction degree information.

[0100] Step S42, presetting a bypass obstacle occlusion degree threshold based on the volume of the patient to be tested, wherein the larger the volume of the patient to be tested is, the smaller the bypass obstacle occlusion degree threshold is.

[0101] Step S43, compare the bypass obstacle obstruction degree information with the preset bypass obstacle obstruction degree threshold. If the bypass obstacle obstruction degree information is less than the preset bypass obstacle obstruction degree threshold, the patient to be tested bypasses the bypass obstacle on the current walking path and moves forward. When the patient to be tested bypasses the bypass obstacle and continues to walk on the current walking path, a bypass success result is output.

[0102] Step S44, if the bypass obstacle occlusion degree information is greater than or equal to the preset bypass obstacle occlusion degree threshold, the walking path of the patient to be tested needs to be replanned to obtain the planned path information, and the bypass success result is output after the patient to be tested walks based on the planned path information.

[0103] Reference Figure 1 Step S5, when receiving a successful crossing result or a successful detour result, outputting a successful obstacle avoidance result, and when receiving a good road condition result or a successful obstacle avoidance result, outputting a good walking state result. Step S5 specifically includes the following sub-steps:

[0104] Step S51, obtaining a logic OR gate judgment module and establishing a signal connection link between the logic OR gate judgment module and the walking-assisting robot, wherein the logic OR gate judgment module includes a first logic OR gate and a second logic OR gate.

[0105] Step S52: When the first logic OR gate receives a successful crossing result or a successful detour result, it outputs a successful obstacle avoidance result.

[0106] Step S53, when the second logic OR gate receives the good road condition result or the obstacle avoidance success result, it outputs the good walking state result.

[0107] Reference Figure 1 Step S6, when the patient is using the walking robot, the contact influence of the walking robot on the patient to be tested is detected to obtain a contact influence coefficient M, based on the contact influence coefficient M, it is determined whether the walking robot needs to be adjusted, and a good human-machine contact result is output. Step S6 specifically includes the following sub-steps:

[0108] Step S61, real-time detection of the body contact between the walking-assist robot and the patient to be tested when the patient is wearing the walking-assist robot to obtain human-machine contact information, wherein the human-machine contact information includes the human-machine contact strength and the human-machine contact duration.

[0109] Step S62, obtaining a contact force influence coefficient BL based on the human-machine contact force, wherein the greater the human-machine contact force, the greater the contact force influence coefficient BL.

[0110] Step S63, obtaining a contact duration influence coefficient BC based on the human-machine contact duration, wherein the longer the human-machine contact duration, the greater the contact duration influence coefficient BC.

[0111] Step S64, according to the contact force influence coefficient BL and the contact duration influence coefficient BC, the contact influence coefficient M is calculated based on the human-machine contact function M=λ1×BC+λ2×BL, where λ1 and λ2 are proportional factors and are both greater than 0. For example, if the human-machine contact force is 40N, the contact force influence coefficient is 1.5, and if the human-machine contact duration is 60min, the contact duration influence coefficient is 2, where λ1 is 1 and λ2 is 2, then the contact influence coefficient M=1×1.5+2×2=5.5.

[0112] Step S65, compare the contact influence coefficient M with the preset contact influence threshold N. If the contact influence coefficient M is greater than or equal to the preset contact influence threshold N, perform a vibration massage operation on the body part of the patient that is in contact with the walking-assist robot and output a good human-machine contact result. If the contact influence coefficient M is less than the preset contact influence threshold N, no adjustment is required and a good human-machine contact result is output.

[0113] Reference Figure 1 Step S7, after receiving the wearing completion result and the good human-machine contact result, output the good wearing contact state result; after receiving the good wearing contact state result and the good walking state result, output the good running state result. Step S7 specifically includes the following sub-steps:

[0114] Step S71, obtaining a logic AND gate judgment module and establishing a signal connection link between the logic AND gate judgment module and the walking-assisting robot, wherein the logic AND gate judgment module includes a first logic AND gate and a second logic AND gate.

[0115] Step S72, when the first logic AND gate receives the wearing completion result and the human-machine contact good result, it outputs the wearing contact state good result.

[0116] Step S73, when the second logic AND gate receives the good result of the wearing contact state and the good result of the walking state, it outputs the good result of the running state.

[0117] The embodiment of the present application also discloses a robot operation status monitoring system.

[0118] Reference Figure 2 , a robot operation status monitoring system comprises:

[0119] The wearing completion confirmation module is used to detect whether the patient to be tested is wearing the walking robot reasonably, and output the wearing completion result after confirming that the patient to be tested is wearing the walking robot reasonably.

[0120] The obstacle detection area determination module is configured to obtain patient information and determine the obstacle detection area based on the patient information.

[0121] The obstacle crossing analysis module is used to detect whether there is an obstacle in the obstacle detection area. If not, a good road condition result is output. If so, it is determined whether the obstacle is crossed or bypassed. If it is an obstacle crossing, the force of each muscle in the leg of the patient to be tested is detected when the patient to be tested crosses the obstacle, and it is determined whether the patient to be tested can successfully cross the obstacle with his own muscle force. If not, the walking robot applies driving force to the patient to be tested to help the patient to complete the crossing. When the patient to be tested crosses the obstacle, the crossing completion result is output.

[0122] The bypass obstacle analysis module is configured to plan a bypass route for the patient to be tested to obtain planned path information if there is a bypass obstacle. The patient to be tested walks based on the planned path information. When the patient to be tested bypasses the bypass obstacle, a bypass success result is output.

[0123] The walking state judgment module is configured to output a successful obstacle avoidance result after receiving a successful crossing result or a successful detour result, and to output a good walking state result after receiving a good road condition result or a successful obstacle avoidance result.

[0124] The human-machine contact analysis module is used to detect the contact impact of the walking robot on the patient when the patient is using the walking robot, and obtain the contact impact coefficient M. Based on the contact impact coefficient M, it is determined whether the walking robot needs to be adjusted, and a good human-machine contact result is output.

[0125] The running status judgment module is configured to output a good wearing contact status result after receiving a wearing completion result and a good human-machine contact result, and to output a good running status result after receiving a good wearing contact status result and a good walking status result.

[0126] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A robot operation status monitoring method and system, characterized in that: The following steps are involved: Detect whether the patient to be tested is wearing the walking-assist robot properly, and output the wearing completion result after confirming that the patient to be tested is wearing the walking-assist robot properly; Acquire patient information, and determine an obstacle detection area based on the patient information; Detect whether there is an obstacle in the obstacle detection area. If not, output a good road condition result. If so, determine whether to cross the obstacle or bypass the obstacle. If it is to cross the obstacle, detect the force of each muscle of the patient's leg when the patient crosses the obstacle, and determine whether the patient's own muscle force can successfully cross the obstacle. If not, apply driving force to the patient based on the walking robot to help the patient complete the crossing. When the patient crosses the obstacle, output the crossing completion result; If it is a detour obstacle, the detour route of the patient to be tested is planned to obtain the planned path information, and the patient to be tested walks based on the planned path information. When the patient to be tested bypasses the detour obstacle, a detour success result is output; When receiving a successful crossing result or a successful detour result, it outputs a successful obstacle avoidance result; when receiving a good road condition result or a successful obstacle avoidance result, it outputs a good walking state result; When the patient is using the walking robot, the contact influence of the walking robot on the patient to be tested is detected to obtain a contact influence coefficient M, based on which it is determined whether the walking robot needs to be adjusted, and a good human-machine contact result is output; When the wearing completion result and the good human-machine contact result are received, the wearing contact state good result is output; when the wearing contact state good result and the walking state good result are received, the running state good result is output.

2. A robot operation status monitoring method according to claim 1, characterized in that: The steps of detecting whether the patient to be tested is wearing the walking-assist robot properly and outputting the wearing completion result after confirming that the patient to be tested is wearing the walking-assist robot properly include: After the patient to be tested wears the walking robot, the battery power of the walking robot is detected to obtain the power information of the machine; The usage time of the walking-assisting robot is determined based on the machine power information to obtain an estimated usable time, and the estimated usable time is compared with a preset usable time threshold. If the estimated usable time is greater than or equal to the preset usable time threshold, a sufficient power result is output; Detect whether the motion data acquisition sensor unit of the patient to be tested fits the limb of the patient to be tested, and if so, output a successful fitting result of the motion acquisition sensor; Detect whether the environmental data acquisition sensor unit of the patient to be tested is blocked, and if not blocked, output the normal result of the environmental sensor; When receiving the result of sufficient power, successful fitting of the motion acquisition sensor and normal result of the environmental sensor, it means that the patient to be tested wears the walking-assist robot properly and the wearing completion result is output.

3. A robot operation status monitoring method according to claim 2, characterized in that: The steps of obtaining patient information and determining an obstacle detection area based on the patient information specifically include: Acquire patient information, wherein the patient information includes patient age information, patient disease information, and patient walking speed information, wherein the patient disease information includes patient disease type information and patient disease severity information; The age influence coefficient AN is obtained based on the patient's age information; Acquire a disease type walking influence data set, wherein the disease type walking influence data set includes information of each disease type and a type influence degree corresponding to each disease type; Based on the disease type walking impact data set, find the type impact degree corresponding to the disease type information of the patient to be tested to obtain the disease type impact coefficient AL; Based on the patient's disease severity information, the disease severity influence coefficient AC is obtained; According to the disease type influence coefficient AL and the disease degree influence coefficient AC, the self-disease influence coefficient AH of the patient to be tested is calculated based on the self-disease function AH=ε1×AL+ε2×AC, where ε1 and ε2 are proportional factors and are both greater than 0; Based on the patient's own walking speed information, the patient's own walking influence coefficient AX is obtained; According to the age influence coefficient AN, the disease influence coefficient AH and the walking influence coefficient AX, the patient's own influence coefficient S is calculated based on the obstacle detection function S = a1 × AN + a2 × AH + a3 × AX, where a1, a2, and a3 are proportional factors and are all greater than 0; The obstacle detection area is determined based on the patient's own influence coefficient S, wherein the larger the patient's own influence coefficient S is, the larger the obstacle detection area is.

4. A robot operation status monitoring method according to claim 3, characterized in that: Whether there is an obstacle in the obstacle detection area is detected, if not, good road condition information is output, if yes, it is determined whether to cross the obstacle or to bypass the obstacle, if it is to cross the obstacle, the force of each muscle of the leg of the patient to be tested is detected when the patient to be tested crosses the obstacle, and it is determined whether the muscle force of the patient to be tested can successfully cross the obstacle, if not, the walking robot is used to apply driving force to the patient to be tested to help the patient to complete the crossing, and the steps of outputting the crossing completion result after the patient to be tested crosses the obstacle specifically include: Detect whether there is an obstacle in the obstacle area. If there is no obstacle, output a good road condition result. If there is an obstacle, determine whether the patient to be tested needs to cross or detour when passing the obstacle. If the patient to be tested needs to cross, output obstacle crossing information, and record the obstacle to be crossed as a crossed obstacle. If the patient to be tested needs to detour, output obstacle detour information, and record the obstacle to be detour as a detour obstacle. When the patient to be tested walks to the side of the obstacle, the distance between the patient to be tested and the obstacle is detected to obtain the obstacle difference distance value P; Detect the height from the current standing level of the patient to the top of the obstacle to obtain the obstacle height difference value h1, obtain the obstacle safety height difference value h2 and store it; Based on the obstacle distance difference value P, the obstacle height difference value h1 and the obstacle safety height difference value h2, the slope function i1=(h1+h2) / P×100% is used to calculate the slope that the patient to be tested needs to cross when crossing the obstacle to obtain the required crossing slope i1; Based on the electromyographic sensors pre-installed on the leg muscles of the patient to be tested, the force of each muscle of the leg of the patient to be tested is detected to obtain muscle force information, wherein the muscle force information includes the position of the force-generating muscles and the force degree of each force-generating muscle; Obtaining leg muscle force sample data of the patient to be tested and sample data of leg lifting height of the patient to be tested under different leg muscle force conditions when walking, calculating the slope that the patient to be tested can cross to obtain multiple characteristic crossing slope sample data based on the leg muscle force sample data of the patient to be tested and the sample data of leg lifting height of the patient to be tested under different leg muscle force conditions when walking, and establishing a characteristic muscle force walking vector model based on the leg muscle force sample data of the patient to be tested, the leg lifting height sample data and the characteristic crossing slope sample data; According to the position of the force-generating muscles and the force degree of each force-generating muscle, the slope that the patient can cross by exerting muscle force on the leg when crossing the obstacle is analyzed based on the characteristic muscle force walking vector model to obtain the patient's ability slope i2; Compare the patient's ability slope i2 with the crossing demand slope i1. If the patient's ability slope i2 is greater than the crossing demand slope i1, no adjustment is required. If the patient's ability slope i2 is less than or equal to the crossing demand slope i1, calculate the difference between the patient's ability slope i2 and the crossing demand slope i1 to obtain the slope difference i. Based on the characteristic muscle force walking vector model, analyze the difference in leg muscle force when the patient needs to increase the slope to the slope difference i when crossing the domain. The human legs are driven based on the driver on the walking robot, wherein the driving force of the driver on the human legs is the difference in the degree of force exerted by the leg muscles. When the patient to be tested crosses the obstacle, the crossing completion result is output.

5. A robot operation status monitoring method according to claim 4, characterized in that: If the obstacle is bypassed, the bypass route of the patient to be tested is planned to obtain the planned path information, and the patient to be tested walks based on the planned path information. When the patient to be tested bypasses the bypass obstacle, the steps of outputting the bypass success result specifically include: If it is a bypass obstacle, the degree of occlusion of the bypass obstacle on the current walking path of the patient to be tested is detected to obtain the bypass obstacle occlusion degree information; Presetting a bypass obstacle occlusion degree threshold based on the volume of the patient to be tested, wherein the larger the volume of the patient to be tested, the smaller the bypass obstacle occlusion degree threshold; The bypass obstacle occlusion degree information is compared with a preset bypass obstacle occlusion degree threshold. If the bypass obstacle occlusion degree information is less than the preset bypass obstacle occlusion degree threshold, the patient to be tested bypasses the bypass obstacle on the current walking path and moves forward. When the patient to be tested bypasses the bypass obstacle and continues to walk on the current walking path, a bypass success result is output; If the bypass obstacle occlusion degree information is greater than or equal to the preset bypass obstacle occlusion degree threshold, the walking path of the patient to be tested needs to be replanned to obtain the planned path information, and the bypass success result is output after the patient to be tested walks based on the planned path information.

6. A robot operation status monitoring method according to claim 5, characterized in that: The steps of outputting a successful obstacle avoidance result after receiving a successful crossing result or a successful detour result, and outputting a good walking state result after receiving a good road condition result or a successful obstacle avoidance result specifically include: Acquire a logic OR gate judgment module and establish a signal connection link between the logic OR gate judgment module and the walking-assisting robot, wherein the logic OR gate judgment module includes a first logic OR gate and a second logic OR gate; When the first logic OR gate receives the crossing success result or the detour success result, it outputs an obstacle avoidance success result; When the second logic OR gate receives the good road condition result or the successful obstacle avoidance result, it outputs a good walking state result.

7. A robot operation status monitoring method according to claim 6, characterized in that: When a patient is using a walking robot, the contact influence of the walking robot on the patient to be tested is detected to obtain a contact influence coefficient M, and based on the contact influence coefficient M, whether the walking robot needs to be adjusted, and outputting a good result of human-machine contact, specifically comprising: Real-time detection of the contact between the walking robot and the patient's body during the patient's wearing of the walking robot to obtain human-machine contact information, wherein the human-machine contact information includes human-machine contact strength and human-machine contact duration; The contact force influence coefficient BL is obtained based on the human-machine contact force, wherein the greater the human-machine contact force, the greater the contact force influence coefficient BL; The contact time influence coefficient BC is obtained based on the human-machine contact time, wherein the longer the human-machine contact time, the greater the contact time influence coefficient BC; According to the contact force influence coefficient BL and the contact duration influence coefficient BC, the contact influence coefficient M is calculated based on the human-machine contact function M=λ1×BC+λ2×BL, where λ1 and λ2 are proportional factors and are both greater than 0; The contact influence coefficient M is compared with the preset contact influence threshold N. If the contact influence coefficient M is greater than or equal to the preset contact influence threshold N, a vibration massage operation is performed on the body part of the patient that is in contact with the walking-assist robot and a good human-machine contact result is output; if the contact influence coefficient M is less than the preset contact influence threshold N, no adjustment is required and a good human-machine contact result is output.

8. A robot operation status monitoring method according to claim 7, characterized in that: The steps of outputting a good wearing contact state result after receiving a wearing completion result and a good human-machine contact result, and outputting a good running state result after receiving a good wearing contact state result and a good walking state result specifically include: Acquire a logic AND gate judgment module and establish a signal connection link between the logic AND gate judgment module and the walking-assisting robot, wherein the logic AND gate judgment module includes a first logic AND gate and a second logic AND gate; When the first logic AND gate receives the wearing completion result and the good human-machine contact result, it outputs a good wearing contact state result; When the second logic AND gate receives the good wearing contact state result and the good walking state result, it outputs the good running state result.

9. A robot operation status monitoring system, characterized in that: The robot operation status monitoring system is used to implement the robot operation status monitoring method described in any one of claims 1 to 8, comprising: The wearing completion confirmation module is used to detect whether the patient to be tested is wearing the walking robot properly, and output the wearing completion result after confirming that the patient to be tested is wearing the walking robot properly; an obstacle detection area determination module, configured to obtain patient information and determine an obstacle detection area based on the patient information; The obstacle crossing analysis module is used to detect whether there is an obstacle in the obstacle detection area. If not, a good road condition result is output. If so, it is determined whether the obstacle is crossed or bypassed. If it is an obstacle crossing, the force of each muscle of the patient's leg is detected when the patient crosses the obstacle, and it is determined whether the patient's own muscle force can successfully cross the obstacle. If not, the walking robot applies a driving force to the patient to help the patient complete the crossing. When the patient crosses the obstacle, the crossing completion result is output; The bypass obstacle analysis module is configured to plan a bypass route for the patient to be tested to obtain planned path information if there is a bypass obstacle, and the patient to be tested walks based on the planned path information, and outputs a bypass success result when the patient to be tested bypasses the bypass obstacle; A walking state judgment module is configured to output an obstacle avoidance success result after receiving a crossing success result or a detour success result, and to output a walking state good result after receiving a road condition good result or an obstacle avoidance success result; The human-machine contact analysis module is used to detect the contact influence of the walking robot on the patient to be tested when the patient is using the walking robot, obtain the contact influence coefficient M, determine whether the walking robot needs to be adjusted based on the contact influence coefficient M, and output a good human-machine contact result; The running status judgment module is configured to output a good wearing contact status result after receiving a wearing completion result and a good human-machine contact result, and to output a good running status result after receiving a good wearing contact status result and a good walking status result.

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