CN108098736A - A kind of exoskeleton robot auxiliary device and method based on new perception - Google Patents
A kind of exoskeleton robot auxiliary device and method based on new perception Download PDFInfo
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
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- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/02—Sensing devices
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
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Abstract
The invention discloses a kind of exoskeleton robot auxiliary device and method based on new perception, described device includes：Left leg sensor assembly, right leg sensor assembly, data processing mainboard, host computer and ectoskeleton controller；The described method includes：S1, left leg sensor assembly and right leg sensor assembly gathered data simultaneously；S2 calculates the angle data of left and right leg knee, ankle according to gathered data respectively；S3 predicts the rotation angle in joint according to result of calculation and sends prediction data；S4 integrates the data of the left and right leg sended over, realizes time synchronization；S5 is sent to ectoskeleton controller or host computer.The present invention program realizes body gait sensory perceptual system using sensor, micro controller and wireless communication module, realizes the motion conditions of prediction human body lower limbs joint following a period of time, solves the problems, such as that ectoskeleton is uncoordinated with wearer motion.
Description
Technical field
The invention belongs to medical auxiliary robot, be related to a kind of exoskeleton robot auxiliary device based on new perception and
Method.
Background technology
Since 21 century, with the continuous maturation of robot technology, robot technology has obtained broader applications.From industry
Robot develops to service humanoid robot, and robot has gradually been entered among daily life, is brought to us many
It is convenient.With the raising of human substance life horizontal and becoming increasingly abundant for cultural life, future robot will be closer with the mankind
Link together.
Loading by lower limbs ectoskeleton is a kind of wearable robot, is supported by providing external force for human body, reaches reduction human body
Load, improve the lasting locomitivity of human body purpose, improve capacity for individual action, specially match somebody with somebody maintenance activity and the medical treatment side of helping the disabled
Face has wide practical use.
Loading by lower limbs ectoskeleton working mechanism captures human locomotion state in real time for sensory perceptual system, and controller generates control letter
Number driving mechanical bone follow human motion.But body gait is captured to output control signal and driving machine from sensory perceptual system
Structure (being usually motor or hydraulic pressure) driving ectoskeleton joint reaches target trajectory and is required to the regular hour, and this process human body is
Through moving to another state, therefore, mechanical exoskeleton gait lags behind wearer's gait, so as to disturb the walking row of wearer
For.
To solve the problems, such as this, the reference signal of control system should be ahead of the motion state of human body, it is necessary to human body
It moves gait and carries out real-time, accurately capture and prediction.
Application publication number is that the application for a patent for invention of CN103431929A discloses a kind of " enhanced power exoskeleton of strength
Walking step state cognitive method and device ", concretely comprises the following steps：Using foot bottom pressure sensor and knee joint encoder and gyroscope
It contacts to earth information, joint angles variation and the measurement of thigh and calf angular velocity information, is proposed according to man-machine portable rule double into pedestrian
Leg is stood, and the support of left leg, right leg are swung, and both legs support, right leg are in preceding, right leg support, left-leg movement, both legs support, and left leg exists
It is preceding to wait the walking sub- phase division methods of 5 gaits, and classified using machine learning algorithm to metrical information in 5 kinds of sub- phases of gait
Decision-making provides a kind of sub- phase identification method of data fusion walking step state.The invention can allow hydraulic control system to shrink in advance, carry
High man-machine portable speed, but because its reference information is limited, fast prediction can not be carried out to wearer's behavior, in quick movement according to
It is old to solve the problems, such as that mechanical exoskeleton gait lags.
Application publication number is that the application for a patent for invention of CN105150211A discloses a kind of " loading-type lower limb exoskeleton machine
The control system of people ", the system are made of two function same subsystems；Each subsystem is by signal pickup assembly, microcontroller
Device, movement executing mechanism and control algolithm composition；The lower limb exoskeleton robot is used to provide power-assisted for wearer's lower limb；
The signal pickup assembly is by diaphragm pressure sensor, magnetic degree sensor, multi-channel amplifier, Multi-channel low pass filter structure
Into the acquisition of the ankle joint angle signal of completion wearer's plantar pressure signal and lower limb exoskeleton robot；The movement is held
Row mechanism is made of servo-driver group and servo electric cylinders group；The control algolithm uses end model- following control algorithm.The invention
Adaptability of the robot to wearer's height can be improved, but it does not have corresponding prediction algorithm, can not realize under wearer
One step action prediction can not solve the problems, such as that mechanical exoskeleton gait lags.
Application publication number is that the application for a patent for invention of CN105078708A discloses a kind of " exoskeleton robot servo antrol
Device ", the device include upper arm, underarm, and upper arm lower end connected with underarm upper end using rotatable joint, the upper arm or under
Arm is equipped with the active briquetting that can be slided perpendicular to axis direction, and the active briquetting and human body are bound, on upper arm or underarm with
Active briquetting is correspondingly arranged there are two microswitch, is equipped with the power set that driving rotates between the upper and lower arms, two
Microswitch controls forward and reverse movement of power set respectively.The invention realizes that ectoskeleton and the real-time of human body are servo-actuated, and has good
Harmony well, synchronism, but it does not have corresponding prediction algorithm, and next action of unpredictable wearer can not solve
Mechanical exoskeleton disturbs the problem of walking behavior of wearer.
The content of the invention
Present invention aims at a kind of exoskeleton robot auxiliary device and method based on new perception is provided, to human body
The motion state of lower limb key position is acquired and predicts, realizes and provides reliable reference information for ectoskeleton controller,
It efficiently solve the problems, such ass that ectoskeleton is uncoordinated with wearer motion, while solves ectoskeleton gait and lag behind wearer's step
The problem of state.
In order to solve the above technical problems, the present invention adopts the following technical scheme that：A kind of ectoskeleton based on new perception
Robot assisted device and method, wherein, described device includes：Left leg sensor assembly, right leg sensor assembly, data processing
Mainboard, host computer and ectoskeleton controller；Wherein, the left leg sensor assembly and the right leg sensor assembly difference
It is connected with data processing mainboard；The data processing mainboard is connected respectively with host computer and ectoskeleton controller.
Further, the left and right leg sensor assembly is by thigh attitude transducer, shank attitude transducer and foot
Slap attitude transducer composition.
Further, the data processing mainboard is made of electrical level transferring chip, micro controller and wireless module.
Further, the host computer and the ectoskeleton controller are used to refer to the data of reception
And it after analyzing, is further processed.
The described method includes：S1, left leg sensor assembly and right leg sensor assembly gathered data simultaneously；S2, according to adopting
Collection data calculate the angle data of left and right leg knee, ankle respectively；S3 carries out the rotation angle in joint according to result of calculation
It predicts and sends prediction data；S4 integrates the data of the left and right leg sended over, realizes time synchronization；S5 is sent to
Ectoskeleton controller or host computer.
Further, in the step S3, Detecting (is come from by Nonlinear Time Series Analysis Takens algorithms
strange attractors in fluid turbulence[J].Takens F.Dy-namical Systems and
Turbulence, 1981,898:366-381.) rotation angle in joint is predicted.
The present invention has following advantageous effect compared with prior art：
The present invention program utilizes the rotation mounted on the attitude transducer acquisition human body lower limbs joint of ectoskeleton wearer's lower limb
Transhipment is dynamic, and the motion conditions of human body lower limbs joint following a period of time are predicted by particular algorithm, can be ectoskeleton controller
Reliable reference information is provided, solves the problems, such as that ectoskeleton is uncoordinated with wearer motion, while solves ectoskeleton gait
The problem of lagging behind wearer's gait.
Description of the drawings
Fig. 1 is the structure diagram based on new sensory perceptual system.
Fig. 2 is the mode of human body lower limbs wearable sensors.
Fig. 3 is the interface hardware block diagram of lower limb institute wearable sensors.
Fig. 4 is data processing motherboard hardware block diagram.
Fig. 5 is the process chart for gathering sensing data.
Fig. 6 is the resolving flow chart of pre-processing sensor data frame.
Fig. 7 is the data synchronization of left and right leg and forwarding process figure.
Specific embodiment
Below in conjunction with the accompanying drawings and specific embodiment the present invention is carried out in further detail with complete explanation.It is appreciated that
It is that specific embodiment described herein is only used for explaining the present invention rather than limitation of the invention.
Reference Fig. 1, a kind of exoskeleton robot auxiliary device and method based on new perception of the invention, wherein, institute
Stating device includes：Left leg sensor assembly, right leg sensor assembly, data processing mainboard, host computer and ectoskeleton control
Device；The left leg sensor assembly and the right leg sensor assembly are connected respectively with data processing mainboard；The data processing
Mainboard is connected respectively with host computer and ectoskeleton controller.
Wherein, the left and right leg sensor assembly is by thigh attitude transducer, shank attitude transducer and sole appearance
State sensor forms；With reference to Fig. 2, the dot of human body lower limbs represents attitude transducer, by same on the thigh, shank and sole of people
The mode of sample wears an attitude transducer respectively, can carry out simple algebraic operation by the data of two adjacent sensors and obtain
Go out the angle information of knee joint, ankle-joint；With reference to Fig. 3, each attitude transducer signal output using RS-232 agreements, in order to
It can communicate with the ARM micro controllers in data processing main plate, it is necessary to which level conversion, is TTL by the level conversion of RS-232 agreements
Level, using chip MAX3387, which has 3 level conversion passages；And the present invention sets sensor per 10ms at data
Manage mainboard transmission primaries sampled data.
The data processing mainboard is made of 2 electrical level transferring chips, 3 micro controllers and 1 wireless module；With reference to figure
4, electrical level transferring chip selection is MAX3387, as the terminal communicated between micro controller and attitude transducer, is responsible for TTL
Conversion between level and RS-232 level；Micro controller is using the STM32F407 microcontrollers of ARM kernels, 2#, 3# micro controller
Left foot, the sensing data of right crus of diaphragm are handled respectively, and passes through SPI modules and is sent to 1# micro controllers, for other two micro-control
The data of device are integrated, and are realized time synchronization and are passed through wireless module and be sent to the ectoskeleton controller or described upper
Computer.Wireless module is using XBee-PR900HP.
The host computer and the ectoskeleton controller are used to after the data of reception are referred to and analyzed,
It is further processed.
The cognitive method includes：Reference Fig. 5,
S1, left leg sensor assembly and right leg sensor assembly gathered data simultaneously；When Programmable detection to 3 sensors
Sampled data, which has all received, successfully just starts next-step operation.
S2 calculates the angle data of left and right leg knee, ankle according to gathered data respectively；MTI-30 attitude transducers
Data are encapsulated according to the data frame of specific format, and therefore, it is necessary to design software elder generation resolved data frames, can just calculate knee
Lid, the angle data of ankle.This method realizes data calculation by interrupting, and resolves flow with reference to Fig. 6, STM32 microcontrollers
UART modules often receive the data of 1 byte and just inspire and once interrupt, therefore, the frame data needs of sensor inspire repeatedly
Interruption, which could resolve, to be finished.
S3 predicts the rotation angle in joint according to result of calculation and sends prediction data；Pass through Nonlinear Time
Sequence analysis Takens algorithms can predict the rotation angle in joint.
According to Takens embedding theorems, for given time sequences y (t) ∈ R, 0 ＜ ＜ t ＜ ＜ n, appropriate delay is given
Time h and Embedded dimensions p can must then postpone vector
D (t)=[y (t), y (t-h) ..., y (t-h (p-1))]T (1)
Using phase space reconfiguration and Takens embedding theorems as theoretical foundation and mathematical tool, the data prediction realized herein
Algorithm flow is as follows：
S31, in each sampling instant t, t >=hp obtains a delay vector D (t)；
S32 calculates current time D (t) and all D (i) observed before, the Euclidean distance between hp ＜ ＜ i ＜ ＜ t
δ (i)=| | D (t)-D (i) | |；
S33 finds M delay vector for being capable of best match with D (t)
M delay vector i.e. minimum with current time D (t) Euclidean distances, wherein
S34, calculating parameter N：
S35 calculates weight factor wj：
S36 calculates the prediction of k ranksI.e.
The algorithm it can be seen from algorithm flow is it needs to be determined that parameter k, h, p and M, since Takens embedding theorems are to phase space
The no theoretic suggestion of selection of reconstruction parameter, selects reconstruction parameter as follows:It is first definite value by prediction order k values,
P ∈ [1,20], h ∈ [1,20], M ∈ [1,50] are taken, MATLAB emulation is carried out to measured data, finds out predictablity rate highest one
Parameter used when group parameter is realized as algorithm.Accuracy rate definition predicts error as shown in formula (3)When
When error is 0, accuracy rate 100%；When prediction of failure, PR then shows as negative
Prediction order k is mainly determined that the prediction order k in micro controller should by exoskeleton system executing agency response speed
It can be set by program, to adapt to different driving mechanisms.
S4 integrates the data of the left and right leg sended over, realizes time synchronization；Its flow is with reference to Fig. 7,2#, 3#
After premeasuring and other data are sent to 1# micro controllers by micro controller by SPI modules, the equally first resolved data frame of 1# micro controllers,
Then data are integrated, realizes time synchronization.
The final process result of data is sent to ectoskeleton controller or upper by S5,1# micro controller by wireless module
Computer.
The foregoing is merely the preferred embodiment of the present invention, are not intended to limit the invention, for those skilled in the art
For, the present invention can have various modifications and changes.All any modifications made within spirit and principles of the present invention are equal
Replace, improve etc., it should all be included in the protection scope of the present invention.
Claims (7)
1. a kind of exoskeleton robot auxiliary device based on new perception, which is characterized in that described device includes：Left leg sensing
Device module, right leg sensor assembly, data processing mainboard, host computer and ectoskeleton controller；Wherein, the left leg sensing
Device module and the right leg sensor assembly are connected respectively with data processing mainboard；The data processing mainboard respectively with upper meter
Calculation machine and ectoskeleton controller are connected.
A kind of 2. exoskeleton robot auxiliary device based on new perception according to claim 1, which is characterized in that institute
Left leg sensor assembly is stated to be made of thigh attitude transducer, shank attitude transducer and sole attitude transducer.
A kind of 3. exoskeleton robot auxiliary device based on new perception according to claim 1, which is characterized in that institute
Right leg sensor assembly is stated to be made of thigh attitude transducer, shank attitude transducer and sole attitude transducer.
A kind of 4. exoskeleton robot auxiliary device based on new perception according to claim 1, which is characterized in that institute
Data processing mainboard is stated to be made of electrical level transferring chip, micro controller and wireless module.
A kind of 5. exoskeleton robot auxiliary device based on new perception according to claim 1, which is characterized in that institute
After host computer is stated for the data of reception to be referred to and analyzed, it is further processed.
A kind of 6. exoskeleton robot auxiliary device based on new perception according to claim 1, which is characterized in that institute
After ectoskeleton controller is stated for the data of reception to be referred to and analyzed, it is further processed.
7. a kind of perception realized using a kind of exoskeleton robot auxiliary device based on new perception described in claim 1
Method, which is characterized in that the described method includes：S1, left leg sensor assembly and right leg sensor assembly gathered data simultaneously；
S2 calculates the angle data of left and right leg knee, ankle according to gathered data respectively；S3, according to rotation of the result of calculation to joint
Gyration is predicted and sends prediction data；S4 integrates the data of the left and right leg sended over, realizes that the time is same
Step；S5 is sent to ectoskeleton controller or host computer.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109793645A (en) * | 2019-01-21 | 2019-05-24 | 徐州医科大学附属医院 | A kind of auxiliary patient Parkinson gait rehabilitation training device |
| CN109814432A (en) * | 2018-12-18 | 2019-05-28 | 航天时代电子技术股份有限公司 | A kind of the communication frame generating method and communication means of human body servo antrol |
| CN110103215A (en) * | 2019-04-12 | 2019-08-09 | 上海中研久弋科技有限公司 | Multi-node collaborative perceives ectoskeleton Neural control system, method, equipment and medium |
| CN110193830A (en) * | 2019-05-24 | 2019-09-03 | 上海大学 | Ankle-joint gait prediction technique based on RBF neural |
| CN110561391A (en) * | 2019-09-24 | 2019-12-13 | 中国船舶重工集团公司第七0七研究所 | Inertia information feedforward control device and method for lower limb exoskeleton system |
| CN111714129A (en) * | 2020-05-07 | 2020-09-29 | 广西科技大学 | Human gait information acquisition system |
| CN112704491A (en) * | 2020-12-28 | 2021-04-27 | 华南理工大学 | Lower limb gait prediction method based on attitude sensor and dynamic capture template data |
-
2016
- 2016-11-24 CN CN201611044772.0A patent/CN108098736A/en active Pending
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109814432A (en) * | 2018-12-18 | 2019-05-28 | 航天时代电子技术股份有限公司 | A kind of the communication frame generating method and communication means of human body servo antrol |
| CN109814432B (en) * | 2018-12-18 | 2020-08-21 | 航天时代电子技术股份有限公司 | Human body follow-up control communication frame generation method and communication method |
| CN109793645A (en) * | 2019-01-21 | 2019-05-24 | 徐州医科大学附属医院 | A kind of auxiliary patient Parkinson gait rehabilitation training device |
| CN109793645B (en) * | 2019-01-21 | 2021-07-13 | 徐州医科大学附属医院 | Supplementary recovered trainer of parkinsonism people gait |
| CN110103215A (en) * | 2019-04-12 | 2019-08-09 | 上海中研久弋科技有限公司 | Multi-node collaborative perceives ectoskeleton Neural control system, method, equipment and medium |
| CN110193830A (en) * | 2019-05-24 | 2019-09-03 | 上海大学 | Ankle-joint gait prediction technique based on RBF neural |
| CN110193830B (en) * | 2019-05-24 | 2022-10-11 | 上海大学 | Ankle joint gait prediction method based on RBF neural network |
| CN110561391A (en) * | 2019-09-24 | 2019-12-13 | 中国船舶重工集团公司第七0七研究所 | Inertia information feedforward control device and method for lower limb exoskeleton system |
| CN111714129A (en) * | 2020-05-07 | 2020-09-29 | 广西科技大学 | Human gait information acquisition system |
| CN112704491A (en) * | 2020-12-28 | 2021-04-27 | 华南理工大学 | Lower limb gait prediction method based on attitude sensor and dynamic capture template data |
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