CN109696908A - Robot and its track setting method and system - Google Patents
Robot and its track setting method and system Download PDFInfo
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- CN109696908A CN109696908A CN201910047842.5A CN201910047842A CN109696908A CN 109696908 A CN109696908 A CN 109696908A CN 201910047842 A CN201910047842 A CN 201910047842A CN 109696908 A CN109696908 A CN 109696908A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0055—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot with safety arrangements
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0088—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
Abstract
The invention discloses a kind of robot and its track setting methods and system, the track setting method includes: to predict attitude angle step: using the attitude angle of particle filter algorithm prediction robot, the particle filter algorithm optimizes the step parameter of the weight formula of the particle filter algorithm using brainstorming optimization algorithm, which includes yaw angle, pitch angle and roll angle；Prediction track coordinate step: the starting track points, the attitude angle at current time and present speed of the robot are obtained, to predict the robot in the track coordinate of subsequent time.The present invention can adjust the driving direction and travel speed of robot so that prediction track is more accurate using the step parameter in the weight formula of brainstorming optimization algorithm optimized particle filter algorithm to facilitate robot in the process of moving in time.It the composite can be widely applied to automatic Pilot technical field.
Description
Technical field
The present invention relates to field of artificial intelligence, more particularly, to a kind of robot and its track setting method and are
System.
Background technique
Particle filter: probability density letter is approximately indicated by finding one group of random sample propagated in state space
Number replaces integral operation with sample average, and then obtains the process of the minimum variance estimate of system mode, these samples are vivid
Be known as " particle ", so be particle filter.
Brainstorming optimization algorithm: the Chinese of Brain Storm Optimization Algorithm, Yi Zhongxin
The intelligent optimization calculation method of type is mainly used for solving extensive higher-dimension Solving Multimodal Function problem.
GPS:Global Positioning System, global positioning system.
SLAM:simultaneous localization and mapping, synchronous superposition.
In sea, land and air transboundary scene, since control environment is more complicated, existing design scheme is generally all more expensive,
It is three-in-one together to be equivalent to an automobile, aircraft and steamer, three sets of engines, three sleeve gears and three sets of individually controls
Device.In order to reduce cost, engine, gear and controller can all be shared, effectively to save hardware cost, but if
After three sets of control systems are integrated, the nonlinear characteristic of system is stronger, and the control algolithm needed will be more complicated, than
It is such as improper using the relatively-stationary Kalman's control algolithm of parameter.
In emergency scene, in order to avoid the generation of peril, the speed of vehicle is not only needed to detect that, also wants pre- measuring car
Direction.In the prior art, have to the sea, land and air intelligence relief assistance operation of special screne to emergency depositary management reason very high
Requirement of real-time, the different surely timely acquisition of the complete parameter data of field scene, system must have enough three to dwell automatically
Ability in sampling continues through live posteriority sampling under the scene for obtaining partial data, makes up priori blind area and work on, make
Correct decision, to realize timely intelligence assistance.
Summary of the invention
The present invention is directed to solve at least some of the technical problems in related technologies.For this purpose, of the invention
One purpose is to provide a kind of robot and its track setting method and system.
The technical scheme adopted by the invention is that:
In a first aspect, the present invention provides a kind of track setting method of robot, which includes: prediction appearance
State angle step: using the attitude angle of particle filter algorithm prediction robot, which is calculated using brainstorming optimization
Method optimizes the step parameter of the weight formula of the particle filter algorithm, which includes yaw angle, pitch angle and roll angle；In advance
It surveys track coordinate step: the starting track points, the attitude angle at current time and present speed of the robot is obtained, to predict the machine
Track coordinate of the device people in subsequent time；
Wherein, which specifically includes: being generated in t moment according to the priori conditions probability of the attitude angle
N number of random sample, N number of random sample are referred to as particle；Left-right position according to the particle relative to previous actual path, will
The particle is divided into Left's particle and rightist particle；The Left grain of the t moment is calculated separately using posterior probability calculation formula
The weight of son and the rightist particle, wherein the posterior probability calculation formula are as follows:π is round
Frequency, w (i) are weight of i-th of particle in the prediction operation of last all particles linear superposition, and R is the standard of laser radar
Error amount, Di are the deviation values in the course line of i-th of particle and laser radar observation, and St is the step parameter of t moment, the St value
It is optimized using brainstorming optimization algorithm；According to the weight of Left's particle and the rightist particle, calculated using weighted average
The robot is calculated in the attitude angle at t+1 moment in method.
Wherein, the artificial sea, land and air three of the machine are dwelt robot, further include modal idenlification step before the prediction attitude angle step
It is rapid: to identify the current driving mode of the robot, which includes three types: first mode, second mode and third
Mode；The first mode is sea, land and empty three mode；The second mode is sea, land and sky, Hai Lu, empty sea, empty six moulds in land
State；The third mode is sea, land and sky, Hai Lu, empty sea, land sky, land-sea, empty nine land, air-sea mode；Prediction attitude angle step
It suddenly include: that the step parameter St of the weight formula is adjusted according to the preceding traveling mode, which is calculated using brainstorming optimization
Method is optimized for the fractional time sequence of the 1/k of mode transition total duration, k=4.
Wherein, the prediction attitude angle step further include: in next t moment, according to the priori conditions probability of the attitude angle,
N number of random particles are regenerated, recalculate the robot in the attitude angle at next t+1 moment according to multiple new particles.
Wherein, further include track optimization step after the prediction track coordinate step: being commented using plural score variance function
The velocity variations of the valence robot, the plural number score variance function calculation formula are as follows:M is score,
N is moment number, ξiFor each moment velocity amplitude,For speed average, ξiFor plural number, imaginary number indicates cross running speed, real number table
Show longitudinal driving speed；The travel speed for adjusting the robot meets minimum evaluation function, the minimum evaluation function is defined as:
So that the mould of the plural number score variance function is minimum.
Wherein, the traveled distance in the robot is further comprised the steps of: after the track optimization step more than scheduled error
When, re-execute the prediction attitude angle step, the prediction track coordinate step and the track optimization step.
Wherein, it further comprises the steps of: to break down in the robot after the track optimization step and can not execute the prediction appearance
When state angle step and the prediction track coordinate step, cloud database or local initialized data base are called, to obtain safe track
Data.
Second aspect, the present invention provide a kind of track setting system of robot, comprising: motor, sensor and processor,
The motor and the sensor are connect with the processor respectively；The motor, for driving the robot to travel；The sensor, including
GPS sensor and laser radar, for obtaining the location information of the robot and sending the processor to；The processor, is used for
Execute such as above-mentioned track setting method.
Wherein, track setting system further includes shift device, communication equipment and memory；The shift device, for connecing
The signal for receiving motor changes the travel speed of the robot, which connect with the processor, for being led to cloud
Letter；The memory is connect with the processor, for storing the aeronautical data of the robot.
The third aspect, the present invention provide a kind of robot, and system is arranged including such as above-mentioned track.
The beneficial effects of the present invention are:
The present invention predicts the attitude angle of robot by using improved particle filter algorithm, to predict that the robot exists
The track coordinate of subsequent time.The improved particle filter algorithm includes following improvement:
One, using the step parameter in the weight formula of brainstorming optimization algorithm optimized particle filter algorithm, so that in advance
Survey track is more accurate, can adjust the driving direction and traveling speed of robot in time to facilitate robot in the process of moving
Degree；
Two, the machine artificial three is dwelt robot, in the case where robot is in different operation modes, adjusts particle filter algorithm
Weight formula in step parameter, to meet robot in the travel speed and angle demand of different operation modes；
Three, there is the particle filter algorithm left and right to have other resampling mechanism, can prevent after being repeatedly iterated,
There is one direction degradation phenomena in particle；
Four, using the velocity variations of plural score variance function evaluation robot, the driving trace of robot can be made
It is smooth, reduce robot during mode transition due to the angular deviation of traveling cause greatly very much to travel it is unstable, to subtract
Accident in few driving process, guarantees driving safety.
Further, the present invention also has the function of emergency self-saving when encountering failure can not sample, and calls cloud number
According to library or local initialized data base, to obtain safe track data.
It the composite can be widely applied to automatic Pilot technical field.
Detailed description of the invention
Fig. 1 is the flow diagram of an embodiment of the track setting method of robot of the present invention；
Fig. 2 is the flow diagram of the step S11 of Fig. 1；
Fig. 3 is the schematic diagram of particle filter algorithm of the present invention；
Fig. 4 is the track emulation schematic diagram of an embodiment of the track setting method of robot of the present invention；
Fig. 5 is the structural schematic diagram of an embodiment of the track setting system of robot of the present invention.
Specific embodiment
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.
Embodiment one:
Referring to Fig. 1, Fig. 1 is the flow diagram of an embodiment of the track setting method of robot of the present invention.Such as Fig. 1
It is shown, the track setting method comprising steps of
S11: prediction attitude angle step: using the attitude angle of particle filter algorithm prediction robot, the particle filter algorithm
Optimize the step parameter of the weight formula of the particle filter algorithm using brainstorming optimization algorithm；
In step s 11, the attitude angle of robot includes: yaw angle, pitch angle and roll angle.It defines as follows respectively:
Yaw angle: the angle between the longitudinal axis and the earth arctic of robot.Value range be (- π, π], the east of body head
It is positive, west is negative.
Pitch angle: the longitudinal axis of robot and the angle of earth seawater levels.Value range be (- π, π], the top of body head
It is positive, lower section is negative.
Roll angle: the horizontal axis of robot and the angle of earth seawater levels.Value range be (- π, π], be under body dextrad
Just, it is negative upwards.
Referring to Figure 2 together and Fig. 3, as shown in Fig. 2, step S11 is specifically included:
S111: N number of random sample is generated according to the priori conditions probability of the attitude angle in t moment, N number of random sample
Referred to as particle；
S112: the left-right position according to the particle relative to previous actual path, by the particle be divided into Left's particle and
Rightist particle；
Wherein, the division methods of Left's particle and rightist particle can refer to the schematic diagram of Fig. 3.
S113: the power of Left's particle and the rightist particle of the t moment is calculated separately using posterior probability calculation formula
Value；
Wherein, the posterior probability calculation formula are as follows:π is pi, and w (i) is
Weight of the i particle in the prediction operation of last all particles linear superposition, R is the standard error value of laser radar, and Di is i-th
The deviation value in the course line of a particle and laser radar observation, St is the step parameter of t moment.
Wherein, step parameter St is optimized using brainstorming optimization algorithm.Specifically, can be used following steps into
Row:
(1) random initializtion is carried out to the step parameter St of all particles, obtains step parameter matrix；
(2) the step parameter matrix is clustered using clustering algorithm, obtains multiclass parameter matrix, the center of every class is
The first row of corresponding parameter matrix；
(3) it generates the first random number to be compared with pre-set first hyper parameter, if first random number is greater than
Equal to first hyper parameter, then the center of a kind of parameter matrix is randomly choosed, and randomly chooses one in one kind parameter matrix
A element replaces the element with a random number, generates a kind of new vector；Conversely,
(4) it generates the second random number to be compared with pre-set second hyper parameter, if second random number is greater than
Equal to second hyper parameter, then the center of a kind of parameter matrix is randomly choosed, and randomly chooses one in one kind parameter matrix
A element replaces the element with a random number, generates a kind of new vector；Conversely,
(5) center of two class parameter matrixs is randomly choosed, and obtains two new vectors using reorganization operation is intersected；
(6) judge whether to reach termination condition, if reaching termination condition, export optimization step parameter matrix；Conversely,
Step (2) then are returned to, carry out next round optimization.
S114: according to the weight of Left's particle and the rightist particle, which is calculated using Weighted Average Algorithm
Attitude angle of the people at the t+1 moment.
In particle filter algorithm, common problem is the sample degeneracy phenomenon of algorithmic statement agency, i.e., by several
After secondary iteration, in addition to a particle, remaining particle all only has small weight, and a large amount of calculating work is all used to excellent at this time
Change on the particle that those almost cut little ice to solution posterior probability density.It is most common reduce degradation phenomena method be
Resampling, its basic thought is to exclude those to have the particle of small weight, so that particle is focused on the particle with big weight
On.
For the one direction degradation phenomena for avoiding particle, step S11 also has the function of resampling, i.e., in next t moment, root
According to the priori conditions probability of the attitude angle, N number of random particles are regenerated, according to this N number of new random particles, re-execute step
Rapid S112~step S114.
S12: the starting track points of the robot, the attitude angle at current time and currently prediction track coordinate step: are obtained
Speed, to predict the robot in the track coordinate of subsequent time.
Step S11 and step S12 are repeated, to predict the track (driving trace) of robot.
Preferably, the artificial sea, land and air three of the machine are dwelt robot, further include modal idenlification step before step S11: identification
The current driving mode of the robot, which includes three types: first mode, second mode and third mode；It should
First mode is sea, land and empty three mode；The second mode is sea, land and sky, Hai Lu, empty sea, empty six mode in land；This
Three mode are sea, land and sky, Hai Lu, empty sea, land sky, land-sea, empty nine land, air-sea mode, and the mode of robot can pass through sensing
Location information, environmental information and the speed of service that device acquires robot in real time obtain.
Step parameter St, the St according to the current driving mode of the robot, in the weight formula of set-up procedure S113
Value is using brainstorming optimization algorithm for the fractional time sequence optimisation of the 1/k of mode transition total duration, k=4.Total duration exists
It between 0.1 second to 10 seconds, is set according to travel speed, default is 1 second.
In addition, in mode transient process, the wing of rolling all around tarnsition velocity and pitch angle the corner speed of calculating robot
Relationship between degree, azimuth tarnsition velocity and roll angle tarnsition velocity, it is ensured that the corner of attitude angle is more than certain uncoordinated
Threshold values just start AutoLock feature, avoid hypertonia injury motor or gear.
Preferably, after step s 12, further include track optimization step: the machine is evaluated using plural score variance function
The velocity variations of device people, the plural number score variance function calculation formula are as follows:M is score, such as m=
1.5, n be moment number, ξiFor each moment velocity amplitude,For speed average, ξiFor plural number, imaginary number indicates cross running speed, real
Number indicates longitudinal driving speed.Benefit using the score variance function evaluation velocity variations of fractional order is: avoiding using biography
Acceleration, deceleration degree cannot be distinguished when evaluating velocity variations in square difference function of system.
The track optimization mode of robot can according to actual needs, be set, following three kinds of modes are specifically included:
(1) simple track optimization mode: reducing frequent left and right turn or brake is alternately acted with throttle, realizes complete
The smooth safe mode transition of journey drives function.
(2) the track optimization mode of medium complexity: the travel speed for adjusting the robot meets minimum evaluation function, this is most
Small evaluation function is defined as: so that the mould of plural score variance function is minimum.The acceleration of robot is set and is slowed down using phase
Same particle filter parameter, the particle filter parameter correspond to the parameter of the formula W (i) in above-mentioned steps S113.
(3) optimal trajectory optimal way: the travel speed for adjusting the robot meets minimum evaluation function, minimum evaluation
Function is defined as: so that the mould of plural score variance function is minimum.The acceleration of robot is set and is slowed down and uses different grains
Sub- filtering parameter.
(1) kind mode is mainly used in civilian inexpensive situation, and (2), (3) mode are mainly used in military high-performance
Situation.
After track optimization step, further comprise the steps of: when the traveled distance of the robot is more than scheduled error, weight
It is new to execute step S11, step S12 and track optimization step.
After track optimization step, step S11 and step can not be executed by further comprising the steps of: to break down in the robot
When S12, cloud database or local initialized data base are called, to obtain safe track data.
Referring to Fig. 4, Fig. 4 is the track emulation schematic diagram of an embodiment of the track setting method of robot of the present invention.
As shown in figure 4, lines 1 indicate that true GPS driving locus coordinate value, dashed lines 2 indicate to use the prediction of track optimization step
Trajectory coordinates value, dashed lines 3 indicate the prediction locus coordinate value without track optimization step.Figure 4, it is seen that dotted line
Lines 2 are compared to dashed lines 3 closer to actual coordinate value, and the track lines after optimizing are more smooth.
In the track setting method of the present embodiment, by using the posture of improved particle filter algorithm prediction robot
Angle, to predict the robot in the track coordinate of subsequent time.The improved particle filter algorithm includes following improvement:
One, using the step parameter in the weight formula of brainstorming optimization algorithm optimized particle filter algorithm, so that in advance
Survey track is more accurate, can adjust the driving direction and traveling speed of robot in time to facilitate robot in the process of moving
Degree；
Two, the machine artificial three is dwelt robot, in the case where robot is in different operation modes, adjusts particle filter algorithm
Weight formula in step parameter, to meet robot in the travel speed and angle demand of different operation modes；
Three, there is the particle filter algorithm left and right to have other resampling mechanism, can prevent after being repeatedly iterated,
There is one direction degradation phenomena in particle；
Four, using the velocity variations of plural score variance function evaluation robot, the driving trace of robot can be made
It is smooth, reduce robot during mode transition due to the angular deviation of traveling cause greatly very much to travel it is unstable, to subtract
Accident in few driving process, guarantees driving safety.
Embodiment two:
Referring to Fig. 5, Fig. 5 is the structural schematic diagram of an embodiment of the track setting system of robot of the present invention.Such as Fig. 5
Shown, the hardware system of track setting system includes processor 51, sensor 52, motor 53, shift device 54, communication equipment
55 and memory 56.Processor 51 is connect with sensor 52, motor 53, communication equipment 55 and memory 56 respectively.
Sensor 52 is used to acquire the location information of the robot, and sensor 52 includes laser radar and GPS sensor.
Motor 53 is for driving robot to travel.
The signal that shift device 54 is used to receive motor 53 changes the travel speed of robot.
Communication equipment 55, for being communicated with cloud.Wherein, which is 5G communication equipment.Optionally, it selects
The 5G chip of Huawei Hai Si.
Memory 56, for storing the aeronautical data of the robot.Memory 56 is similar to the black box of aircraft.
Processor 51 is responsible for whole-sample probability calculation and control for executing track setting method described in embodiment one
System, the control of motor movement signal, the default enabled control of data parameters, resampling signal handle the realization with modal idenlification.
It in other embodiments, can not also include shift device 54 and/or communication equipment 55 and/or memory 56.It can
Selection of land, the hardware system further include SLAM azimuth synchro device.
Embodiment three:
The present invention also provides a kind of robot, which there is the track as described in embodiment two system is arranged.
It is to be illustrated to preferable implementation of the invention, but the invention is not limited to the implementation above
Example, those skilled in the art can also make various equivalent variations on the premise of without prejudice to spirit of the invention or replace
It changes, these equivalent deformations or replacement are all included in the scope defined by the claims of the present application.
Claims (10)
1. a kind of track setting method of robot characterized by comprising
Predict attitude angle step: using the attitude angle of particle filter algorithm prediction robot, the particle filter algorithm uses head
Brain storm optimization algorithm optimizes the step parameter of the weight formula of the particle filter algorithm, the attitude angle include yaw angle,
Pitch angle and roll angle；
Prediction track coordinate step: obtaining the starting track points, the attitude angle at current time and present speed of the robot, with
Predict the robot in the track coordinate of subsequent time.
2. track setting method according to claim 1, which is characterized in that the prediction attitude angle step specifically includes:
Multiple random samples, the multiple random sample quilt are generated according to the priori conditions probability of the attitude angle in t moment
Referred to as particle；
The particle is divided into Left's particle and rightist grain by the left-right position according to the particle relative to previous actual path
Son；
Left's particle of the t moment and the weight of the rightist particle are calculated separately using posterior probability calculation formula,
Wherein, the posterior probability calculation formula are as follows:π is pi, and w (i) is i-th
Weight of the son in the prediction operation of last all particles linear superposition, R is the standard error value of laser radar, and Di is i-th
The sub deviation value with the course line of laser radar observation, St is the step parameter of t moment, and the St value is excellent using brainstorming
Change algorithm to optimize；
According to the weight of Left's particle and the rightist particle, the robot is calculated using Weighted Average Algorithm and is existed
The attitude angle at t+1 moment.
3. track setting method according to claim 2, which is characterized in that the artificial sea, land and air three of machine are dwelt machine
People, the prediction attitude angle step further include before modal idenlification step:
Identify the current driving mode of the robot, the traveling mode includes three types: first mode, second mode and
Third mode；The first mode is sea, land and empty three mode；The second mode is sea, land and sky, Hai Lu, empty sea, land
Empty six mode；The third mode is sea, land and sky, Hai Lu, empty sea, land sky, land-sea, empty nine land, air-sea mode；
The prediction attitude angle step includes: to adjust the step parameter of the weight formula according to the preceding traveling mode
St, fractional time sequence of the St value using brainstorming optimization algorithm for the 1/k of mode transition total duration optimize,
K=4.
4. track setting method according to claim 2 or 3, which is characterized in that the prediction attitude angle step further include:
Multiple random particles are regenerated, according to multiple according to the priori conditions probability of the attitude angle in next t moment
New particle recalculates the robot in the attitude angle at next t+1 moment.
5. track setting method according to claim 1 or 2, which is characterized in that after the prediction track coordinate step
Further include track optimization step:
The velocity variations of the robot, the plural number score variance function calculation formula are evaluated using plural score variance function
Are as follows:M is score, and n is moment number, ξiFor each moment velocity amplitude,For speed average, ξiIt is multiple
Number, imaginary number indicate cross running speed, real number representation longitudinal driving speed；
The travel speed for adjusting the robot meets minimum evaluation function, the minimum evaluation function is defined as: so that institute
The mould for stating plural score variance function is minimum.
6. track setting method according to claim 5, which is characterized in that further include step after the track optimization step
It is rapid: when the traveled distance of the robot is more than scheduled error, to re-execute the prediction attitude angle step, the prediction
Track coordinate step and the track optimization step.
7. track setting method according to claim 5, which is characterized in that further include step after the track optimization step
It is rapid:
When the robot, which breaks down, can not execute the prediction attitude angle step and the prediction track coordinate step, adjust
With cloud database or local initialized data base, to obtain safe track data.
8. system is arranged in the track of robot a kind of characterized by comprising motor, sensor and processor, the motor and
The sensor is connected to the processor respectively；
The motor, for driving the robot to travel；
The sensor, including GPS sensor and laser radar, for obtaining the location information of the robot and sending institute to
State processor；
The processor, for executing track setting method as claimed in any one of claims 1 to 7.
9. system is arranged in track according to claim 8, which is characterized in that the track setting system further includes selector
Part, communication equipment and memory；
The shift device, the signal for receiving motor change the travel speed of the robot；
The communication equipment is connected to the processor, for being communicated with cloud；
The memory is connected to the processor, for storing the aeronautical data of the robot.
10. a kind of robot, which is characterized in that system is arranged including track as claimed in claim 8 or 9.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111121784A (en) * | 2019-12-24 | 2020-05-08 | 中国航空工业集团公司西安飞机设计研究所 | Unmanned reconnaissance aircraft route planning method |
| CN112904884A (en) * | 2021-01-28 | 2021-06-04 | 歌尔股份有限公司 | Method and device for tracking trajectory of foot type robot and readable storage medium |
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