CN101887271A - Mobile robot path planning method - Google Patents
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- CN101887271A CN101887271A CN 201010230039 CN201010230039A CN101887271A CN 101887271 A CN101887271 A CN 101887271A CN 201010230039 CN201010230039 CN 201010230039 CN 201010230039 A CN201010230039 A CN 201010230039A CN 101887271 A CN101887271 A CN 101887271A
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Abstract
The invention discloses a mobile robot path planning method, which comprises the following steps of: A, determining a moving destination of a robot, and setting the number of sensors of the robot and the number of directions towards which the robot can move; B, detecting the environmental information of the surrounding by using the robot; C, defining a mapping relationship between the path planning method and an artificial immune network; and D, resolving the maximum concentration of the artificial immune network and determining an antibody corresponding to the maximum concentration as the moving direction of the robot. The mobile robot path planning method of the invention has the advantage of reaching a destination point under a complex obstacle environment with the local minimization problem, and is feasible and effective under the complex obstacle environment. Simulation results obtained under the U-shaped obstacle environment further show the high efficiency of planning results obtained by the method of the invention under the complex environment.
Description
Technical field
The present invention relates to mobile robot technology, especially a kind of mobile robot's paths planning method.
Background technology
Mobile robot technology mainly comprises navigation and location technology, sensor technology and information fusion thereof, track following technology, path planning technology, the airmanship based on Geographic Information System, the cooperation of many bodies and interaction technique, bionics technology etc.Wherein, the path planning technology is an indispensable important component part in the Mobile Robotics Navigation technology, and it requires robot independently to determine the path according to the instruction and the environmental information that give, and avoiding obstacles is realized task object.Path planning is the safety guarantee that the mobile robot finishes the work, and also is simultaneously the important symbol of the intelligent degree of mobile robot.Especially under the situation that the precision of robot hardware system can not be resolved in a short time, research to paths planning method seems particularly important, this will fundamentally change mobile robot's navigation performance, mobile robot's intellectual level will be improved, reduce the nondeterministic statement that the mobile robot exists in moving process, improve speed and dirigibility that the mobile robot moves, for remote transfer robot, sniffing robot, the service robot of developing high intelligence are laid a solid foundation.Simultaneously, the path planning Study on Technology has also produced very big impetus to correlation techniques such as automatic control, machine learning, pattern-recognitions.
Mobile robot path planning is robotics important research field still not, also is the important binding site of artificial intelligence and robotics.Path planning is divided into global path planning problem and local path planning problem, and so-called global path planning refers to robot and grasps whole environmental informations in advance, finds a global minimum just to find optimal path.The local paths planning problem is then more complicated, it is a kind of more near the path planning problem of realistic situation, refer to robot and only can know local environmental information, can only plan while moving, usually entering partial closure's environment causes carrying out path planning, for example be absorbed in the U-shaped zone, this shows on the paths planning method to be exactly the local minimum problem.
The document of associated machine people path planning is increasing in recent years, studies the preliminary phase ratio with the eighties in 20th century, and being has all had huge development on the degree of depth of studying or range, has begun to take shape the multi-faceted research of theory, algorithm and application.The application of intelligent algorithms such as fuzzy logic, neural network, genetic algorithm has solved the insurmountable path planning problem of parts of traditional method.But the application of intelligent algorithm in the robot path planning at present also has been subjected to great limitation, be confined to the modeling and the cognition of environment as neural network, when fuzzy logic is applied in the complicated unknown dynamic environment, fuzzy rule is difficult to be extracted etc., and also not having especially at present can the perfect paths planning method that solves the local minimum problem.
Summary of the invention
The objective of the invention is to avoid weak point of the prior art and a kind of mobile robot's based on artificial immune network paths planning method is provided, it can solve the local minimum problem of mobile robot's path planning under complex environment.
The objective of the invention is to be achieved through the following technical solutions.
A kind of mobile robot's paths planning method, step comprises:
A. determine the target of robot movement; But set the number of sensors of robot and the working direction number of robot;
B. the environmental information around robot detects;
But C. define the mapping relations of described paths planning method and artificial immune network according to the working direction number of the number of sensors of described target, environmental information, robot and robot; Described environmental information is defined as antigen, but the working direction of described robot is defined as antibody, but the controlling mechanism of finding the solution the working direction of described robot is defined as concentration equation, be defined as stimulation but the working direction of described robot is consistent with the direction of described target, but the deviation in driction of the working direction of described robot and described target is defined as inhibition;
D. find the solution the Cmax of described artificial immune network according to the concentration equation of described artificial immune network, the antibody of described Cmax correspondence is the working direction of robot;
When E. the angle of the direction of the working direction of described robot and described target was less than 90 °, robot took a step forward along the working direction of described robot, after taking a step forward, when robot does not satisfy end condition, carried out step B;
The angle of the working direction of described robot and the direction of described target is during more than or equal to 90 °, set virtual target at random, direction with described virtual target replaces the direction of described target to carry out step B, C and D, find the solution the working direction of robot, robot takes a step forward along the working direction of described robot, after taking a step forward, when robot does not satisfy end condition, carry out step B.
Preferably, described environmental information comprises the deflection of described target, the deflection of sensor, and three data of distance between the barrier of sensor and sensor place direction, and these three data are formed an antigen.
Another is preferred, when robot is provided with
MDuring individual sensor,
jThe deflection of individual sensor
,
Another is preferred, but when the working direction of described robot is
NWhen individual, the
iBut individual working direction
,
Mobile robot's of the present invention paths planning method has used the artificial immune network model that is used for robot behavior control, set up the mapping relations between artificial immune network model and the robot path planning method, laid a good foundation for artificial immune network is used for robot path planning's problem.Mobile robot's of the present invention paths planning method adopts the artificial immune network model, introduced artificial immune network model randomness factor, when running into the local minimum problem, can walk out the blind alley by factor at random, can arrive impact point having under the complex barrier substance environment of local minimum problem, method for planning path for mobile robot of the present invention is feasible and effective under the complex barrier substance environment existing, and the simulation result under U type obstacle environment further illustrates the high efficiency of this paper method program results under complex environment.
Description of drawings
The invention will be further described to utilize accompanying drawing, but the content in the accompanying drawing does not constitute any limitation of the invention.
Fig. 1 is the model synoptic diagram of mobile robot, target and the barrier of one embodiment of the present of invention.
Fig. 2 is that one embodiment of the present of invention are at the simulation result synoptic diagram that has under the complex barrier substance environment of local minimum problem.
Fig. 3 is that the Artificial Potential Field paths planning method of prior art is at the simulation result synoptic diagram that has under the complex barrier substance environment of local minimum problem.
Fig. 4 is the simulation result synoptic diagram of one embodiment of the present of invention under the U-shaped obstacle environment.
Fig. 5 is the simulation result synoptic diagram of Artificial Potential Field paths planning method under the U-shaped obstacle environment of prior art.
Reference numeral:
Target 10, barrier 11, robot 13, sensor 14, the positive dirction 15 of robot.
Embodiment
With the following Examples the present invention is further described.
With reference to embodiment of paths planning method of the specification of a model mobile robot of the present invention of Fig. 1, this method comprises following step.
A. determine the target 10 that robot 13 moves; Set the number of the sensor 14 of robot 13
MBut working direction number with robot 13
N
But the working direction of robot 13 is the rotatable angle settings according to the topworks of robot 13, and the precision of topworks is high more, but the number of working direction is many more.For predetermined topworks, but the working direction number of robot 13
NFix.When but the working direction of described robot 13 is
NWhen individual, the
iBut the deflection of individual working direction
,
,
B. the environmental information around robot 13 detects
Ag j , measuring ability by
M Individual sensor 14 is finished, the environmental information of acquisition
Ag j The angle (being the deflection of target 10) that comprises the positive dirction 15 of target 10 and robot, the angle of the direction of each sensor 14 and the positive dirction of robot 15 (deflection of sensor 14), and the distance of the barrier 11 of each sensor 14 and sensor 14 place directions.For example among Fig. 1, the deflection of target 10 is
θ g , sensor
S j Deflection be
, at sensor
S j Direction on, sensor
S j With the distance of barrier 11 be
d j
C. according to described target 10, environmental information
Ag j , but the working direction number of sensor 14 numbers of robot 13 and robot 13 defines the mapping relations of described paths planning method and artificial immune network; Described environmental information
Ag j Be defined as antigen, but the working direction of described robot 13 is defined as antibody, but the controlling mechanism of finding the solution the working direction of described robot 13 is defined as concentration equation, be defined as stimulation but the direction of the working direction of described robot 13 and described target 10 is consistent, but the deviation in driction of the working direction of described robot 13 and described target 10 is defined as inhibition.
Wherein, described environmental information
Ag j The deflection that comprises described target 10
θ g , sensor 14 deflection
, and the distance of the barrier 11 of each sensor 14 and sensor 14 place directions
d j Three data, these three data are formed an antigen.
D. find the solution the Cmax of described artificial immune network according to the concentration equation of described artificial immune network, the antibody of described Cmax correspondence is the working direction of robot 13.After the working direction of robot 13 was determined, robot 13 rotations self forwarded the positive dirction 15 of robot the working direction of robot 13 to, and the positive dirction 15 along robot moves then.Therefore, the working direction of each robot 13 is the positive dirction 15 of robot.
The angle of the direction of the working direction of E. described robot 13 and described target 10 is during less than 90 °, and robot 13 takes a step forward along the working direction of described robot 13, after taking a step forward, when robot 13 does not satisfy end condition, carries out step B;
The angle of the direction of the working direction of described robot 13 and described target 10 is during more than or equal to 90 °, set virtual target at random, direction with described virtual target replaces the direction of described target 10 to carry out step B, C and D, find the solution the working direction of robot 13, robot 13 takes a step forward along the working direction of described robot 13, after taking a step forward, when robot 13 does not satisfy end condition, carry out step B.
End condition generally refers to distance between robot 13 and the target 10 less than a value in robot path planning method, and for example less than the radius or the diameter of robot 13, this end condition is generally determined according to the sizes of robot 13 itself.
According to Fig. 2 and Fig. 3 contrast, in the path planning with local minimum problem, Fig. 2 adopts the mobile robot 13 of paths planning method of the present invention can pass through complex barrier thing 11, successfully arrives impact point 10.Fig. 3 adopts the mobile robot 13 of prior art Artificial Potential Field paths planning method can't solve the local minimum problem, can not proceed path planning.
According to Fig. 4 and Fig. 5 contrast, in the path planning of the environment with U-shaped barrier, Fig. 2 adopts the mobile robot 13 of paths planning method of the present invention can break away from U-shaped barrier 11 voluntarily, successfully arrives impact point 10.Fig. 5 adopts the mobile robot 13 of prior art Artificial Potential Field paths planning method to solve and proceeds path planning, can not reach impact point 10.
Should be noted that at last; above embodiment only is used to illustrate technical scheme of the present invention but not limiting the scope of the invention; although the present invention has been done detailed description with reference to preferred embodiment; those of ordinary skill in the art is to be understood that; can make amendment or be equal to replacement technical scheme of the present invention, and not break away from the essence and the scope of technical solution of the present invention.
Claims (4)
1. a mobile robot paths planning method is characterized in that, step comprises:
A. determine the target of robot movement; But set the number of sensors of robot and the working direction number of robot;
B. the environmental information around robot detects;
But C. define the mapping relations of described paths planning method and artificial immune network according to the working direction number of the number of sensors of described target, environmental information, robot and robot; Described environmental information is defined as antigen, but the working direction of described robot is defined as antibody, but the controlling mechanism of finding the solution the working direction of described robot is defined as concentration equation, be defined as stimulation but the working direction of described robot is consistent with the direction of described target, but the deviation in driction of the working direction of described robot and described target is defined as inhibition;
D. find the solution the Cmax of described artificial immune network according to the concentration equation of described artificial immune network, the antibody of described Cmax correspondence is the working direction of robot;
When E. the angle of the direction of the working direction of described robot and described target was less than 90 °, robot took a step forward along the working direction of described robot, after taking a step forward, when robot does not satisfy end condition, carried out step B;
The angle of the working direction of described robot and the direction of described target is during more than or equal to 90 °, set virtual target at random, direction with described virtual target replaces the direction of described target to carry out step B, C and D, find the solution the working direction of robot, robot takes a step forward along the working direction of described robot, after taking a step forward, when robot does not satisfy end condition, carry out step B.
2. mobile robot's according to claim 1 paths planning method, it is characterized in that, described environmental information comprises the deflection of described target, the deflection of sensor, and three data of distance between the barrier of sensor and sensor place direction, these three data are formed an antigen.
3. mobile robot's according to claim 1 paths planning method is characterized in that, when robot is provided with
MDuring individual sensor,
jThe deflection of individual sensor
,
4. mobile robot's according to claim 1 paths planning method is characterized in that, but when the working direction of described robot is
NWhen individual, the
iBut individual working direction
,
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| CN102113853A (en) * | 2011-02-28 | 2011-07-06 | 莱克电气股份有限公司 | Method for cleaning intelligent dust collector |
| CN102169347A (en) * | 2011-03-08 | 2011-08-31 | 浙江工业大学 | Multi-robot path planning system based on cooperative co-evolution and multi-population genetic algorithm |
| CN103085070A (en) * | 2013-01-15 | 2013-05-08 | 上海交通大学 | Quadruped robot motion planning method for facing complex terrain |
| CN103412490A (en) * | 2013-08-14 | 2013-11-27 | 山东大学 | Polyclone artificial immunity network algorithm for multirobot dynamic path planning |
| CN104407616A (en) * | 2014-12-03 | 2015-03-11 | 沈阳工业大学 | Dynamic path planning method for mobile robot based on immune network algorithm |
| CN104516350A (en) * | 2013-09-26 | 2015-04-15 | 沈阳工业大学 | Mobile robot path planning method in complex environment |
| CN105607041A (en) * | 2015-09-22 | 2016-05-25 | 吉林大学 | Pulse positioning model based on bionic sand scorpion positioning function |
| CN106477125A (en) * | 2016-09-27 | 2017-03-08 | 杭州南江机器人股份有限公司 | A kind of automatic labeling device and labeling method |
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| CN107169688A (en) * | 2016-03-07 | 2017-09-15 | 中国石油化工股份有限公司 | A kind of goods buying and spelling ship/car transportation resources based on Artificial Immune Algorithm |
| CN107362525A (en) * | 2016-05-13 | 2017-11-21 | 环球娱乐株式会社 | Tackle device, game machine and dealer's replacement device |
| CN109421056A (en) * | 2017-08-25 | 2019-03-05 | 科沃斯机器人股份有限公司 | Self-movement robot |
| CN110362085A (en) * | 2019-07-22 | 2019-10-22 | 合肥小步智能科技有限公司 | A kind of class brain platform for extraordinary crusing robot |
| WO2020073681A1 (en) * | 2018-10-10 | 2020-04-16 | Midea Group Co., Ltd. | Method and system for providing remote robotic control |
| CN111337931A (en) * | 2020-03-19 | 2020-06-26 | 哈尔滨工程大学 | AUV target searching method |
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| CN111857142A (en) * | 2020-07-17 | 2020-10-30 | 广州大学 | Path planning obstacle avoidance auxiliary method based on reinforcement learning |
| CN113406957A (en) * | 2021-05-19 | 2021-09-17 | 成都理工大学 | Mobile robot autonomous navigation method based on immune deep reinforcement learning |
| US11137764B2 (en) | 2019-04-15 | 2021-10-05 | International Business Machines Corporation | Recharging for autonomous in-pipe vehicles |
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| CN102113853B (en) * | 2011-02-28 | 2013-01-02 | 莱克电气股份有限公司 | Method for cleaning intelligent dust collector |
| CN102113853A (en) * | 2011-02-28 | 2011-07-06 | 莱克电气股份有限公司 | Method for cleaning intelligent dust collector |
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| CN103412490A (en) * | 2013-08-14 | 2013-11-27 | 山东大学 | Polyclone artificial immunity network algorithm for multirobot dynamic path planning |
| CN103412490B (en) * | 2013-08-14 | 2015-09-16 | 山东大学 | For the polyclone Algorithm of Artificial Immune Network of multirobot active path planning |
| CN104516350B (en) * | 2013-09-26 | 2017-03-22 | 沈阳工业大学 | Mobile robot path planning method in complex environment |
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| CN104407616A (en) * | 2014-12-03 | 2015-03-11 | 沈阳工业大学 | Dynamic path planning method for mobile robot based on immune network algorithm |
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| CN105607041A (en) * | 2015-09-22 | 2016-05-25 | 吉林大学 | Pulse positioning model based on bionic sand scorpion positioning function |
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| CN109421056A (en) * | 2017-08-25 | 2019-03-05 | 科沃斯机器人股份有限公司 | Self-movement robot |
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| CN111784748B (en) * | 2020-06-30 | 2023-05-02 | 深圳市道通智能航空技术股份有限公司 | Target tracking method and device, electronic equipment and mobile carrier |
| CN111857142A (en) * | 2020-07-17 | 2020-10-30 | 广州大学 | Path planning obstacle avoidance auxiliary method based on reinforcement learning |
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Application publication date: 20101117 |