CN105867365A - Path programming and navigation system based on improved artificial potential field method and method thereof - Google Patents
Path programming and navigation system based on improved artificial potential field method and method thereof Download PDFInfo
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- 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
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
Abstract
The invention discloses a coal mine rescue robot path programming and navigation system based on an improved artificial potential field and a method thereof. An obstacle filling method and a disturbance potential field method are combined. In the obstacle filling method, virtual filling is performed on a concave obstacle, a potential field function is constructed and filled and a corresponding repulsive potential field function is generated. In the disturbance potential field method, through adding a disturbance potential field in a gravitational potential field, a gravitational potential field function is changed and a rescue robot can autonomously walk out of a local minimum point when falling into the local minimum point. The two methods are combined, which can well help the underground rescue robot escape from the local minimum point; and the path is reprogrammed, and the robot can be avoided from falling into other local minimum points again and can successfully complete a disaster rescue task; an algorithm is concise and high reliability is possessed.
Description
Technical field
The present invention relates to a kind of robot path planning's navigation system and method, specifically apply Mine Disaster Relief Robot
, use the paths planning method improving Artificial Potential Field multi-technical fusion.
Background technology
When underground coal mine mine disaster occurs, use robot to carry out detecting and rescue is reliable and effective approach, for down-hole
The key issue of robot, as power supply, explosion-proof, robot stride mechanical flexibility etc. are studied, long-term and unremitting by scientific research personnel
Effort, achieved with significant progress.Underground coal mine environment is special, and tunnel intersects, and narrow space is closed, robot path
The planning difficult problem that always restriction robot applies at underground coal mine, Disaster Relief Robot moves robot as one, its path
The level of planning determines the efficiency of the disaster relief to a certain extent.The task of mine Disaster Relief Robot is, necessarily having dynamically and
In static circumstances not known, finds one optimal path not having to collide, the optimization of relevant parameter to be met, such as,
Short path, the shortest time, lowest energy consumption etc..Traditional Artificial Potential Field is the virtual cattle of the one put forward for 1986 by Khabit
The method of gravitation, is initially to solve robot capturing object when, and its arm can not touch workbench, later
Apply in robot motion's avoidance.Its main thought is to be simplified to a little by robot, impact point and barrier, and impact point is to machine
Device people produces gravitation, and barrier produces repulsion to robot, in whole environment, controls robot by the effect made a concerted effort and transports
Dynamic, carry out path planning.Artificial Potential Field Method is more ripe in legacy paths planing method and is concisely and efficiently algorithm, and it is main
Thinking is, by impact point, robot is produced gravitation, and barrier produces phase interaction between repulsion, and robot to robot
Firmly, form an intelligent Artificial Potential Field, robot is carried out real-time path and plans.But traditional Artificial Potential Field is deposited
In the problem of local minimum point, when barrier is near impact point, there is also robot and shake around impact point, cause
The problem that robot cannot arrive impact point.
At present, according to local minimum point's problem of Artificial Potential Field, conventional solution has: barrier completion method, detection
Method, robot acceleration is with reference to method, and distance parameter is with reference to method, equipotential field lines method, Follow wall etc..
Barrier completion method: by spill barrier is carried out virtual filling, produces new repulsion potential field function, it is to avoid
Robot is absorbed in the local minimum point of spill barrier again, and shortcoming is the shape being difficult to timely disturbance in judgement thing, often sentences
Disconnected error causes avoidance failure.
Probe technique: by the common barrier model producing local minimum point of robot input, utilizing robot to carry
Detection device, detection front whether exist may produce local minimum point barrier, carry out path planning avoidance in advance；Lack
Point is to be limited in scope due to the detection of robot, encounters the large obstacle of underground coal mine, often cannot visit in advance
Measure complete barrier, ultimately result in avoidance failure.
Robot acceleration is with reference to method: robot acceleration is the weight whether reflection robot will enter local minimum point
Want parameter, the acceleration parameter of robot is joined in repulsion function, regulated at any time by robot acceleration magnitude and scold
Force function, it is to avoid robot is absorbed in local minimum point, thus completes avoidance；Shortcoming is that method comparison is complicated, often because adding
Speed processes target location by mistake as local minimum point.
Distance parameter is with reference to method: by being incorporated in repulsion function by the range information of robot Yu target location, pass through
Both range information regulation repulsion functions, it is to avoid robot is absorbed in local minimum point, thus completes avoidance；Shortcoming is intended to moment prison
Surveying the position of robot and impact point, the complexity of algorithm increases, and algorithm operational efficiency is low, poor robustness.
Equipotential field lines method: before robot carries out path planning, carries out model construction to known environmental information, passes through
Equipotential field lines, determines the position that in whole environment, potential field is minimum, including local minimum point and target location, it is to avoid robot falls into
Enter local minimum point, thus complete avoidance；Shortcoming be due to will to environmental information it is known that and want structural environment model, uncomfortable
In underground coal mine environment unknown after being used in mine disaster.
Follow wall: when robot arrives local minimum point when, select a suitable direction, allow robot
Local minimum point is jumped out until robot in edge along barrier, thus completes avoidance；Shortcoming is that algorithm complexity is high, calculates
Method memory usage is high, and efficiency is low.
Visible existing airmanship is applied in underground coal mine unstructured moving grids also to be needed to improve, and therefore, research is suitable for
Navigation system and the method for underground coal mine Disaster Relief Robot have great importance.
Summary of the invention
In order to solve underground coal mine Disaster Relief Robot local minimum point's problem in Artificial Potential Field Method, the invention provides one
Plant based on filling barrier and the underground coal mine Disaster Relief Robot Path Planning Technique of disturbance potential field fusion.The present invention is with conventional
Robot path planning's technology is compared, and have employed original filling barrier method, in conjunction with disturbance potential field method, improves disaster relief machine
People flees from the performance of local minimum point in spill barrier, and this algorithm can effectively help robot to flee from local minimum point,
And avoid robot to be absorbed in local minimum point continuously, improve robustness and the accuracy of algorithm, reduce the complexity of algorithm.
The technical solution used in the present invention is as follows:
The invention provides a kind of paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field, should
Planing method is original barrier completion method and disturbance potential field to be combined, and helps robot to flee from local minimum point, again
Planning avoidance path, it is to avoid be again absorbed in local minimum point；
Described local minimum point refer to robot under the paths planning method of Artificial Potential Field Method, due to gravitation potential field and scolding
The common effect of power potential field, in the position not arriving target, makes a concerted effort to be zero, causing being absorbed in certain region cannot due to suffered potential field
Continue path planning；
Described barrier completion method is the detection device by Disaster Relief Robot, and spill barrier is carried out virtual filling,
It is made to ultimately form the barrier of more rule, it is to avoid to produce local minimum point；
Described disturbance potential field method is when robot is absorbed in local minimum point, by introducing perturbation potential in total potential field function
Field function, changes total potential field function, helps underground rescue robot to walk out local minimum point.
According to paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field of the present invention, institute
State paths planning method to be combined with the new disturbance potential field method proposed by known barrier completion method, when robot is absorbed in local
During minimal point, first pass through increase perturbation potential field function and help Disaster Relief Robot to walk out local minimum point, now carry out spill barrier
Hindering thing to be filled, structure fills potential field function, progressively walks out spill barrier along with robot, and filling area is gradually increased, now
Repulsion potential field function is also gradually increased, and total potential field function changes, and Disaster Relief Robot path is planned again, until spill obstacle
Thing is completely filled, and robot gets around spill barrier, eventually arrives at target location.
According to paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field of the present invention, institute
Stating perturbation potential field function and robot and there is proportional relationship to the air line distance of impact point, perturbation potential field function is only in the disaster relief
Just starting when robot is absorbed in local minimum point and add total potential field function, other, robot was only according to the gravitation letter of impact point in moment
The repulsion function of number and barrier carries out path planning.
According to paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field of the present invention, institute
Stating barrier and will produce new repulsion potential field function, filling scope incrementally increases away from local minimum point with Disaster Relief Robot, fills out
Fill the inversely proportional relation of distance of potential field repulsion function and robot to local minimum point.
According to paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field of the present invention, institute
Stating paths planning method when Disaster Relief Robot runs into recessed barrier at underground coal mine, specific works step is as follows:
Step one: known underground coal mine global information is input in path planning system；
Step 2: detecting obstacles thing information, merges known underground coal mine global information, builds barrier repulsion potential field mould
Type and impact point gravitational potential field model, carry out preliminary planning to path；
Step 3: disaster relief task starts, Disaster Relief Robot starts to run to impact point, until being absorbed in spill barrier local
Minimal point；Now making a concerted effort suffered by robot is zero, arrives the potential field minimum point in whole path planning；
Step 4: start to flee from local minimum point, starts perturbation potential field function, and now total potential field function changes, robot
Concussion occurs, temporarily flees from local minimum point；
Step 5: be again absorbed in other local minimum points of spill barrier in order to avoid Disaster Relief Robot, proceed by
Spill barrier is filled, and filling scope is fled from local minimum point with Disaster Relief Robot and progressively expanded；
Step 6: after waiting a scan period, it is judged that robot has fled from local minimum point the most, if be still absorbed in
Local minimum point, returns to step 2 class model each to present stage and re-establishes, re-start path planning；If fleeing from office
Portion's minimal point, proceeds disaster relief task according to existing potential field model；
Step 7: robot arrives target location, and path planning terminates.
According to paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field of the present invention, institute
In the step 2 stated, barrier repulsion potential field model is:
Fatt(X)=-gradUatt(X)=-Katt|X-Xg| (1)
Impact point gravitational potential field function model is:
According to paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field of the present invention, institute
In the step 4 stated, perturbation potential field function model is:
According to paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field of the present invention, institute
Stating in step 5, filling barrier repulsion potential field model is:
Compared with prior art, it is an advantage of the current invention that:
(1) the underground coal mine Disaster Relief Robot that the present invention uses completion method and disturbance potential field method to blend carries out path rule
Draw, take full advantage of the completion method change to spill barrier repulsion potential field model, help disaster relief machine in conjunction with disturbance potential field method
People flees from local minimum point, and avoids it to be again absorbed in other local minimum points.
(2) present invention employs the mixing Explosion-proof Design of malleation intrinsic safety type, mix the light weight of Explosion-proof Design, both
Ensure that the safety of navigation system, make again robot have good exercise performance.
(3) present invention employs improvement completion method, solve bring owing to the investigative range of robot own is limited to recessed
The detection failure of shape barrier, thus avoid and be absorbed in local minimum point and cannot escape.
(4) present invention is when using disturbance potential field method, it is proposed that new perturbation potential field function Kdisρ2(X, Xg), by robot
Import to, in potential field function, improve the motility of disturbing function with impact point range information.
Accompanying drawing explanation
The position view of Tu1Shi local minimum point；
Fig. 2 is the range of choice schematic diagram of specific item punctuate；
Fig. 3 a～Fig. 3 c is local minimum point's schematic diagram of spill barrier；
Fig. 4 a and Fig. 4 b is robot probe's scope schematic diagram；
Fig. 5 is to fill barrier schematic diagram；
Fig. 6 a～Fig. 6 c is that algorithm running orbit emulates schematic diagram；
Fig. 7 is that simulation algorithm runs time contrast schematic diagram；
Fig. 8 is that simulation algorithm memory usage contrasts schematic diagram；
Fig. 9 is algorithm flow chart.
Detailed description of the invention
In Traditional Man potential field function, total potential field of environmental model is as follows:
Utotal=Uatt+Urep (5)
When robot is in path planning, being absorbed in local minimum point, using increases disturbance potential field Lai Shi robot and flees from office
Portion's minimal point, is defined as follows disturbance potential field:
Wherein, KdisIt it is disturbance potential field constant；ρ (X, Xg) it is the distance of robot distance objective point；ρaFor robot whether
Arrive the judge distance of impact point.
After introducing disturbance potential field, when robot is absorbed in local minimum point, total potential field of environmental model is as follows:
Utotal=Uatt+Urep+Udis (7)
If Disaster Relief Robot is absorbed in local minimum point, potential field adds disturbance potential field, help Disaster Relief Robot to flee from
Local minimum point, based on above model, when Disaster Relief Robot carries out avoidance, in each sampling period, detects disaster relief machine
The scope that people can arrive, searches out the potential field minimum point in the range of whole spy, as specific item punctuate, uses vector method
Power being synthesized, it is judged that whether robot is in local minimum point, if being in local minimum point, potential field intensity sum comprises
Disturbance potential field.Assuming that the maximal rate that robot moves is Vmax, the sampling period is t0, then the accessibility scope of robot is exactly
With current location as the center of circle, with Vmaxt0For the circle of radius, in order to ensure the stationarity of robot motion and in motor process
Reliability, control robot objective speed point in motor process be selected in radius annular region.Sub-goal point selection
Also there is relation with robot in acceleration direction, and be static when robot is in local minimum point, disturb owing to adding
Kinetic potential field, local minimum point is walked out in machine talent conference, so the acceleration direction of robot is identical with the direction of perturbation potential field force,
So the selected extended line in acceleration direction is specific item punctuate with the junction of annular region, as in figure 2 it is shown, wherein robot moves
Dynamic angle, θ ∈ (0,2 π), dash area is the region of sub-goal point selection.
The present invention proposes a kind of when Disaster Relief Robot runs into recessed barrier at underground coal mine, flees from local minimum point
Method, is contained in underground rescue Algorithms of Robots Navigation System in advance by coal mine down-hole tunnel geography information, and robot is come by tunnel itself
Saying not to be obstacle, the Mobile life-saving capsule of down-hole, mine car, plant equipment etc. are all the obstacle needing to avoid for robot.
As it is shown on figure 3, illustrate two kinds of recessed barriers in the underworkings of ore deposit, when similar barrier is encountered by robot, it is difficult to
Differentiate whether front exists local minimum point, even if so dyspraxia thing completion method, also may not necessarily escape out local pole in time
Point.
As shown in figures 4 a and 4b, Disaster Relief Robot is roadway moving under mine, before in Fig. 4 a, Disaster Relief Robot detects
Side is two barriers, now search angle α ＜ 180 °, but the investigative range of Disaster Relief Robot is limited, and fact of case is that both belong to
In same barrier, when position in robot motion to Fig. 4 b, robot has been absorbed in spill barrier, former spy
Angle measurement α ＞ 180 °, robot probe to other barriers, and judge that detect before two barriers belong to same obstacle
Thing, now robot is the most progressively absorbed in local minimum point, ultimately results in goal nonreachable.As shown in Figure 3 c, run into such
Barrier, robot can't be absorbed in local minimum point.But when mine disaster occurs, barrier kind is many, the most only considers one
The situation of kind is not enough, is difficult to practice in the tunnel that mine disaster occurred.
Method and the barrier completion method proposed before of disturbance potential field are combined by the present invention, it is contemplated that underground coal mine is changeable
Barrier situation, main thought is after Disaster Relief Robot is absorbed in local minimum point, by disturbance potential field Lai Shi robot
Flee from local minimum point, and dyspraxia thing fills the barrier producing local minimum point, the barrier after structure filling
Hinder thing repulsion function, while making robot flee from local minimum point, it is to avoid robot is absorbed in local minimum point again, such as Fig. 5
Shown in.
In this case, it is possible to the region producing local minimum point is filled, thus form a bigger barrier of area
Hindering thing, the repulsion potential field in the region being filled is:
Wherein: klocalRepresent and fill region repulsion potential field proportionality coefficient；x-xlocalRepresent when robot exits local minimum point with
The distance of local minimum point；PlFor filling the coverage in region.
Original filling barrier method can well avoid Disaster Relief Robot to be absorbed in local minimum point in advance, but
It will be seen that the investigative range that the premise that the method is used is robot is greater than the length of barrier in simulation result.Such as figure
Shown in 4a, when the scope of robot probe is less than the length of barrier, then cannot judge whether matrix barrier in advance, therefore
In conjunction with the disturbance potential field method being mentioned above, when robot detectable range is visited and do not measured matrix barrier, for avoiding machine
Device people is absorbed in local minimum point, increases disturbance market, helps robot to flee from local minimum point.
When Disaster Relief Robot is absorbed in local minimum point, by increasing disturbance potential field, robot is made to flee from local minimum point,
In the scenario above, the direction of disturbance potential field should be the opposite direction of the original direction of advance of robot, only increases reciprocal
Disturbance potential field Cai Nengshi robot breaks away from local minimum point, otherwise may proceed to move ahead, in fact it could happen that collide barrier or office
The situation of portion's concussion, total potential field that now robot is subject to is shown in formula (9), the scope of sub-goal point selection such as Fig. 2 institute
Showing, if barrier is in a two quadrant, then the position of specific item punctuate should select at three four-quadrants.By increasing disturbance potential field, machine
Device people flees from local minimum point.In order to avoid being again absorbed in local minimum point, carry out the filling of recessed barrier, after filling
Potential field intensity is as follows:
Utotal=Uatt+Urep+Udis+Ulocal (9)
The concave domain of local minimum point may be produced as shown in Figure 4 b, i.e. barrier spill district when robot exits completely
Territory is filled complete.Now, cancel disturbance potential field, potential field that robot is now subject to and be:
Utotal=Uatt+Urep+Ulocal (10)
What the present invention proposed is a kind of method of path planning of underground coal mine Disaster Relief Robot avoidance, for verification algorithm can
Row and optimization property, the barrier situation under mine that have chosen has carried out emulation without loss of generality, and emulation is all at Matlab7.0.4
In the environment of carry out, respectively the space complexity of robot path track and algorithm is emulated.The present invention uses ZY08-
C-G type avoidance dolly, is programmed in conjunction with Visual C++, is tested material object.
The present invention have chosen two kinds and varies in size, the barrier that occupied area is different, and the method that have employed disturbance potential field is entered
Go emulation, as shown in Fig. 6 a, Fig. 6 b and Fig. 6 c, set in Fig. 6 a target location as (0.45,0.2), impact point position in Fig. 6 b
It is set to (0.45,0.05).In Fig. 6 a, after robot is absorbed in local minimum point, potential field function adds disturbance potential field,
Carried out again planning to path, but through twice again planning robot can not escape local minimum point, but again
The secondary other local minimum point that has been absorbed in, finally could not walk out spill barrier；Seeing from Fig. 6 b, robot is equally
Experienced by twice local minimum point, but walk out local minimum point eventually through disturbing the method increasing kinetic potential field, success avoidance is recessed
Shape barrier；When we are it will be clear that robot in Fig. 6 a is absorbed in local minimum point, robot distance objective point
Position is much smaller than the position of robot distance objective point in Fig. 6 b.From the formula (7) definition to disturbance potential field, ρ (X, Xg)
Bigger at Fig. 6 b, the repulsion that perturbation potential field model produces is bigger, it is easier to deviate original track, it is simple to path planning again.
But robot obstacle-avoiding takes more time and distance in Fig. 6 b, add the difficulty of algorithm, the reliability decrease of algorithm.
Fig. 6 c is combined with disturbance potential field method and fills the result after barrier method combines, can be clearly from Fig. 6 c
See, when robot is absorbed in local minimum point, make robot flee from local minimum point by disturbance potential field, owing to being filled with barrier
Hinder thing, as in potential field function, repulsion potential field increases, so that the track of robot is the steepest relative to track before
High and steep, and along with the increase of filling area, robot gradually flees from spill barrier, again quickly arrives target after path planning
Point, path becomes apparent from.
When full attribute runs, the present invention has carried out the contrast of space complexity to two kinds of different algorithms, logical
Cross the contrast to space complexity, be appreciated that the operation time of algorithms of different, algorithm run-time memory occupancy situation etc., this
All carry out comparison algorithm and optimize the important parameter of performance.According to the contrast of algorithm space complexity, several different calculation can be compared
The optimization performance of method, therefrom selects the algorithm of optimum, and the selection to Disaster Relief Robot optimal path has important effect.Emulation knot
Fruit is as follows: along with the increase of simulation sample quantity, compare the operation time of two kinds of algorithms, as shown in Figure 7.Along with sample size
Increase, compare two kinds of algorithm runtime system memory usage situations, as shown in Figure 8.Wherein, 1 represents the imitative of disturbance potential field method
True curve, 2 represent that disturbance potential field combines the simulation curve of completion method.
Interpretation of result: from Riming time of algorithm and Installed System Memory occupancy analysis result, two kinds of algorithms are at emulation sample
During this number 500000, Riming time of algorithm and Installed System Memory occupancy difference in performance are the most little.But along with simulation sample
Quantity increase, disturbance potential field combine completion method the most at runtime or memory usage on, have the biggest advantage.?
On the operation time, when simulation sample number one timing, disturbance potential field combines the algorithm time of completion method than single perturbation potential
Field method is the most a lot；On memory usage, the memory usage of disturbance potential field method approaches 59.5%, and disturbance potential field combines and fills
The memory usage of method approaches 43.5%, and the latter's memory usage is lower.
The present invention uses ZY08-C-G type avoidance dolly to carry out full-scale investigation, carries out on-the-spot mould in conjunction with Visual C++ programming
Intending avoidance, avoidance trolley travelling workflow diagram is as shown in Figure 9.
The full-scale investigation of the present invention uses Visual C++ to program the write of avoidance dolly, and dolly has been carried out actual keeping away
Barrier experiment, the results show, dolly can complete avoidance task well, and eventually arrive at the impact point that we set.
Local minimum point's problem that the present invention exists according to Artificial Potential Field Method in underground coal mine Disaster Relief Robot path planning,
By proposing the concept of disturbance potential field, in conjunction with the method filling barrier of original document, solve underground coal mine disaster relief machine
People is absorbed in local minimum point's problem of spill barrier.When Disaster Relief Robot is absorbed in the local minimum point of down-hole spill barrier
Time, by increasing disturbance potential field in potential field function, mine Disaster Relief Robot can be made to make robot successfully flee from local minimum
Point, but according to simulation result:
(1) Disaster Relief Robot may be absorbed in other local minimum points of same barrier again, now combines original literary composition
Offer the filling barrier method of proposition, form new repulsion potential field function.
(2) when robot is absorbed in local minimum point, first pass through disturbance potential field and help robot to flee from local minimum point, then
Help robot path planning again by filling barrier method, be possible not only to avoid Disaster Relief Robot to be again absorbed in local pole
Point, and path planning can be completed, successful implementation disaster relief task.
Through full-scale investigation, carry out full-scale investigation by ZY08-C-G type avoidance dolly, it was demonstrated that the feasibility of experiment.From
The angle controlled is said, the method can apply to go in whole system and do not affect the stability of system well.
Claims (8)
1. a paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field, it is characterised in that these rule
The method of drawing is disturbance potential field method and original barrier completion method to be combined, and helps robot to flee from local minimum point, again
Planning avoidance path, it is to avoid be again absorbed in local minimum point；
Described local minimum point refer to robot under the paths planning method of Artificial Potential Field Method, due to gravitation potential field and repulsion gesture
The common effect of field, in the position not arriving target, makes a concerted effort to be zero, causing being absorbed in certain region cannot continue due to suffered potential field
Complete path planning；
Described barrier completion method is the detection device by Disaster Relief Robot, and spill barrier is carried out virtual filling so that it is
Ultimately form the barrier of more rule, it is to avoid produce local minimum point；
Described disturbance potential field method is when robot is absorbed in local minimum point, by introducing disturbance potential field letter in total potential field function
Number, changes total potential field function, helps underground rescue robot to walk out local minimum point.
Paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field the most according to claim 1,
It is characterized in that, known barrier completion method is combined by described paths planning method with the new disturbance potential field method proposed, when
When robot is absorbed in local minimum point, first passes through increase perturbation potential field function and help Disaster Relief Robot to walk out local minimum point,
Now carrying out spill barrier filling, structure is filled potential field function, is progressively walked out spill barrier, filling area along with robot
Being gradually increased, now repulsion potential field function is also gradually increased, and total potential field function changes, and Disaster Relief Robot path is advised again
Drawing, until spill barrier is completely filled, robot gets around spill barrier, eventually arrives at target location.
Paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field the most according to claim 2,
It is characterized in that, there is proportional relationship, disturbance potential field with the air line distance of robot to impact point in described perturbation potential field function
Function only just starts when Disaster Relief Robot is absorbed in local minimum point and adds total potential field function, other in moment robot only according to
The gravitation function of impact point and the repulsion function of barrier carry out path planning.
Paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field the most according to claim 2,
It is characterized in that, described barrier will produce new repulsion potential field function, scope of filling with Disaster Relief Robot away from local minimum
Point incrementally increases, and fills the inversely proportional relation of distance of potential field repulsion function and robot to local minimum point.
Paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field the most according to claim 1,
It is characterized in that, described paths planning method is when Disaster Relief Robot runs into spill barrier at underground coal mine, and specific works walks
Rapid as follows:
Step one: known underground coal mine global information is input in path planning system；
Step 2: detecting obstacles thing information, merges known underground coal mine global information, build barrier repulsion potential field model and
Impact point gravitational potential field model, carries out preliminary planning to path；
Step 3: disaster relief task starts, Disaster Relief Robot starts to run to impact point, until being absorbed in spill barrier local minimum
Point；Now making a concerted effort suffered by robot is zero, arrives the potential field minimum point in whole path planning；
Step 4: start to flee from local minimum point, starts perturbation potential field function, and now total potential field function changes, and robot occurs
Concussion, temporarily flees from local minimum point；
Step 5: be again absorbed in other local minimum points of spill barrier in order to avoid Disaster Relief Robot, proceed by spill
Barrier is filled, and filling scope is fled from local minimum point with Disaster Relief Robot and progressively expanded；
Step 6: after waiting a scan period, it is judged that robot has fled from local minimum point the most, if being still absorbed in local
Minimal point, returns to step 2 class model each to present stage and re-establishes, re-start path planning；If fleeing from local pole
Point, proceeds disaster relief task according to existing potential field model；
Step 7: robot arrives target location, and path planning terminates.
Paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field the most according to claim 5,
It is characterized in that, in described step 2, barrier repulsion potential field model is:
Fatt(X)=-gradUatt(X)=-Katt|X-Xg|
Impact point gravitational potential field function model is:
。
Paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field the most according to claim 5,
It is characterized in that, in described step 4, perturbation potential field function model is:
。
Paths planning method based on the underground coal mine Disaster Relief Robot improving Artificial Potential Field the most according to claim 5,
It is characterized in that, in described step 5, filling barrier repulsion potential field model is:
。
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106708054A (en) * | 2017-01-24 | 2017-05-24 | 贵州电网有限责任公司电力科学研究院 | Inspection robot path planning method combining map grid with potential field method obstacle avoidance |
| CN106873597A (en) * | 2017-03-24 | 2017-06-20 | 上海思岚科技有限公司 | The implementation method of virtual rail is set by computer client to mobile robot |
| CN107357293A (en) * | 2017-07-31 | 2017-11-17 | 上海应用技术大学 | Method for planning path for mobile robot and system |
| CN109696169A (en) * | 2019-03-07 | 2019-04-30 | 齐鲁工业大学 | Spill barrier avoidance air navigation aid and device, AGV trolley based on AGV trolley |
| CN109839956A (en) * | 2019-03-04 | 2019-06-04 | 北京邮电大学 | A kind of paths planning method and device of unmanned plane |
| CN110262478A (en) * | 2019-05-27 | 2019-09-20 | 浙江工业大学 | Man-machine safety obstacle-avoiding route planning method based on modified embedded-atom method |
| CN110262512A (en) * | 2019-07-12 | 2019-09-20 | 北京机械设备研究所 | A kind of mobile robot is detached from the barrier-avoiding method and system of U-shaped obstacle trap |
| CN110471429A (en) * | 2019-09-19 | 2019-11-19 | 华南农业大学 | Grass-removing robot Real-time Obstacle Avoidance Method based on modified embedded-atom method |
| CN110764411A (en) * | 2019-11-06 | 2020-02-07 | 大连理工大学 | Two-wheeled self-balancing vehicle autonomous obstacle avoidance method for multi-obstacle environment |
| CN111481933A (en) * | 2020-05-18 | 2020-08-04 | 浙江工业大学 | Game path planning method based on improved potential field grid method |
| CN111546343A (en) * | 2020-05-13 | 2020-08-18 | 浙江工业大学 | Method and system for planning route of defense mobile robot based on improved artificial potential field method |
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| CN116202550A (en) * | 2023-05-06 | 2023-06-02 | 华东交通大学 | Automobile path planning method integrating improved potential field and dynamic window |
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| CN106708054A (en) * | 2017-01-24 | 2017-05-24 | 贵州电网有限责任公司电力科学研究院 | Inspection robot path planning method combining map grid with potential field method obstacle avoidance |
| CN106708054B (en) * | 2017-01-24 | 2019-12-13 | 贵州电网有限责任公司电力科学研究院 | Routing planning method for inspection robot by combining map grids and potential field method obstacle avoidance |
| CN106873597A (en) * | 2017-03-24 | 2017-06-20 | 上海思岚科技有限公司 | The implementation method of virtual rail is set by computer client to mobile robot |
| CN106873597B (en) * | 2017-03-24 | 2020-11-10 | 上海思岚科技有限公司 | Method for realizing setting of virtual track for mobile robot through computer client |
| CN107357293A (en) * | 2017-07-31 | 2017-11-17 | 上海应用技术大学 | Method for planning path for mobile robot and system |
| CN109839956A (en) * | 2019-03-04 | 2019-06-04 | 北京邮电大学 | A kind of paths planning method and device of unmanned plane |
| CN109696169A (en) * | 2019-03-07 | 2019-04-30 | 齐鲁工业大学 | Spill barrier avoidance air navigation aid and device, AGV trolley based on AGV trolley |
| CN110262478A (en) * | 2019-05-27 | 2019-09-20 | 浙江工业大学 | Man-machine safety obstacle-avoiding route planning method based on modified embedded-atom method |
| CN110262478B (en) * | 2019-05-27 | 2022-04-19 | 浙江工业大学 | Man-machine safety obstacle avoidance path planning method based on improved artificial potential field method |
| CN110262512A (en) * | 2019-07-12 | 2019-09-20 | 北京机械设备研究所 | A kind of mobile robot is detached from the barrier-avoiding method and system of U-shaped obstacle trap |
| CN110262512B (en) * | 2019-07-12 | 2022-03-29 | 北京机械设备研究所 | Obstacle avoidance method and system for moving robot to separate from U-shaped obstacle trap |
| CN110471429A (en) * | 2019-09-19 | 2019-11-19 | 华南农业大学 | Grass-removing robot Real-time Obstacle Avoidance Method based on modified embedded-atom method |
| CN110764411A (en) * | 2019-11-06 | 2020-02-07 | 大连理工大学 | Two-wheeled self-balancing vehicle autonomous obstacle avoidance method for multi-obstacle environment |
| CN110764411B (en) * | 2019-11-06 | 2020-08-21 | 大连理工大学 | Two-wheeled self-balancing vehicle autonomous obstacle avoidance method for multi-obstacle environment |
| CN111546343A (en) * | 2020-05-13 | 2020-08-18 | 浙江工业大学 | Method and system for planning route of defense mobile robot based on improved artificial potential field method |
| CN111481933A (en) * | 2020-05-18 | 2020-08-04 | 浙江工业大学 | Game path planning method based on improved potential field grid method |
| CN111880522A (en) * | 2020-06-01 | 2020-11-03 | 东莞理工学院 | Novel autonomous assembly robot path planning autonomous navigation system and method |
| CN112947417A (en) * | 2021-01-27 | 2021-06-11 | 厦门大学 | Control method for obstacle avoidance of intelligent moving body |
| CN113084811A (en) * | 2021-04-12 | 2021-07-09 | 贵州大学 | Mechanical arm path planning method |
| CN113534819A (en) * | 2021-08-26 | 2021-10-22 | 鲁东大学 | Method and storage medium for pilot-follow multi-agent formation path planning |
| CN113534819B (en) * | 2021-08-26 | 2024-03-15 | 鲁东大学 | Method and storage medium for pilot following type multi-agent formation path planning |
| CN116202550A (en) * | 2023-05-06 | 2023-06-02 | 华东交通大学 | Automobile path planning method integrating improved potential field and dynamic window |
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