CN108983780A - One kind is based on improvement RRT*The method for planning path for mobile robot of algorithm - Google Patents

One kind is based on improvement RRT*The method for planning path for mobile robot of algorithm Download PDF

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CN108983780A
CN108983780A CN201810819848.5A CN201810819848A CN108983780A CN 108983780 A CN108983780 A CN 108983780A CN 201810819848 A CN201810819848 A CN 201810819848A CN 108983780 A CN108983780 A CN 108983780A
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node
point
new
algorithm
path
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朱宏辉
丁玉豪
明瑞冬
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Wuhan University of Technology WUT
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • 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
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • 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
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • 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
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • 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
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0242—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using non-visible light signals, e.g. IR or UV signals
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • 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
    • G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • 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
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Electromagnetism (AREA)
  • Acoustics & Sound (AREA)
  • Feedback Control In General (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The present invention discloses a kind of method for planning path for mobile robot based on improvement RRT* algorithm, and target bias strategy is introduced into standard RRT* algorithm, the randomness of sampled point is reduced；And propose and evade step-length extension method, it can enable random tree reasonably far from barrier zone, avoid falling into local minimum；And the path that RRT* algorithm obtains is improved to the present invention using the smooth strategy of inverted order examination connection and carries out path smooth processing, reduce robot commutation action, keeps robot motion more stable.The present invention improves RRT* algorithm compared with primary standard RRT* algorithm, and the path cooked up is more excellent, and it is less to expend the time.

Description

A kind of method for planning path for mobile robot based on improvement RRT* algorithm
Technical field
The invention belongs to mobile robot path planning technical fields, are related to a kind of robot path planning method, especially It is related to a kind of based on the method for planning path for mobile robot for improving RRT* algorithm.
Background technique
With the development of society, mobile robot is in human lives using more and more extensive.(robot is certainly for path planning It is main to find a feasible path from initial position to target position) it is the basis that robot completes work in every.Currently, global Path planning algorithm mainly has Dijkstra (Di Jiesitela) algorithm, A* algorithm, ant group algorithm etc..But most of algorithm applications It is the setting to algorithm design parameter in the successful key of path planning, and is not suitable for applying in higher-dimension complex space. In actual application, dijkstra's algorithm search rate is slower, and complexity is higher, to need to consume more meter Evaluation time.A* algorithm is more stringent to the selection requirement of heuristic function, and with the expansion of institute's research environment scale, its fortune It is exponential other for calculating space to increase.Ant group algorithm is easier to fall into local minimum again, causes that feasible road can not be cooked up Diameter.
Quick Extended random tree (RRT) algorithm based on sampling is one of the new method for path planning, is answered with other It is compared for path planning such as ant group algorithm, Artificial Potential Field Method with conventional methods such as A* algorithms, it has probability completeness and receipts Hold back the remarkable advantages such as fast speed, but since its search process is excessively average, thus the final path calculated usually all with Shortest path has relatively large deviation.Improved RRT algorithm-RRT* algorithm, it is while inheriting RRT algorithm probability completeness Be also equipped with asymptotical optimality to get to final path be closer to shortest feasible path.But the deficiency of RRT* algorithm It is in a length of cost when its asymptotical optimality is to slow down rate of convergence and increase search.
Thus, it is desirable to have a kind of can improve existing skill based on the method for planning path for mobile robot for improving RRT* algorithm The drawbacks described above of art.
Summary of the invention
The object of the present invention is to provide it is a kind of based on improve RRT* algorithm method for planning path for mobile robot, overcome on The shortcomings that stating the prior art reduces the randomness of search, improves rate of convergence, it is possible to prevente effectively from local minimum is fallen into, Robot is set successfully barrier to be avoided quickly to reach target point.
To achieve the above object, the technical solution adopted by the present invention is that:
A kind of method for planning path for mobile robot based on improvement RRT* algorithm, it is characterised in that the following steps are included:
(1) laser radar sensor, ultrasonic sensor, the infrared sensor carried by mobile robot acquires machine Manually make environmental information, carry out grating map modeling, is barrier zone or clear area by each grid tag, and determine Starting point and target point；
(2) stochastical sampling point x is generated in clear area using the bigoted strategy of targetrand, the randomness of search is reduced, Improve search efficiency；
(3) it finds and stochastical sampling point xrandNearest node xnearest；
(4) it is inserted into new node xnew；
(5) point set in new node contiguous range is traversed, minimum cost value node is found；
(6) reconnection operation is carried out to neighborhood node；
(7) it repeats the above steps, until xnewFall in destination node xgoalPredeterminable area within the scope of, obtain optimal rule Draw path；
(8) it introduces inverted order examination connection smooth strategy and path smooth processing is carried out to the path that step (7) obtain, obtain machine People optimal movement path under practical circumstances.
Further, in step (2), it is specific that stochastical sampling point is generated in clear area using target bias strategy Are as follows: when obtaining sampled point at random each time, first it is distributed according to non-uniform probability and obtains a probability value p at randomg, work as pgLess than first Begin the threshold value p setmax, then selection target point xgoalFor sampled point；Otherwise, sampled point is obtained at random in global area.
Further, in step (4), new node specific steps are inserted into are as follows:
(4.1) in clear area, in straight line xrandxnearestOne point x of upper interceptionnew, make | | xnearest-xnew| | etc. In step-size in search p, if line segment xnewxnearestIt is not exposed to barrier zone, just node xnewIt is inserted into as new node, it is no Then give up xnew, return step (2), wherein | | xnearest-xnew| | indicate xnearestTo xrandBetween Euclidean distance；
(4.2) work as xgoal=xrandAnd extension new node is when touching barrier, then using evade step-length extension method be inserted into it is new Node.
Further, in step (5), minimum cost value node is found specifically: traverse node xnewPoint set in contiguous range Xnear, compare one by one from node xiTo xstartWith xiTo xnewThe sum of cost value Cost | | xnew-xi||+Cost||xi-xstart| |, wherein Cost | | xm-xn| | indicate node xmPath is to node x in sequencenPath length, the smallest point x of neutralization numberi As minimum cost value node xmin。
Further, in step (6), reconnection operation specific steps are carried out to neighborhood node are as follows:
(6.1) node xminAs xnewFather node, be then turned off xnewWith the connection of former father node, x is connectednewWith xmin；
(6.2) for point set XnearIn except node xminExcept any node xi, by Cost | | xnew-xstart| | with Cost||xi-xnew| | the sum of and Cost | | xi-xstart| | it compares, if the former is smaller, disconnects xiWith original father node Connection, by xnewThe father node new as its, and recurrence changes xiChild node connection, it is on the contrary then keep original company It is constant to connect relationship.
Further, in step (8), path smooth processing is carried out to the path that step (7) obtains using inverted order examination connection Specifically includes the following steps:
(8.1) all nodes on final path that step (7) obtains successively are labeled as x0,x1,x2,···,xn-1, xn, wherein x0With xnStart node and destination node are respectively represented, and enables caching point set X={ x0}；
(8.2) tie point x is attemptedaInitial value is starting point x0, another trial tie point xbIt is initialized as destination node xn, Judge xaWith xbWhether line between the two can touch barrier zone, by x if it can touch barrierbAgain assignment xn-1, again to current xaWith xbBetween line carry out collision judgment, repeatedly, until search out first node xk(1≤ K≤n), n is node total number, works as xb=xkWhen meet xaAnd xbBetween line will not touch barrier until, and will at this time Node xbIt is added in point set X；
(8.3) by xaIt is assigned a value of xb, by xbIt is assigned a value of xn, the collision judgment process of step (8.2) is repeated, and will meet non- The new node x of impact conditionsbIt is added in point set X；
(8.4) step (8.3) are repeated, until finally working as xb=xnWhen, meet xaWith xbBetween line will not touch barrier Hinder object, then by x at this timebIt is added in point set X, is then sequentially connected the node of point set X, as final smooth path.
Further, in step (4.2), the detailed process for evading step-length extension method includes:
A, with xnearestIt is that radius work is justified for the center of circle, step-size in search p, finds the point x for meeting condition on circumferencetFor extension section Point is inserted into as new node；
If the point x of the condition of satisfaction B, can not be foundt, then give up this extended operation, return step (2).
Further, the point x in step A, on circumferencetThe condition of satisfaction specifically includes:
1. point xtWith point xnearestBetween line without barrier zone；
2. point xtWith point xnearestThe distance between value be a step-length；
3. the condition that meets 1. with condition 2. under the premise of, point xnearestWith point xtDirection line and point xnearestWith point xgoalDirection line angle theta it is minimum, and to avoid node towards target point inverse expansion, θ is no more than 90 degree.
Further, in step (7), predeterminable area specifically: | | xgoal-xnew| | it is less than or equal to step-size in search, and Line segment xgoalxnewIt is not exposed to barrier zone.
The medicine have the advantages that
Of the invention is existed based on the method for planning path for mobile robot for improving RRT* algorithm for basic RRT* algorithm The problem of improved from following two points: 1) introduce target bias strategy, reduce the randomness of sampled point；2) basic herein On, it proposes and evades step-length extension method, random tree is enabled reasonably far from barrier zone, to avoid falling into local minimum. Of the invention not only ensure that effective obstacle avoidance ability based on the method for planning path for mobile robot for improving RRT* algorithm, simultaneously Rate of convergence is also improved, robot success avoiding obstacles is made quickly to reach target point.
Detailed description of the invention
Fig. 1 is RRT* algorithm expanding node process schematic；
Fig. 2 is to evade step-length extension method schematic diagram；
Fig. 3 is that inverted order tries connection flow chart；
Fig. 4 is that standard RRT* algorithm finds optimal path result schematic diagram under Rectangular Obstacles environment；
Fig. 5 is that present invention improvement RRT* algorithm finds optimal path result schematic diagram under Rectangular Obstacles environment；
Fig. 6 is that standard RRT* algorithm finds optimal path result schematic diagram under round obstacle environment；
Fig. 7 is that present invention improvement RRT* algorithm finds optimal path result schematic diagram under Rectangular Obstacles environment；
Fig. 8 is that standard RRT* algorithm finds optimal path result schematic diagram in the case where mixing obstacle environment；
Fig. 9 is that present invention improvement RRT* algorithm finds optimal path result schematic diagram in the case where mixing obstacle environment；
Figure 10 is that the present invention improves RRT* algorithm smooth paths figure under Rectangular Obstacles environment；
Figure 11 is that the present invention improves RRT* algorithm smooth paths figure under round obstacle environment；
Figure 12 is that the present invention improves RRT* algorithm smooth paths figure in the case where mixing obstacle environment；
Figure 13 is that standard RRT* algorithm and the present invention improve RRT* algorithm iteration number comparison diagram；
Figure 14 is that standard RRT* algorithm and the present invention improve the final path length comparison diagram of RRT* algorithm.
Specific embodiment
To keep the purposes, technical schemes and advantages of the invention implemented clearer, below in conjunction in the embodiment of the present invention Attached drawing, technical solution in the embodiment of the present invention is further described in more detail.In embodiments of the present invention, path planning Method the following steps are included:
(1) laser radar sensor, ultrasonic sensor, the infrared sensor carried by mobile robot acquires machine Manually make environmental information, carry out grating map modeling, is barrier zone or clear area by each grid tag, and determine Starting point and target point；For Fig. 4 into Figure 12, black portions are barrier zone, and white portion is clear area；S point is starting Point, G point are target point.
(2) stochastical sampling point is generated in clear area using the bigoted strategy of target, reduces the randomness of search, improves Search efficiency；
(3) it finds and stochastical sampling point xrandNearest node xnearest；
(4) it is inserted into new node xnew；
(5) point set in new node contiguous range is traversed, minimum cost value node is found；
(6) reconnection operation is carried out to neighborhood node.
(7) it repeats the above steps, until xnewFall in destination node xgoalPredeterminable area within the scope of, obtain optimal rule Draw path.
(8) it introduces inverted order examination connection smooth strategy and path smooth processing is carried out to the path that step (7) obtain, obtain machine People optimal movement path under practical circumstances.
In the step (2), stochastical sampling point is generated in clear area using target bias strategy specifically: every When primary random acquisition sampled point, first it is distributed according to non-uniform probability and obtains a probability value p at randomg, work as pgLess than initially set Threshold value pmax, then selection target point xgoalFor sampled point；Otherwise, sampled point is obtained at random in global area.
In the step (4), it is inserted into new node specific steps are as follows:
(4.1) in clear area, in straight line xrandxnearestOne point x of upper interceptionnew, make | | xnearest-xnew|| (indicate xnearestTo xrandBetween Euclidean distance) be equal to step-size in search p.If line segment xnewxnearestIt is not exposed to obstacle Region, just node xnewIt is inserted into as new node, otherwise gives up xnew, return step (2).
(4.2) work as xgoal=xrandAnd it extends then new using the insertion of step-length extension method is evaded when new node touches barrier Node.
In the step (5), minimum cost value node is found specifically: traverse node xnewPoint set X in contiguous rangenear, Compare one by one from node xiTo xstartWith xiTo xnewThe sum of cost value Cost | | xnew-xi||+Cost||xi-xstart| | (wherein Cost||xm-xn| | indicate node xmPath is to node x in sequencenPath length), and the smallest point x of valueiIt is as minimum Cost value node xmin。
In the step (6), reconnection operation is carried out to neighborhood node specifically:
(6.1) node xminAs xnewFather node, be then turned off xnewWith the connection of former father node, x is connectednewWith xmin。
(6.2) for point set XnearIn except node xminExcept any node xi, by Cost | | xnew-xstart| | with Cost||xi-xnew| | the sum of and Cost | | xi-xstart| | it compares.If the former is smaller, x is disconnectediWith original father node Connection, by xnewThe father node new as its, and recurrence changes xiChild node connection, it is on the contrary then keep original company It is constant to connect relationship.
In the step (4.2), new node is inserted into using step-length extension method is evaded specifically:
A, with xnearestIt is that radius work is justified for the center of circle, step-size in search p, finds the point x for meeting condition on circumferencetFor extension section Point is inserted into as new node；
If the point x of the condition of satisfaction B, can not be foundt, then give up this extended operation, return step (2)；
In the step A, expanding node xtThe condition for needing to meet specifically:
1. point xtWith point xnearestBetween line without barrier zone；
2. point xtWith point xnearestThe distance between value be a step-length；
3. the condition that meets 1. with condition 2. under the premise of, point xnearestWith point xtDirection line and point xnearestWith point xgoalDirection line angle theta it is minimum, and to avoid node towards target point inverse expansion, θ is no more than 90 degree.
Step (1) is repeated to step (7), until xnewFall in destination node xgoalPredeterminable area within the scope of.
The improved RRT* pseudo-code of the algorithm of the present invention is as shown in table 1:
The improved RRT* pseudo-code of the algorithm table of table 1
The present invention generates path by solution and there are a large amount of unnecessary redundancies turnover node problems, and it is flat to introduce inverted order examination connection Sliding strategy carries out path smooth processing to the path that step (7) obtain, as shown in figure 3, specifically includes the following steps:
(8.1) all nodes on final path that step (7) obtains successively are labeled as x0,x1,x2,···,xn-1, xn, n is node total number, wherein x0With xnStart node and destination node are respectively represented, and enables caching point set X={ x0}。
(8.2) tie point x is attemptedaInitial value is starting point x0, another trial tie point xbIt is initialized as destination node xn, Judge xaWith xbWhether line between the two can touch barrier zone, by x if it can touch barrierbAgain assignment xn-1, again to current xaWith xbBetween line carry out collision judgment, repeatedly, until search out first node xk(1≤ K≤n), work as xb=xkWhen meet xaAnd xbBetween line will not touch barrier until, and will node x at this timebIt is added to a little Collect in X；
(8.3) by xaIt is assigned a value of xb, by xbIt is assigned a value of xn, the collision judgment process of step (8.2) is repeated, and will meet non- The new node x of impact conditionsbIt is added in point set X；
(8.4) step (8.3) are repeated, until finally working as xb=xnWhen, meet xaWith xbBetween line will not touch barrier Hinder object, then by x at this timebIt is added in point set X, is then sequentially connected the node of point set X, as final smooth path；
The validity and feasibility that RRT* algorithm is improved for the verifying present invention, at association M490 of 64 Windows 7 Under this (Intel Core processor, dominant frequency 2.5GHz, memory 4GB) hardware environment of note, it is imitative that simulation is carried out using MATLAB2009a Very, the two-dimensional surface that global state region is 1000 × 1000, starting point S (300,100), target point G (900,800).For Simulated environment, selection step-size in search are p=25, radius of neighbourhood r=50, probability threshold value pmax=0.2.
RRT* algorithm is improved to robot path using standard RRT* algorithm and the present invention first under Rectangular Obstacles environment It is planned, experimental result is as shown in Figure 4 and Figure 5.
For verifying, the present invention improves RRT* algorithm adaptability under various circumstances, under round obstacle environment and mixed type It improves RRT* algorithm using standard RRT* algorithm and the present invention respectively under obstacle environment to plan robot path, experiment knot Fruit is as shown in Figures 6 to 9.
RRT* algorithm is improved for the verifying present invention and introduces the validity and feasibility that inverted order tries the smooth strategy of connection, in rectangle The good optimal road of RRT* algorithmic rule is improved to the present invention respectively under obstacle environment, round obstacle environment and mixed type obstacle environment Diameter carries out path smooth processing, and experimental result is as shown in Figure 10 to Figure 12.Figure 10 to Figure 12 is corresponded with Fig. 6 to Fig. 9 respectively.
After can be seen that the smooth strategy of present invention improvement RRT* algorithm introduction inverted order examination connection such as Figure 10 to Figure 12, redundancy Turnover node is greatly reduced, and path becomes more smooth, reduces commutation action of the robot in moving process, so that machine Device people can smoothly reach target point.
The stability that RRT* algorithm is improved for the verifying present invention, is run multiple times standard RRT* algorithm and innovatory algorithm of the present invention Path planning is carried out, record each run is as a result, the experimental data comparison diagram such as figure that statistics the number of iterations and path length obtain Shown in 13 and Figure 14.
RRT* algorithm is improved compared with standard RRT* algorithm as Figure 13 and Figure 14 can be seen that the present invention, required iteration Number and planning path length have apparent reduction.
It can be calculated table 2 according to Figure 13 and Figure 14 data.
2 two kinds of algorithm experimental Comparative result analytical tables of table
Note: I indicates that mean iterative number of time, L indicate average path length
From table 2 it can be seen that the present invention improves the validity and feasibility of RRT* algorithm.In two kinds of different barrier zones Under, RRT* algorithm is improved compared with original RRT* algorithm, and mean iterative number of time reduces 30% to 40%, and average planning path is long Degree reduces 5% or so.Therefore it is more excellent than the path that standard RRT* algorithmic rule comes out to demonstrate present invention improvement RRT* algorithm, It is less to expend the time.

Claims (9)

1. a kind of based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that the following steps are included:
(1) laser radar sensor, ultrasonic sensor, the infrared sensor acquisition machine carried by mobile robot is artificial Make environmental information, carry out grating map modeling, is barrier zone or clear area by each grid tag, and determine starting Point and target point；
(2) stochastical sampling point x is generated in clear area using the bigoted strategy of targetrand, the randomness of search is reduced, is improved Search efficiency；
(3) it finds and stochastical sampling point xrandNearest node xnearest；
(4) it is inserted into new node xnew；
(5) point set in new node contiguous range is traversed, minimum cost value node is found；
(6) reconnection operation is carried out to neighborhood node；
(7) it repeats the above steps, until xnewFall in destination node xgoalPredeterminable area within the scope of, obtain optimal planning road Diameter；
(8) it introduces inverted order examination connection smooth strategy and path smooth processing is carried out to the path that step (7) obtain, obtain robot and exist Optimal movement path under actual environment.
2. according to claim 1 based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that: In step (2), stochastical sampling point is generated in clear area using target bias strategy specifically: obtain random each time When sampled point, first it is distributed according to non-uniform probability and obtains a probability value p at randomg, work as pgLess than threshold value p initially setmax, then select Select target point xgoalFor sampled point；Otherwise, sampled point is obtained at random in global area.
3. according to claim 1 based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that: In step (4), it is inserted into new node specific steps are as follows:
(4.1) in clear area, in straight line xrandxnearestOne point x of upper interceptionnew, make | | xnearest-xnew| | equal to searching Suo Buchang p, if line segment xnewxnearestIt is not exposed to barrier zone, just node xnewIt is inserted into as new node, otherwise gives up Abandon xnew, return step (2), wherein | | xnearest-xnew| | indicate xnearestTo xrandBetween Euclidean distance；
(4.2) work as xgoal=xrandAnd extension new node is when touching barrier, then is inserted into new section using evading step-length extension method Point.
4. according to claim 1 based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that: In step (5), minimum cost value node is found specifically: traverse node xnewPoint set X in contiguous rangenear, compare one by one from section Point xiTo xstartWith xiTo xnewThe sum of cost value Cost | | xnew-xi||+Cost||xi-xstart| |, wherein Cost | | xm-xn| | indicate node xmPath is to node x in sequencenPath length, the smallest point x of neutralization numberiAs minimum cost value node xmin。
5. according to claim 1 based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that: In step (6), reconnection operation specific steps are carried out to neighborhood node are as follows:
(6.1) node xminAs xnewFather node, be then turned off xnewWith the connection of former father node, x is connectednewWith xmin；
(6.2) for point set XnearIn except node xminExcept any node xi, by Cost | | xnew-xstart| | with Cost | | xi-xnew| | the sum of and Cost | | xi-xstart| | it compares, if the former is smaller, disconnects xiIt, will with the connection of original father node xnewThe father node new as its, and recurrence changes xiChild node connection, it is on the contrary then keep original connection relationship not Become.
6. according to claim 1 based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that: In step (8), step (7) is obtained using inverted order examination connection path progress path smooth processing specifically includes the following steps:
(8.1) all nodes on final path that step (7) obtains successively are labeled as x0,x1,x2,···,xn-1,xn, Middle x0With xnStart node and destination node are respectively represented, and enables caching point set X={ x0}；
(8.2) tie point x is attemptedaInitial value is starting point x0, another trial tie point xbIt is initialized as destination node xn, judge xa With xbWhether line between the two can touch barrier zone, by x if it can touch barrierbAgain assignment xn-1, then It is secondary to current xaWith xbBetween line carry out collision judgment, repeatedly, until search out first node xk(1≤k≤n), N is node total number, works as xb=xkWhen meet xaAnd xbBetween line will not touch barrier until, and will node x at this timebAdd Enter into point set X；
(8.3) by xaIt is assigned a value of xb, by xbIt is assigned a value of xn, the collision judgment process of step (8.2) is repeated, and non-collision item will be met The new node x of partbIt is added in point set X；
(8.4) step (8.3) are repeated, until finally working as xb=xnWhen, meet xaWith xbBetween line will not touch barrier, Then by x at this timebIt is added in point set X, is then sequentially connected the node of point set X, as final smooth path.
7. according to claim 3 based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that: In step (4.2), the detailed process for evading step-length extension method includes:
A, with xnearestIt is that radius work is justified for the center of circle, step-size in search p, finds the point x for meeting condition on circumferencetFor expanding node, i.e., It is inserted into as new node；
If the point x of the condition of satisfaction B, can not be foundt, then give up this extended operation, return step (2).
8. according to claim 7 based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that: Point x in step A, on circumferencetThe condition of satisfaction specifically includes:
1. point xtWith point xnearestBetween line without barrier zone；
2. point xtWith point xnearestThe distance between value be a step-length；
3. the condition that meets 1. with condition 2. under the premise of, point xnearestWith point xtDirection line and point xnearestWith point xgoal's The angle theta of direction line is minimum, and to avoid node towards target point inverse expansion, θ is no more than 90 degree.
9. according to claim 1 based on the method for planning path for mobile robot for improving RRT* algorithm, it is characterised in that: In step (7), predeterminable area specifically: | | xgoal-xnew| | it is less than or equal to step-size in search, and line segment xgoalxnewDo not connect Contact barrier zone.
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Cited By (58)

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Publication number Priority date Publication date Assignee Title
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WO2023197092A1 (en) * 2022-04-11 2023-10-19 电子科技大学 Unmanned aerial vehicle path planning method based on improved rrt algorithm

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103914068A (en) * 2013-01-07 2014-07-09 中国人民解放军第二炮兵工程大学 Service robot autonomous navigation method based on raster maps
WO2015167220A1 (en) * 2014-05-02 2015-11-05 한화테크윈 주식회사 Device for planning path of mobile robot and method for planning path of mobile robot
CN106774329A (en) * 2016-12-29 2017-05-31 大连理工大学 A kind of robot path planning method based on oval tangent line construction
CN107085437A (en) * 2017-03-20 2017-08-22 浙江工业大学 A kind of unmanned aerial vehicle flight path planing method based on EB RRT
CN107631734A (en) * 2017-07-21 2018-01-26 南京邮电大学 A kind of dynamic smoothing paths planning method based on D*_lite algorithms

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103914068A (en) * 2013-01-07 2014-07-09 中国人民解放军第二炮兵工程大学 Service robot autonomous navigation method based on raster maps
WO2015167220A1 (en) * 2014-05-02 2015-11-05 한화테크윈 주식회사 Device for planning path of mobile robot and method for planning path of mobile robot
CN106774329A (en) * 2016-12-29 2017-05-31 大连理工大学 A kind of robot path planning method based on oval tangent line construction
CN107085437A (en) * 2017-03-20 2017-08-22 浙江工业大学 A kind of unmanned aerial vehicle flight path planing method based on EB RRT
CN107631734A (en) * 2017-07-21 2018-01-26 南京邮电大学 A kind of dynamic smoothing paths planning method based on D*_lite algorithms

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
D. FOX 等: "The dynamic window approach to collision avoidance", 《IEEE ROBOTICS & AUTOMATION MAGAZINE》 *
SERTAC KARAMAN 等: "Anytime Motion Planning using the RRT*", 《2011 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION》 *
朱宏辉 等: "基于改进RRT*算法的路径规划", 《武汉理工大学学报》 *

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CN113341984A (en) * 2021-06-15 2021-09-03 桂林电子科技大学 Robot path planning method and device based on improved RRT algorithm
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CN113515123B (en) * 2021-06-25 2024-04-12 北京精密机电控制设备研究所 Robot real-time path planning method based on improved RRT algorithm
CN113515127A (en) * 2021-07-23 2021-10-19 华北电力大学（保定） Mobile robot path planning method and system
CN113485375B (en) * 2021-08-13 2023-03-24 苏州大学 Indoor environment robot exploration method based on heuristic bias sampling
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CN113448336A (en) * 2021-08-30 2021-09-28 天津施格机器人科技有限公司 3D obstacle avoidance path planning method
CN114415718A (en) * 2021-12-22 2022-04-29 中国航天科工集团八五一一研究所 Three-dimensional track planning method based on improved potential field RRT algorithm
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CN114489052B (en) * 2021-12-31 2024-05-28 杭州电子科技大学 Path planning method for improving RRT algorithm reconnection strategy
WO2023197092A1 (en) * 2022-04-11 2023-10-19 电子科技大学 Unmanned aerial vehicle path planning method based on improved rrt algorithm
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CN116820136A (en) * 2023-08-25 2023-09-29 雄安国创中心科技有限公司 Unmanned aerial vehicle path optimization method and device based on RRT algorithm

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