CN107883961A - A kind of underwater robot method for optimizing route based on Smooth RRT algorithms - Google Patents
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- 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
- G01C21/20—Instruments for performing navigational calculations
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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
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Abstract
The invention discloses a kind of underwater robot method for optimizing route based on Smooth RRT algorithms, belong to optimization field.For the characteristics of underwater robot working environment is complicated, requirement of real-time is high, to meet path planning needs, herein on the basis of classical Quick Extended tree (RRT) algorithm, the method for optimizing route of Smooth RRT algorithms is proposed.The selection for adding convergence factor and angular factors to improve the growing point of expansion tree and explore point, improves algorithm speed and practicality.To take into account underwater robot apart from the most short and particular/special requirement of handling, using greedy algorithm to path planning smoothing processing.Test result indicates that：This method can be rapidly completed route searching, shorten planning distance while search efficiency is improved, and the path after optimization processing meets the requirement of underwater robot planning system more suitable for the tracking of robot.
Description
Technical field
The present invention relates to optimization field, and in particular to a kind of underwater robot path optimization based on Smooth-RRT algorithms
Method.
Background technology
Robot path planning can be divided into global path planning and local paths planning, when unknown or part is unknown to environment
Shi Caiyong local paths plannings.Because complicated marine environment is merely with the global path planning of sea chart, it is impossible to unknown seabed
Landform and ship etc., which are made, timely and effectively reacts.Path planning is an important directions of robot research, is referred to according to certain
One performance indications, robot searches one in residing environment can avoid obstacle from original state to dbjective state most
Excellent or sub-optimal path.Traditional path planning algorithm has Grid Method, Artificial Potential Field Method, polygon approach method, genetic algorithm etc.,
But the barrier that these methods are required in pair determination space is modeled, and is not suitable for the path planning for solving in complex environment.
Quick Extended tree (RRT) algorithm carries out collision detection in state space to sampled point, and being not required to be modeled space can be fast
Speed effectively searches for higher dimensional space.
Problems be present while completing to explore circumstances not known and plan in RRT algorithms：It is random in sample space
Sampling, therefore same task is repeatedly planned, caused path is all different every time, influences the efficiency of task completion, and to machine
Device human organism causes damage.Many researchers propose the innovatory algorithm of correlation such as：The dynamic RRT based on forecast model is calculated
Method, reduce planning time；KD tree concepts, improve search efficiency.But these improved methods do not account for underwater robot
Movement angle limitation, caused path is not smoothed, easily generation stairstepping and zigzag section, to machine
Device human organism causes damage.A kind of method for optimizing route based on Smooth-RRT algorithms is proposed for this present invention, adds angle
Path planning is handled using greedy algorithm while the factor, make caused by path more it is smooth it is smooth, tend to be practical,
So as to obtain the optimum programming path of underwater robot.
The content of the invention
It is an object of the invention to provide a kind of underwater robot method for optimizing route based on Smooth-RRT algorithms, sheet
Method, which realizes, improves RRT algorithms to the Optimum utilization in path, makes it meet needed for underwater personal performance, to examine simultaneously
Consider and be smoothed to improving RRT generations path.So not only caused path is more smooth smooth, while significantly contracts
The steering number of underwater robot is subtracted.
The object of the present invention is achieved like this：
It is an object of the invention to provide a kind of underwater robot method for optimizing route based on Smooth-RRT algorithms, its
It is characterised by, comprises the steps of：
Step 1：Initialize T1=xinit, xinitFor initial position；
Step 2：Judge | xinit-xgoal|≤ρ, if so, going to step 11, it is not, then goes to step 3；xgoalFor mesh
Cursor position；
Step 3：Generate random point xrand；
Step 4：Given one 0 to 1 bias variable Bias, the random number rand that generation is one 0 to 1；
Step 5：If rand<Bias, then xrand=xgoal；Otherwise, xrandIt is constant；
Step 6：Find out xnearMake D (xnew,xrand)≤D(x,xrand)；xnearFor distance xrandNearest point；xnewTo expand
The new exploration node of exhibition；
Step 7：According to the father node x of current locationnear-1, current location xnearAnd xrandObtain in outgoing direction knots modification
θ；If θ ＜ θmaxThen perform xnear+ ρ θ, otherwise perform (xrand-xnear)·θmax；ρ is fixed step size；θmaxFor hard-over
The scope for exploring point is limited for constraints；θ is direction knots modification；
Step 8：In xnewAnd xrandLine on seek xnewMake D (xnew,xnear)=ρ, and xnew∈CfreeIf in the presence of this
The x of samplenew, step 9 is gone to, if being not present, goes to step 3；
Step 9：Increase node, T on expansion treek+1=Tk+xnew；
Step 10：Judge | xnew-xgoal|≤ρ, if so, going to step 11, it is not, then goes to step 3；
Step 11：Path is smoothed using greedy algorithm；
Step 12：Terminate, obtain the path after optimization.
Described greedy algorithm, it is characterised in that make xnew=x0, connection x is attempted successively1,x2,...xNUntil that can not arrive
The first node x reachedi, then xi-1It is exactly xnewReachable point, replace x using straight linenewAnd xi-1Between path；Order
xtemp=xi-1, said process is repeated, until xnew=xN；Added to take into account the requirement of robot manipulation's performance in greedy algorithm
Angular factors θmax；Final x0,x1,...xNBetween path be smoothed as some necklace straight lines, make path more fairing.
The beneficial effects of the present invention are：
(1) improve expansion tree growing point and explore point selection, make tree extension have the trend for tending to target point and
Cook up the optimal route for tending to be practical.
(2) path planning is smoothed, eliminates the unnecessary point in path planning, make path more smooth suitable
Freely, while the steering number of underwater robot is also reduced.
Brief description of the drawings
Fig. 1 is the RRT algorithm flow charts added after convergence factor and angular factors；
Fig. 2 is angular factors θmaxRepresent figure；
Fig. 3 is selection θmaxPath planning figure under classics RRT algorithms at=60 °；
Fig. 4 is selection θmaxPath planning figure under RRT algorithms after being improved at=60 °；
Fig. 5 is selection θmaxPass through greedy algorithm smoothing processing rear path planning chart at=60 °；
Fig. 6 is selection θmaxPath planning figure under classics RRT algorithms at=30 °；
Fig. 7 is selection θmaxPath planning figure under RRT algorithms after being improved at=30 °；
Fig. 8 is selection θmaxPass through greedy algorithm smoothing processing rear path planning chart at=30 °.
Embodiment
Illustrate below in conjunction with the accompanying drawings and the present invention is described in more detail：
A kind of underwater robot path optimization based on Smooth-RRT algorithms proposed by the present invention specifically includes following several
Individual step.
Step 1：Underwater robot movement angle problem.
Curve determined by reference point is motion path in underwater robot motion planning, and it is a time series,
Being translated into mathematic(al) representation can be expressed as：
S represents the path length in unit, SnRepresent the distance from origin-to-destination.The tangential angle θ (s) and fortune in path
The dynamic equal θ in angle：
θ=θ (s) (2)
The movement angle of underwater robot is：ψ (s)=θ (s)-β (s), wherein β (s) are the hydrodynamic force of underwater robot
Angle, as β (s)=0, just there is ψ (s)=θ (s), underwater robot can carry out translational motion, elevating movement and rotary motion,
The present invention only considers two directions of motion of its rotation and translation.
Step 2：Classical RRT algorithms.
RRT algorithms include structure expansion tree and inquiry two stages of shortest path.RRT algorithms are first passed through in task space G
One point x of middle random selectionrand, the then detection range x in current RRTrandNearest xnear, finally given birth to according to fixed step size ρ
Grow new node xnew.Then the node x of extension is judged in the growth course of treenewWhether target point is reached, once reach
Search then is retracted to starting point from the point, so as to obtain a complete path.Comprise the following steps that：
1.T1=xinit；
2. judge | xinit-xgoal|≤ρ, if so, going to step 8, it is not, goes to step 3；
3. generate random point xrand；
4. find out xnearMake D (xnew,xrand)≤D(x,xrand)；
5. in xnewAnd xrandLine on seek xnewMake D (xnew,xnear)=ρ, and xnew∈CfreeIf as existing
xnew, step 6 is gone to, if being not present, goes to step 3；
6. increase node on expansion tree, Tk+1=Tk+xnew；
7. judge | xnew-xgoal|≤ρ, if so, going to step 8, it is not, goes to step 3；
8. terminate.
Step 3：Based on Smooth-RRT innovatory algorithms.Add convergence factor and angular factors improve growing point, explored
Point, and using greedy algorithm to path smoothing processing.
Classical RRT algorithms are sampled in many unnecessary regions, waste substantial amounts of calculating time and cost, algorithm
Convergence rate is slower.Learn that the random point that has its source in for causing RRT algorithm drawbacks described above produces at random in the total space by analysis
It is raw.Therefore it is determined that add convergence factor Bias during random point, the extension of tree is made have the trend for tending to target point.
Work as rand<Have during Bias,
xrand=xgoal (4)
In view of this method is not in total space random distribution, therefore improves the search efficiency of algorithm, and due to tree node
Extension be intended to target point, cook up in theory come path can also be closer to shortest path.Step as exists
The 3rd step is done into following improvement in basic RRT algorithms：
(1) random point x is generatedrand；
(2) the bias variable Bias of one 0 to 1, the random number rand that generation is one 0 to 1 are given；
(3) if rand<Bias, then xrand=xgoal；Otherwise, xrandIt is constant.
With hard-over θmaxThe scope for exploring point is limited for constraints, so as to cook up the optimal route for tending to be practical.
Random point x is produced firstrand, with xrandFor target point, calculate and explore point xnew, to make planning rear path meet manipulation constraint, need
Will be according to the father node x of current locationnear-1, current location xnearAnd xrandIt is the θ in (2) to obtain outgoing direction knots modification, for super
The random point x gone out in coveragerand, with hard-over θmaxX is calculated for restrictive conditionnew：
By the improvement chosen to exploring point, probing direction is limited with hard-over, can obtain tending to practical path.It is comprehensive
On to RRT algorithm improvements flow such as Fig. 1.
In view of often there is shake and unnatural phenomenon in the path that the RRT algorithmic rules after improving go out.When environment is complicated
When, many unnecessary breaks can be produced for avoiding obstacles, are difficult tracking for robot.Therefore, it is greedy set forth herein utilizing
Center algorithm is smoothed to path.The thought of greedy algorithm is exactly the original state from problem, by a series of
Greed is selected to be resolved a kind of solution approach of Optimum Solution.The scope that he is applicable is that problem has optimal sub- knot
Structure and greed select, and provide simple efficient solution method with the strategy of greed selection.
Herein RRT algorithms inquiry phase, from xgoalStart to search father node one by one, until the root node of random tree
xinitUntill.Form path node queue and be expressed as (x0,x1,...xN), x0Represent initial point xinit, xNRepresent target point xgoal。
Then caused path is carried out using greedy thought smoothly, x being made in processing procedurenew=x0, connection x is attempted successively1,
x2,...xNUntil the first node x that can not be reachedi, then xi-1It is exactly xnewReachable point, replace x using straight linenewWith
xi-1Between path.Make xtemp=xi-1, said process is repeated, until xnew=xN.To take into account the requirement of robot manipulation's performance
Angular factors θ is added in greedy algorithmmax.Such as Fig. 2, final x0,x1,...xNBetween path be smoothed for some necklaces it is straight
Line, make path more fairing.
Step 4：Experimental analysis.
In order to verify the practicality of Smooth-RRT algorithms and superiority, l-G simulation test is carried out using MATLAB, will be improved
Algorithm is contrasted after rear RRT algorithms and smoothing processing, and the lower left corner is starting point (0,0) in Fig. 3-8, and the upper right corner is target
Node (100,100), state space randomly generate 100 different barriers of radius for (x, y)=(100,100) and transported
Dynamic planning.Green broken line to improve RRT algorithms path, discount as the result after the smoothing processing of same planning problem by red.If
Step-size in search ρ=5, maximum turning angle θmaxRespectively 60 ° and 30 °.
The randomness that Fig. 3 and Fig. 6 can be seen that classical RRT algorithms is stronger, and than more tortuous, robot steering is frequent in path.
Added by Fig. 4 is visible with Fig. 7 after convergence factor and angular factors to the searcher tropism in path more clearly and algorithm the convergence speed
Accelerate, then can be seen that the path after greedy algorithm smoothing processing more levels off to real navigation situation and drawn by Fig. 5 and Fig. 8
Optimal path.
For the path optimization of underwater robot, the present invention proposes a kind of Smooth-RRT algorithms.By add convergence because
Son and angular factors, improve the search efficiency of algorithm；The path after improvement is smoothed using greedy algorithm, gone
Except the unnecessary point in path planning, path planning distance is shortened.Learnt by the simulation experiment result, caused by this paper algorithms
Path is more smooth smooth, while significantly reduces the steering number of underwater robot, so as to be better adapted to water
The control system requirement of lower robot.
Claims (2)
1. it is an object of the invention to provide a kind of underwater robot method for optimizing route based on Smooth-RRT algorithms, it is special
Sign is, comprises the steps of：
Step 1：Initialize T1=xinit, xinitFor initial position；
Step 2：Judge | xinit-xgoal|≤ρ, if so, going to step 11, it is not, then goes to step 3；xgoalFor target position
Put；
Step 3：Generate random point xrand；
Step 4：Given one 0 to 1 bias variable Bias, the random number rand that generation is one 0 to 1；
Step 5：If rand<Bias, then xrand=xgoal；Otherwise, xrandIt is constant；
Step 6：Find out xnearMake D (xnew,xrand)≤D(x,xrand)；xnearFor distance xrandNearest point；xnewFor extension
New exploration node；
Step 7：According to the father node x of current locationnear-1, current location xnearAnd xrandObtain the θ in outgoing direction knots modification；If θ
＜ θmaxThen perform xnear+ ρ θ, otherwise perform (xrand-xnear)·θmax；ρ is fixed step size；θmaxIt is used for about for hard-over
The scope of point is explored in the limitation of beam condition；θ is direction knots modification；
Step 8：In xnewAnd xrandLine on seek xnewMake D (xnew,xnear)=ρ, and xnew∈CfreeIf as existing
xnew, step 9 is gone to, if being not present, goes to step 3；
Step 9：Increase node, T on expansion treek+1=Tk+xnew；
Step 10：Judge | xnew-xgoal|≤ρ, if so, going to step 11, it is not, then goes to step 3；
Step 11：Path is smoothed using greedy algorithm；
Step 12：Terminate, obtain the path after optimization.
2. a kind of underwater robot method for optimizing route based on Smooth-RRT algorithms according to claim 1, its institute
The greedy algorithm stated, it is characterised in that make xnew=x0, connection x is attempted successively1,x2,...xNUntil can not reach first
Node xi, then xi-1It is exactly xnewReachable point, replace x using straight linenewAnd xi-1Between path；Make xtemp=xi-1, weight
Multiple said process, until xnew=xN；Angular factors θ is added in greedy algorithm to take into account the requirement of robot manipulation's performancemax；
Final x0,x1,...xNBetween path be smoothed as some necklace straight lines, make path more fairing.
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