CN110146103A - Consider the unmanned machine paths planning method of target trend and energy recharge - Google Patents
Consider the unmanned machine paths planning method of target trend and energy recharge Download PDFInfo
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Abstract
The present invention discloses the unmanned machine paths planning method of a kind of consideration target trend and energy recharge, path planning field applied to unmanned machine, do not consider the problems of in the prior art unmanned machine midway source energy station carry out energy supplement, the present invention has initially set up the network topological diagram comprising source node and destination node and intermediate energy recharge node, and path constraints are normalized to a little and the weight on side, and it is moved towards according to the energy constraint condition of destination node and Future targets node, calculate the optimal path between source node and destination node, to simplify the path planning process of unmanned machine, reduce the operating cost of unmanned machine.
Description
Technical field
It is the invention belongs to the path planning field of unmanned machine, in particular to a kind of to consider target trend and energy recharge
Unmanned machine Path Planning Technique.
Background technique
Intelligent unattended equipment is the important component of the internet of things era, and its path planning problem is its development need solution
One of critical issue certainly.Unmanned machine needs the road passed through by multiple intermediate nodes when completing assigned tasks
Diameter selection is often diversified, not uniquely, and different paths have it is different execute the time, consumption the energy not yet
Equally, this just need unmanned machine meet system limit requirement and under the conditions of, consider the constraint condition in path, searching is optimal
Path, this is also the mobile premise of smart machine.Such as in rescue and relief work, since the requirement to short time and low time delay is compared
Height, unmanned machine move between each target point, how to be reached using the smallest cost and are rescued target area, and handle in real time
It is problem in need of consideration that data, which pass control centre back,.Therefore, research has also been made to optimization path planning problem in many scholars.
It is found according to existing literature search, in path planning problem, although there is the algorithm of many path plannings,
The weight on movement routine side is only considered mostly, and there is no the weights for considering intermediate node, that is to say, that does not account for nobody
Equipment carries out the energy at midway source energy station and supplements this realistic problem, does not also account for Future targets trend and path in
Intermediate node many particular problems in reality, such as due to road conditions difference, each energy different by the speed of each path
Queuing time and the energy prices difference etc. of tiny node are supplemented, these situations in practice can all influence the road to unmanned machine
Diameter planning, while existing algorithm, there is also model complexity height, algorithm the convergence speed waits many practical problems slowly.
Summary of the invention
In order to solve the above technical problems, the invention proposes a kind of unmanned machine lightweight path rule for considering energy recharge
The method of drawing considers the vehicle mobility model in reality, proposes and a kind of comprehensively considers the speed of vehicle in the process of moving, the energy
Prolongation, the nodes loss factor such as energy prices realize the strategy of optimal mobile scheme.
The technical solution adopted by the present invention are as follows: consider the unmanned machine lightweight paths planning method of energy recharge, comprising:
S1, window sequence is moved towards according to target establish energy recharge node set, constructed according to energy recharge node set
First topological diagram；
S2, target is moved towards into source node in window sequence and all destination nodes extend in the first topological diagram, obtained
Second topological diagram；
S3, right value update is carried out to the second topological diagram；
S4, according to the topological diagram after step S3 right value update, calculate source node to current goal using backstepping algorithm is moved towards
The optimal path of node；
S5, current target node is proceeded to, if current target node is global final goal node, terminated；Otherwise with
Current target node is as new source node, the optimal path of calculating source node to next destination node.
Further, consider that money spends the weight described in step S3 to be updated:
The money flower between this two energy source nodes is updated according to the distance expense unit price between two energy supplemental nodes
Take weight, the source node energy adjacent with this is updated according to the distance expense unit price between source node and adjacent energy supplemental node
Money between supplemental node spends weight, more according to the distance expense unit price between destination node and adjacent energy supplemental node
Money between the fresh target node energy supplemental node adjacent with this spends weight, according to the corresponding equipment energy of unmanned machine
The corresponding energy of maximum capacity, energy supplemental node is monovalent and in the energy that the energy supplemental node requires supplementation with, and updating should
The money of energy supplemental node spends weight.
Further, consider the time spend the weight described in step S3 be updated: according to two energy supplemental nodes it
Between distance by the time update this two can time weight between source node, according to source node and adjacent energy supplemental node
Between distance the time weight between the source node energy supplemental node adjacent with this is updated by the time, according to destination node
Distance between adjacent energy supplemental node is updated between destination node and the adjacent energy supplemental node by the time
Time weight, according to the corresponding energy maximum capacity of unmanned machine, the supplement energy source speed of energy supplemental node and in the energy
The energy that source supplemental node requires supplementation with updates the time weight of the energy supplemental node.
Further, comprehensively consider money cost and spend the weight described in step S3 to be updated with the time:
Define RMWeight coefficient is spent for money, defines RTWeight coefficient, and R are spent for the timeM+RT=1；
Weight, time is spent to spend weight, R according to the money between two energy supplemental nodesM、RTUpdate two energy
Weight between source node, according between source node and adjacent energy supplemental node money spend weight, the time spend weight,
RM、RTThe weight between the source node energy supplemental node adjacent with this is updated, according to destination node and adjacent energy supplemental node
Between money spend weight, the time spend weight, RM、RTIt updates between the destination node energy supplemental node adjacent with this
Weight spends weight, time to spend weight, R according to the money of energy supplemental nodeM、RTUpdate the power of the energy supplemental node
Value.
Further, the step S1 include it is following step by step:
S11, window sequence is moved towards according to target establish energy recharge node set；
If the distance between two energy recharge nodes in S12, step S11 set, which are less than unmanned machine, expires energy movement
Then topological diagram, the power of this directed edge is added using the line between two energy recharge nodes as a directed edge in distance
Weight is the distance between two energy recharge nodes, traverses the combination of two of all energy recharge nodes, obtains final the
One topological diagram.
Further, step S2 includes:
S21, source node is added to the first topological diagram, specifically: if the distance of source node to some energy source node is less than
The maximum distance that unmanned machine can advance in the remaining energy of source node, then by source node between the energy recharge node
Current topological diagram is added as a directed edge in line, the weight of this directed edge be source node to the energy recharge node it
Between distance；
S22, it target is moved towards into the destination node in window sequence is added to the first topological diagram, specifically: if some energy
The distance of node to destination node, which is less than unmanned machine, expires energy moving distance and destination node to nearest energy recharge node
Apart from its difference, then current topology is added to using the energy recharge node to the line between destination node as a directed edge
Figure, the weight of this directed edge are the distance between energy recharge node to destination node；
S23, according to step S21 and step S22, obtain the second topological diagram.
Further, backstepping algorithm is moved towards described in step S4 calculates optimal path to next destination node, including it is following
Step by step:
A1, the energy that target moves towards reservation needed for current target node to next-hop in window sequence is calculated；
A2, the energy of the reservation according to needed for current target node, step S3 the second topological diagram, using dijkstra's algorithm
Calculate a destination node to current target node optimal path；
Or the second topological diagram of the energy of the reservation according to needed for current target node, step S3, using dijkstra's algorithm
Calculate source node to current target node optimal path.
Further, step A1 specifically:
If current target node is that target moves towards the last one destination node in window sequence, apart from the target
Next-hop of the nearest energy recharge node of node as current target node, so that current target node be calculated to next
The energy retained needed for jumping；
Otherwise the next-hop of current target node is obtained according to the optimal path of the destination node to next destination node, from
And the energy retained needed for current target node to next-hop is calculated.
Further, using current target node as new source node described in step S5, source node is calculated to next mesh
Mark node optimal path, including it is following step by step:
B1, using current target node as new source node, more fresh target moves towards window sequence；
Whether the first current topological diagram of B2, judgement has covered updated target and has moved towards the corresponding energy benefit of window sequence
To node set, the return step S2 if having covered updates the second current topological diagram；Otherwise return step S1, according to update
Target afterwards moves towards window sequence, updates the first current topological diagram.
Beneficial effects of the present invention: in the present invention, network topological diagram has been established in advance, and according to needs in reality
The road conditions of consideration supplement the factors such as energy time and price, and consider different task to the side of different weight factors
Weight carries out the combined optimization of time and cost, establishes normalized weight network；Unmanned machine obtains in advance before execution task
The information for having taken weight network goes out the optimal path of execution task using destination node energy constraint conditional plan；This hair simultaneously
The bright unmanned machine paths planning method that also proposed a kind of consideration target trend and energy recharge, method of the invention are more applicable
In actual unmanned machine path planning, and optimal path planning scheme can be obtained, and reduce the operation of unmanned machine
Cost.
Detailed description of the invention
Fig. 1 is the unmanned machine lightweight paths planning method flow chart that the present invention considers energy recharge.
Fig. 2 is the unmanned machine lightweight paths planning method schematic diagram of a scenario that the present invention considers energy recharge.
Fig. 3 is the unmanned machine lightweight paths planning method energy supplemental node network that the present invention considers energy recharge
Figure.
Fig. 4 is the unmanned machine lightweight paths planning method illustration figure that the present invention considers energy recharge.
Specific embodiment
For convenient for those skilled in the art understand that technology contents of the invention, with reference to the accompanying drawing to the content of present invention into one
Step is illustrated.
In the present invention, pass through process of multiple intermediate energy recharge nodes from source node to destination node in unmanned machine
In, by considering the path planning of multifactor combined optimization, determine a series of optimal movement of the unmanned machine when executing tasks
Path.
As shown in Figure 1 be flow chart of the method for the present invention, it is known that the coordinate map of all energy recharge nodes and path with
Complementary energy cost is illustrated in figure 2 schematic diagram of a scenario provided in this embodiment, including source node qs, multiple destination node (Q=< q1,
q2,…,qn>) and several energy recharge node (P=[P1,P2,…,Pm]).It asks: current vehicle position s=q0Lead to a system
Column target point (Q=< q1,q2,…,qn>) optimal path；Realization process of the invention are as follows:
Step 1: being moved towards according to unmanned machine, construct energy recharge node topology figure G.
Step 1.1: being based on map, discovery and the energy recharge node near unmanned machine target trend, constitute the energy and mend
Give node set (P=[P1,P2,…,Pm]).Specific implementation step is as follows:
Step 1.2.1: unmanned machine target trend is expressed as W={ < q by source node and destination node sequencei,qi+1>,i
=s ... s+w-1 }, indicate that unmanned machine will be by source node qsPlace sets out, i.e., will pass through qs+1,qs+2,…,qs+wEqual destination nodes, w
It is the considered destination node maximum number of current calculating, can be pre-configured with or adaptively adjust, w value is in the present embodiment
3, i.e. target is moved towards to include at least a source node and two destination nodes in window sequence.
Step 1.2.2: setting P is empty set, checks respectively for target and moves towards sequence, will move towards all complementary energy sections near sequence
Point is added to P.Specific practice is, with < qi,qi+1> it is that diameter defines border circular areas on map, the border circular areas will be located at
Interior all complementary energy nodes are added in P, i=s ... s+w-1.
Step 1.2.3: by the complementary energy node in P, number be (P (1),, P (m)).
Step 1.2: to energy recharge node set (P (1),, P (m)), m indicate energy recharge node sum, two-by-two
Path distance is checked, if there is distance between P (i) and P (j) is less than the path that equipment expires energy moving distance L, by P (i) P (j)
G is added as a directed edge.Specific implementation step is as follows:
Step 1.2.1: P (1) and P (2), P (3) ... the distance of P (m), it is assumed that unmanned machine can when expiring the energy are calculated separately
It is L with mobile maximum distance, if d(P(1),P(j))< L just claims P (1) P (j) neighbours' point each other, then opens up P (1) P (j) addition
The weight flutterred in figure G, and assign its side is d(P(1),P(j))；
Step 1.2.2: P (2) and P (1) P (3) ... is calculated separately at a distance from P (m), it is assumed that can be with when unmanned machine expires the energy
Mobile maximum distance is L, if d(P(2),P(j))P (2) P (j) is then added the side topological diagram G and concentrated, and assigns its side by < L
Weight is d(P(2),P(j)).And so on, qualified each node is added in network topological diagram G.Its schematic diagram such as Fig. 3
It is shown.
Step 2: the node moved towards in sequence W being added in network topological diagram, the topological diagram G ' of expansion is generated.
Topological diagram G is replicated a as expansion topological diagram G ', G '=G and increases the node into sequence W on G '.
Step 2.1: by first node in sequence W, i.e. source node qs, it is added to topological diagram
If from qsThere is distance d to P (i)(qs,P(i))Path less than Ls, then by (qs, P (i)) and it is added as directed edge
G'.Here, Ls is vehicle in qsThe maximum distance that can advance of the remaining energy.
Step 2.2: by remaining destination node { q in sequence Wi, wherein i=s+1 ..., s+w }, it is added to expansion topological diagram
G’
If from P (j) to qiIn the presence of distance d(P(j),qi)Path less than L-L (r), then by (P (j), qi) add as directed edge
Enter G '.Here L (r) is from qiThe minimum distance of energy website is filled to nearby.So far the foundation of whole network topological diagram is completed.Its
Schematic diagram is as shown in Figure 4.
Step 3: calculating weight on expanding topological diagram G '.
Opposite side collection and point centralized value are updated, and complete the foundation of weight network: the weight for assigning side collection before only depends on
Length in path, however under practical environment, unmanned machine need to consider various composite factors, for example, road conditions with
And it is lined up the time of the supplement energy, the road conditions and toll of each paths are all different, the benefit of each energy supplemental node
It fills energy source time and price is also had any different, this just needs us to assign their weight again according to the actual situation.Weight computing
Method is divided into following 3 class situation, can configure:
Situation 1. carries out weight imparting in terms of money cost: since the road conditions of every section of distance are different, required for them
Toll unit price it is also different, it is assumed that travelling expenses unit price between every two energy supplemental node P (i) and P (j) is Mp(i, j),
Then the toll that this section of distance needs is Mp(i,j)*d(P(i),P(j)), this value is assigned to side P (i) P (j) as it in money
The weight for spending aspect, is denoted as WM(i, j), and the note that weight between source node and energy supplemental node adjacent thereto is similar
Make WM(qs, i), the weight between destination node node adjacent thereto is denoted as WM(j,qi).The energy list of each energy supplemental node
Valence is not also identical, is set as Mp(i), i is the number of each energy supplemental node.Assuming that supplement energy can be full of every time, and
Equipment energy maximum capacity is Fmax, since the energy consumed between P (i) and P (j) is (d(P(i),P(j))*Fmax)/L, then
The energy that i energy supplemental node requires supplementation with is L- (d(P(i),P(j))*Fmax)/L, the cost for supplementing energy needs is [L-
(d(P(i),P(j))*Fmax)/L]*Mp(i), this node is assigned as its weight in terms of money cost using this value, be denoted as WM
(i).It is obvious that the weight of each edge is fixed, but the right value update of each energy supplemental node is dynamically, this depends on
Before it the case where a line, it is also dependent upon the energy prices of each energy supplemental node.So far it completes and is spending cost side
In face of the right value update of each edge and each energy supplemental node.
Situation 2. carries out weight imparting in terms of time loss: due to every section of distance the case where is different, what they passed through
Speed is also different, also different by their time caused by this.Assuming that P (i) P (j) distance speed be desired for V (i,
J), then being d by the time required for this edge(P(i),P(j))This value is denoted as W by/V (i, j)T(i, j), and it is assigned
This edge is given as weight.And weight between source node and energy supplemental node adjacent thereto it is similar be denoted as WT(qs, i),
Weight between destination node node adjacent thereto is denoted as WT(qi,j).The supplement energy source speed of each energy supplemental node is not yet
It is identical, it is set as Vp(i), i is the number of each energy supplemental node.Assuming that supplement energy can be full of every time, and equipment energy
Source maximum capacity is Fmax, since the energy consumed between P (i) and P (j) is (d(P(i),P(j))*Fmax)/L, then in i-th of energy
The energy that source supplemental node requires supplementation with is L- (d(P(i),P(j))*Fmax)/L, the time for supplementing energy needs is [L-
(d(P(i),P(j))*Fmax)/L]/Vp(i), it is contemplated that the crowding of each energy supplemental node is different, it is assumed that the queuing of node P (i)
Waiting time is desired for T (i), is then [L- (d in the total time that this node is spent(P(i),P(j))*Fmax)/L]/Vp(i)+T
(i), this node is assigned as its weight in terms of time loss using this value, be denoted as WT(i).It is obvious that the power of each edge
The case where value is fixed, but the right value update of each energy supplemental node is dynamically, this depends on its preceding a line, it is preceding
A line is shorter (father's side right value smaller), then the weight of this point is bigger, and preceding a line is longer (father's side right value is bigger), then this
The weight of point is smaller.Its weight is also dependent upon the queuing time expectation of each energy supplemental node.So far it completes in time flower
Take aspect to the right value update of each edge and each energy supplemental node.
The combination weights that situation 3. spends money and the time spends: in different tasks, to money cost and time
The degree that stresses of consumption is different.Such as in the anti-dangerous disaster relief, the requirement that task spends the time with regard to relatively high, but
During transporting cargo, the requirement that task spends money is with regard to relatively high.In view of this consideration, R is definedMIt spends and weighs for money
Value defines RTWeight, and R are spent for the timeM+RT=1, the two values are freely defined according to task needs.So each edge P
(i) total weight value of P (j) is Wall(i, j)=RM*WM(i,j)+RT*WT(i, j), equally, each energy supplemental node P (i) it is total
Weight is Wall(i)=RM*WM(i)+RT*WT(i).Distinguishingly, it is assumed that node P (I1+ 1) destination node q is representedi, its own weight
It is 0.So far it completes a little and step is weighed in the tax on side.
Step 4. calculates optimal path using backstepping algorithm is moved towards
Consider the optimization path algorithm of node weight function and target trend, basic ideas are inverse along sequence W is moved towards
To calculating, calculate separately from qi-1To qiShortest path, i finally acquires from s+w to s+1 from qsGo to qs+1Optimal path.
It is as follows to move towards backstepping algorithm calculating pseudocode:
Step 4.1: being calculated on G ' from q using dijkstra's algorithmi-1To qiShortest path.Dijkstra's algorithm is
The well-known algorithm of computer field, the present invention are not set forth in detail herein.
Step 4.2: once find out qi-1To qiOptimal path, unmanned machine is in qs+w-1The energy for needing to retain determines that
, update unmanned machine qi-1The energy retained, i.e. Remainc (q are needed at placei-1)=L-dist (qi-1；vi-1(next))。
vi-1It (next) is qi-1To qiOptimal path on next-hop, it may be possible to destination node or complementary energy website.
5. recalculating optimal path after proceeding to next target point
When along optimal path arrival targeted sites qs+1When, judge whether to have arrived at ideal website.If having arrived at most
Target point afterwards, then algorithm terminates.
Otherwise, current location is updated to by q by s=s+1s, more Basel Ⅱ Accord sequence W is included in new destination node, if opening up
It flutters figure G and has covered the location W, then go to step 2, otherwise, go to step 1, update topological diagram G.
Below by a specific embodiment, the present invention is further elaborated.
As shown in figure 4, given source node and destination node and energy supplemental node, and net is established according to step 1
Network topological diagram, and each parameter of side and node has marked on the diagram.Assuming that unmanned machine expires energy capacity Fmax=20,
Maximum operating range L=100, the weight R that the time spends and money is spentM=0.1, RT=0.9, then will be made below source section
Point arc to destination node qiThe planning of optimal path takes into consideration only the feelings up to a destination node for convenience of description here
Condition.Here arc represents source node qs, dst is destination node qi。
The array d [] that length is 4 is defined first, and element d [i-1] indicates the shortest distance of source point arc to node P (i).
Define the array f [] that length is 4.Its element f [i-1] is if it is 1, then it represents that the most short of source point arc to P (i) has been found
Path, element f [i-1] is if it is 0, then it represents that there are no the shortest paths for finding source point arc to P (i).To each node P
(i) the array p that a length is 5 is definedi[], the storage node serial number that (i) successively passes through from source point to P, i.e. shortest path.
Calculate the weight with source node adjacent node and side.Consider arc to P (1) first, in terms of money cost, this section
The toll that distance needs is WM(arc, 1)=Mp(arc,1)*d(arc,1)=3*50=150.Money at P (1), which is spent, is
WM(1)=[(d(arc,P(1))*Fmax)/L]*Mp(1)=50.Consider arc to P (1), in terms of time cost, this section of distance needs
Time be WT(arc, 1)=d(arc,P(1))/ V (arc, 1)=1.67.It is W that time at P (1), which spends,T(1)=
[(d(arc,P(1))*Fmax)/L]/Vp(1) (1)=2+T.Then side arcP (1) total weight value Wall(arc, 1)=RM*WM(arc,1)+
RT*WT(arc, 1)=16.5, the total weight value W of point P (1)all(1)=RM*WM(1)+RT*WT(1)=6.8.Then arc directly arrives P
(1) path total weight value is Wall(arc,1)+Wall(1)=16.5+6.8=23.3.
Then consider arc to P (3), in terms of money cost, the toll that this section of distance needs is WM(arc, 3)=Mp
(arc,3)*d(arc,3)=40*0.4=16.It is W that money at P (3), which is spent,M(3)=[(d(arc,P(3))*Fmax)/L]*Mp(3)
=32.Consider arc to P (3), in terms of time cost, the time that this section of distance needs is WT(arc, 3)=d(arc,P(3))/V
(arc, 3)=2.It is W that time at P (3), which spends,T(3)=[(d(arc,P(3))*Fmax)/L]/Vp(3) (3)=1+T.Then side
ArcP (3) total weight value Wall(arc, 3)=RM*WM(arc,3)+RT*WT(arc, 3)=3.4, the total weight value W of point P (3)all(3)=
RM*WM(3)+RT*WT(3)=4.1.Then it is W that arc, which directly arrives the path total weight value of P (3),all(arc,3)+Wall(3)=3.4+
4.1=7.5.
Update each array: by d [0] and d [2] be updated to 23.3 and 7.5, other elements ∞ respectively.Due to d [0] > d
[2], then P (3) is the nearest point of current distance source point, is updated array f [2]=1, other elements 0, and update array p3
[0]=0, p3[1]=3, p1[0]=0, p1[1]=1 it have now been completed the operation of first round searching minimal path.
With P (3) for intermediate path, continue next round operation.Consider that P (3) arrive P (1), in terms of money cost, this
The toll that Duan Lucheng needs is WM(3,1)=Mp(3,1)*d(p(3),P(1))=0.6*40=24.Money at P (1) is spent
For WM(1)=(d(p(3),P(1))*Fmax)/L*Mp(1)=40.Consider that P (3) arrive P (1), in terms of time cost, this section of distance is needed
The time wanted is WT(3,1)=d(p(3),P(1))/ V (3,1)=4.It is W that time at P (1), which spends,T(1)=[(d(p(3),P(1))*
Fmax)/L]/Vp(1) (1)=1.8+T.Then side P (3) P (1) total weight value Wall(3,1)=RM*WM(3,1)+RT*WT(3,1)=6,
The total weight value W of point P (1)all(1)=RM*WM(1)+RT*WT(1)=5.6.Then P (3) directly arrives the path total weight value of P (1) and is
Wall(3,1)+Wall(1)=6+5.6=11.6.
Consider that P (3) arrive P (2), in terms of money cost, the toll that this section of distance needs is WM(3,2)=Mp(3,2)*
d(p(3),P(2))=1*90=90.It is W that money at P (2), which is spent,M(2)=(d(p(3),P(2))*Fmax)/L*Mp(2)=108.It examines
Consider P (3) and arrive P (2), in terms of time cost, the time that this section of distance needs is WT(3,2)=d(p(3),P(2))/ V (3,2)=
4.5.It is W that time at P (2), which spends,T(2)=[(d(p(3),P(2))*Fmax)/L]/Vp(2) (2)=2.8+T.Then side P (3) P
(2) total weight value Wall(3,2)=RM*WM(3,2)+RT*WT(3,2)=13, the total weight value W of point P (2)all(2)=RM*WM(2)+RT*
WT(2)=13.3.Then it is W that P (3), which directly arrives the path total weight value of P (2),all(3,2)+Wall(2)=13+13.3=26.3.
Update each array: by d [0]=23.3 > d [2]+Wall(3,1)+Wall(1)=19.1, thus update d [0]=
19.1, update d [1]=d [2]+Wall(3,2)+Wall(2)=33.8, other elements are constant.Due to d [0] < d [1], then (1) P
It is the nearest point of current distance source point, updates array f [0]=1, other elements 0.Because d [0] has update, therefore updates array
p1[]=p3[], and p1[2]=1.P at this time1[]=[0,3,1,0,0].It has now been completed the second wheel and find minimal path
Operation.
Process as above is copied, until f [3]=1, p at this time4[]=[0,3,1,2,4], optimal path i.e. arc, P
(3)、P(1)、P(2)、qi, as shown in Figure 4.Its path total weight value is stored in array d [3].
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.For ability
For the technical staff in domain, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made
Any modification, equivalent substitution, improvement and etc. should be included within scope of the presently claimed invention.
Claims (9)
1. considering the unmanned machine paths planning method of target trend and energy recharge characterized by comprising
S1, window sequence is moved towards according to target establish energy recharge node set, according to energy recharge node set building first
Topological diagram；
S2, target is moved towards into source node in window sequence and all destination nodes extend in the first topological diagram, obtains second
Topological diagram；
S3, right value update is carried out to the second topological diagram；
S4, according to the topological diagram after step S3 right value update, calculate source node to current target node using backstepping algorithm is moved towards
Optimal path；
S5, current target node is proceeded to, if current target node is global final goal node, terminated；Otherwise with current
Destination node is as new source node, the optimal path of calculating source node to next destination node.
2. the unmanned machine paths planning method according to claim 1 for considering target trend and energy recharge, feature
It is, considers that money spends the weight described in step S3 to be updated:
The money between this two energy source nodes, which is updated, according to the distance expense unit price between two energy supplemental nodes spends power
Value updates the source node energy adjacent with this according to the distance expense unit price between source node and adjacent energy supplemental node and supplements
Money between node spends weight, updates mesh according to the distance expense unit price between destination node and adjacent energy supplemental node
The money marked between the node energy supplemental node adjacent with this spends weight, maximum according to the corresponding equipment energy of unmanned machine
The corresponding energy of capacity, energy supplemental node is monovalent and in the energy that the energy supplemental node requires supplementation with, and updates the energy
The money of supplemental node spends weight.
3. the unmanned machine paths planning method according to claim 1 for considering target trend and energy recharge, feature
It is, considers that the time spends the weight described in step S3 to be updated: being passed through according to the distance between two energy supplemental nodes
Time updates the time weight between this two energy source nodes, logical according to the distance between source node and adjacent energy supplemental node
The time weight between the time update source node energy supplemental node adjacent with this is crossed, is mended according to destination node and the adjacent energy
It fills the distance between node and time weight between the destination node energy supplemental node adjacent with this is updated by the time, according to
The corresponding energy maximum capacity of unmanned machine, energy supplemental node supplement energy source speed and the energy supplemental node need
The energy of supplement updates the time weight of the energy supplemental node.
4. special according to the unmanned machine paths planning method of the trend and energy recharge of consideration target described in claim 2,3
Sign is, comprehensively considers money cost and spends the weight described in step S3 to be updated with the time:
Define RMWeight coefficient is spent for money, defines RTWeight coefficient, and R are spent for the timeM+RT=1；
Weight, time is spent to spend weight, R according to the money between two energy supplemental nodesM、RTUpdate two energy sections
Weight between point spends weight, time to spend weight, R according to the money between source node and adjacent energy supplemental nodeM、RT
The weight between the source node energy supplemental node adjacent with this is updated, according between destination node and adjacent energy supplemental node
Money spend weight, the time spend weight, RM、RTThe weight between the destination node energy supplemental node adjacent with this is updated,
Weight, time is spent to spend weight, R according to the money of energy supplemental nodeM、RTUpdate the weight of the energy supplemental node.
5. the unmanned machine paths planning method according to claim 1 for considering target trend and energy recharge, feature
Be, the step S1 include it is following step by step:
S11, window sequence is moved towards according to target establish energy recharge node set；
If the distance between two energy recharge nodes in S12, step S11 set be less than unmanned machine expire the energy move away from
From then using the line between two energy recharge nodes as a directed edge addition topological diagram, the weight of this directed edge
For the distance between two energy recharge nodes, the combination of two of all energy recharge nodes is traversed, final first is obtained
Topological diagram.
6. the unmanned machine paths planning method according to claim 5 for considering target trend and energy recharge, feature
It is, step S2 includes:
S21, source node is added to the first topological diagram, specifically: if the distance of source node to some energy source node is less than nobody
The maximum distance that equipment can advance in the remaining energy of source node, then by source node to the line between the energy recharge node
Current topological diagram is added as a directed edge, the weight of this directed edge is source node between the energy recharge node
Distance；
S22, the destination node moved towards in window is added to the first topological diagram, specifically: if some can source node to target section
The distance of point, which is less than unmanned machine, expires the difference at a distance from energy moving distance and destination node to nearest energy recharge node, then will
The energy recharge node is added to current topological diagram, this directed edge as a directed edge to the line between destination node
Weight be the energy recharge node the distance between to destination node；
S23, according to step S21 and step S22, obtain the second topological diagram.
7. the unmanned machine paths planning method according to claim 6 for considering target trend and energy recharge, feature
Be, moved towards described in step S4 backstepping algorithm calculate to next destination node optimal path, including it is following step by step:
A1, the energy that target moves towards reservation needed for current target node to next-hop in window sequence is calculated；
A2, the energy of the reservation according to needed for current target node, step S3 the second topological diagram, calculated using dijkstra's algorithm
Optimal path of the upper destination node to current target node out；
Or the second topological diagram of the energy of the reservation according to needed for current target node, step S3, it is calculated using dijkstra's algorithm
Optimal path of the source node to current target node out.
8. the unmanned machine paths planning method according to claim 7 for considering target trend and energy recharge, feature
It is, step A1 specifically:
If current target node is that target moves towards the last one destination node in window sequence, apart from the destination node
Next-hop of the nearest energy recharge node as current target node, so that current target node be calculated to next-hop institute
The energy that need to retain；
Otherwise the next-hop of current target node is obtained according to the optimal path of the destination node to next destination node, to count
Calculate the energy retained needed for obtaining current target node to next-hop.
9. the unmanned machine paths planning method according to claim 8 for considering target trend and energy recharge, feature
It is, using current target node as new source node described in step S5, calculates source node to the best of next destination node
Path, including it is following step by step:
B1, using current target node as new source node, more fresh target moves towards window sequence；
Whether the first current topological diagram of B2, judgement, which has covered updated target, is moved towards the corresponding energy recharge section of window sequence
Point set, the return step S2 if having covered update the second current topological diagram；Otherwise return step S1, according to updated
Target moves towards window sequence, updates the first current topological diagram.
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