CN102679982A - Route planning method for autonomous underwater vehicle aiming at undetermined mission time - Google Patents
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Abstract
The invention relates to a route planning method for an autonomous underwater vehicle aiming at undetermined mission time. The route planning method is characterized by comprising the following steps of: encoding a route planning problem to obtain a population with a scale of N; estimating population fitness and selecting N good individuals from the current population; carrying out interlace operation on the good individuals at a probability of pc; carrying out mutation operation on the interlaced good individuals at a probability of pm; carrying out an elicitation type mutation operation to obtain an optimal individual; and decoding the optimal individual according to an encoding rule in the step 1, so as to obtain a planned route of each AUV (Autonomous Underwater Vehicle). The route planning method for the autonomous underwater vehicle aiming at the undetermined mission time provided by the invention can be used for planning the routes of a plurality of AUVs, so as to realize cooperative route plane of a multi-AUV system, and ensure the global optimum of a planning result. Furthermore, the route planning method has the characteristics that use is convenient and generality is strong, and can be applied to the route plane of a robot and the like.
Description
Technical field
The invention belongs to AUV Task Distribution and routeing field, relate to a kind of autonomous submarine navigation device routeing method towards uncertain task time.
Background technology
AUV need carry out tasks such as observation usually in a big way, these task points often can only be learnt through prior estimation required task time.Therefore; When carrying out the multiple spot task, must in planning ROV arrival task point successively sequence, consider the uncertainty of each task point required time; And receive the maximum operating time of ROV to retrain making a return voyage and the duration of charging of being caused, make the required time of whole task process minimum.
In recent years, the routeing research field at AUV has obtained some achievements in research, but for the research of many way points of the AUV routeing with uncertain task time, research is both at home and abroad found.
Summary of the invention
For fear of the weak point of prior art, the present invention proposes a kind of autonomous submarine navigation device routeing method towards uncertain task time, to the heuristic genetic manipulation of routeing problem, to improve routeing result's performance.
A kind of autonomous submarine navigation device routeing method towards uncertain task time is characterized in that step is following:
Step 1: to the routeing problem scale that obtains of encoding is the population of N; Described coded system is: the coding of each feasible solution of routeing problem is by representing for a plurality of sub-coded sequences; Each sub-coded sequence is represented the waypoint sequence of 1 AUV, and the required AUV quantity of all way points has been navigated by water in the representative of the quantity of sub-coded sequence; The production method of said sub-coded sequence: produce N at random
VIndividual [1, N
V] positive integer, as the way point quantity of the required navigation of each ROV; Said N
VFor participating in the AUV quantity of task;
Wherein:
(i=1 ... N
V), L is the length in air route, V is the speed of ROV;
If when having only an air route in the individuality then carry out air route exchange with the best air route in a fragment of selecting at random in this air route and another individuality; Concrete exchanged form is: new way point D inserts before or after its nearest way point; New way point D will be inserted among the former air route A-B-C-A; The point nearest apart from D is B, obtains inserting result: A-D-B-C-A or A-B-D-C-A; If Dis (A, D)+Dis (B, C) greater than Dis (A, B)+(D C) then selects the latter, otherwise selects the former, wherein Dis (C Dis
1, C
2) represent C
1And C
2Between distance;
Step 5: to the defect individual after intersecting with Probability p
mCarry out mutation operation, carry out heuristic mutation operation with following any one mode:
1) heuristic local exchange: two air routes of each exchange are confirmed at random; A fragment of selecting at random in each air route exchanged in another air route go; According to the INM method each point in this fragment is inserted in another air route, guarantees simultaneously in each heuristic local exchange process, between identical two air routes swap operation can not take place twice;
2) the shortest heuristic air route merges: in individuality, seek two air routes the shortest, and will be wherein one be inserted in another air route with the INM method, with the two merging;
3) the longest heuristic air route splits: the air route that cost in the individuality is maximum splits into two air routes from a random point, and insert in the empty air route with the INM method in the air route that newly splits out;
Step 6: circulation step 2～5N
tInferior, obtain optimum individual;
Step 7: optimum individual is decoded according to the coding rule in the step 1, obtain the planning air route of each AUV.
A kind of autonomous submarine navigation device routeing method that the present invention proposes towards uncertain task time; Can be used for the air route of a plurality of AUV is planned; Realize the collaborative routeing of many AUV system; Can guarantee global optimum's property of program results, and have easy to use, the characteristics of highly versatile; Also can be applicable to routeing to robot, unmanned vehicle etc.
Description of drawings
Fig. 1: the synoptic diagram of coded system of the present invention;
Fig. 2: the synoptic diagram of employed insertion closest approach (INM) method in the heuristic genetic manipulation of the present invention;
Fig. 3: the false code of feasible solution evaluation algorithms of the present invention
Embodiment
Combine embodiment, accompanying drawing that the present invention is further described at present:
The performing step of the embodiment of the invention is:
1. the routeing problem is encoded
The genetic coding mode is as shown in Figure 1.The coding of each feasible solution can be by representing that for a plurality of sub-coded sequences each sub-coded sequence is represented the waypoint sequence of 1 AUV, and the required AUV quantity of all way points has been navigated by water in the quantity representative of sub-coded sequence.
2. generation initial population
For scale is the population of N, and each individuality all will produce at random.Supposing has N
VIndividual AUV fellowship task at first, produces N at random
VIndividual [1, N
V] positive integer, as the way point quantity of the required navigation of each ROV.Then, confirm the concrete numerical value of each way point, and guarantee not take place way point and repeat.
3. population fitness assessment
The process of calculating each ideal adaptation degree in the population is exactly to calculate the process of the air route cost of a certain AUV.Owing to the limited working time of AUV, often need AUV abort and charging.That is to say that each AUV executes the task according to the waypoint sequence of assigning in advance, need determine whether to continue next target is executed the task or maked a return voyage and charge at each task point AUV.When execution is maked a return voyage, Departure times on reverse, duration of charging d
FuelAnd the time of returning way point also is comprised in the cost of air route.
Because be not pre-determined the required task time at each way point place, only can be provided by prior probability distribution.Therefore, the present invention proposes the cost that an air route is estimated in a kind of air route simulation (RSM).Its concrete grammar is: to the cost double counting N of every air route sequence
RSMInferior, the concrete observation time of each way point is generated by its probability distribution at random when calculating at every turn, uses this N then
RSMThe expectation value of inferior result of calculation is as the estimation cost in this air route.Can obtain this individual fitness estimated value after the air route of all AUV in forming into columns estimated.The evaluation algorithms of ideal adaptation degree is seen Fig. 3.
Concrete calculating: to the cost f of every air route sequence
iDouble counting N
RSMInferior, the concrete observation time of each way point is generated by its probability distribution at random when calculating at every turn, uses this N then
RSMThe expectation value of inferior result of calculation is as the estimation cost in this air route; Can obtain this individual fitness estimated value after the air route of all AUV in forming into columns estimated
Wherein:
(i=1 ... N
V), L is the length in air route, V is the speed of ROV;
4. from current population, select defect individual
Proportionally back-and-forth method is selected N defect individual from current population.Each individual selected probability equals the ratio of its fitness and population ideal adaptation degree sum in the current population, produces the random number between [0,1] at random, if this number is then chosen this individuality greater than ratio, otherwise does not select.
5. defect individual is carried out interlace operation
In the intersection step, from defect individual, select two individuals at random according to crossover probability p
cCarry out interlace operation.Therefrom select the minimum air route of two costs at first respectively, then two air routes are exchanged and as their article one air route.When having only an air route in the individuality, then carry out the air route exchange with a fragment of selecting at random in this air route and the best air route in another individuality.After the air route exchange is accomplished, need delete the way point that repeats in the individuality.The deletion here is meant deletes the point on the air route, individual Central Plains, and new air route of inserting is constant.Heuristic genetic manipulation is embodied in a kind of insertion closest approach method (INM), and its concrete implication is: new way point should insert before or after its nearest way point, promptly at the order of a new way point in the air route by apart from its nearest way point decision.As shown in Figure 2, new way point D will be inserted among the former air route A-B-C-A, be B apart from the nearest point of D.Therefore have following two kinds to insert result: A-D-B-C-A and A-B-D-C-A, finally which kind of result is accepted then to be decided by these two kinds of results' air route cost.If Dis is (C
1, C
2) represent C
1And C
2Between distance, if Dis (A, D)+Dis (B, C) greater than Dis (A, B)+(D C) then selects the latter, otherwise selects the former Dis.
6. the defect individual after intersecting is carried out mutation operation
In the variation step, each all will be according to the variation Probability p through the defect individual after intersecting
mCarry out mutation operation.
Variation step of the present invention comprises following three kinds of heuristic mutation operations:
1) heuristic local exchange: this operation comprises some swap operations, and two air routes of each exchange are confirmed at random.A fragment of selecting at random in each air route exchanged in another air route go; According to the INM method each point in this fragment is inserted in another air route, guarantees simultaneously in each heuristic local exchange process, between identical two air routes swap operation can not take place twice.
2) the shortest heuristic air route merges: this operates in and seeks two air routes the shortest in the individuality, and will be wherein one be inserted in another air route with the INM method, with the two merging.
3) the longest heuristic air route splits: this operation air route that cost in the individuality is maximum splits into two air routes from a random point, and insert in the empty air route with the INM method in the air route that newly splits out.
Need to prove that algorithm is that mutation operation has no preference for these three kinds of inspirations, a kind of method of picked at random makes a variation when certain makes a variation.
7. end condition is judged
If step 3～5 are performed discontented N
tInferior, continue execution in step 3～6; Otherwise the routeing process finishes, and obtains optimum individual.
8. decoding output
Coding rule according in the step 1 is decoded, and obtains the planning air route of each AUV.
The genetic algorithm tool box (Genetic Algorithm Tool) that present embodiment utilizes
to be provided; Present embodiment experimentizes to 9 Solomon series standard test problems in vehicle routeing field, and they are respectively: Solomon_25_R, Solomon_25_C, Solomon_25_RC, Solomon_50_R, Solomon_50_C, Solomon_50_RC, Solomon_100_R, Solomon_100_C, Solomon_100_RC.Wherein 25,50,100 represent target quantity; Three kinds of representing way point to distribute respectively of R, C, RC are dissimilar: far away, concentrate, not only far but also concentrate.Basis in former Solomon problem adds a standard deviation in the demand to each client.The customer demand of former problem is regarded as the expectation value of required task time on the way point, and its standard deviation is the zero even distribution to 1/3rd expectation values.The vehicle maximum load of former problem is regarded as the maximum operating time of AUV, the constant airspeed of each AUV, and suppose that the distance of being navigated by water in the unit interval is the unit length on the Solomon problem map, duration of charging d
Fuel=5.
The concrete parameter of routeing method is provided with as follows: N=800, p
c=0.9, p
m=0.4, N
RSM=10, N
t=10000.
Table 1～3 have provided uses and the contrast and experiment of not using two kinds of algorithms of heuristic genetic manipulation of the present invention.Can find out that therefrom the algorithm that this paper proposes is the performance of boosting algorithm significantly, and the performance that algorithm brought that the big more this paper of the scale of problem proposes improves obvious more.
The experimental result that couples of 25 way point Solomnon of table 1. ask
The experimental result of table 2. pair 50 way point Solomnon problems
The experimental result of table 3. pair 100 way point Solomnon problems
Claims (1)
1. autonomous submarine navigation device routeing method towards uncertain task time is characterized in that step is following:
Step 1: to the routeing problem scale that obtains of encoding is the population of N; Described coded system is: the coding of each feasible solution of routeing problem is by representing for a plurality of sub-coded sequences; Each sub-coded sequence is represented the waypoint sequence of 1 AUV, and the required AUV quantity of all way points has been navigated by water in the representative of the quantity of sub-coded sequence; The production method of said sub-coded sequence: produce N at random
VIndividual [1, N
V] positive integer, as the way point quantity of the required navigation of each ROV; Said N
VFor participating in the AUV quantity of task;
Step 2 assessment population fitness: to the cost f of every air route sequence
iDouble counting N
RSMInferior, the concrete observation time of each way point is generated by its probability distribution at random when calculating at every turn, uses this N then
RSMThe expectation value of inferior result of calculation is as the estimation cost in this air route; Can obtain this individual fitness estimated value after the air route of all AUV in forming into columns estimated
Wherein:
(i=1 ... N
V), L is the length in air route, V is the speed of ROV;
Step 3 is selected N defect individual from current population: each individual selected probability equals the ratio of its fitness and population ideal adaptation degree sum in the current population; Produce [0 at random; 1] random number between if this number is then chosen this individuality greater than ratio, otherwise is not selected;
Step 4 pair defect individual is with Probability p
cCarry out interlace operation: from N defect individual, select the minimum air route of two costs at first respectively, then two air routes are exchanged and as their article one air route;
If when having only an air route in the individuality then carry out air route exchange with the best air route in a fragment of selecting at random in this air route and another individuality; Concrete exchanged form is: new way point D inserts before or after its nearest way point; New way point D will be inserted among the former air route A-B-C-A; The point nearest apart from D is B, obtains inserting result: A-D-B-C-A or A-B-D-C-A; If Dis (A, D)+Dis (B, C) greater than Dis (A, B)+(D C) then selects the latter, otherwise selects the former, wherein Dis (C Dis
1, C
2) represent C
1And C
2Between distance;
Step 5: to the defect individual after intersecting with Probability p
mCarry out mutation operation, carry out heuristic mutation operation with following any one mode:
1) heuristic local exchange: two air routes of each exchange are confirmed at random; A fragment of selecting at random in each air route exchanged in another air route go; According to the INM method each point in this fragment is inserted in another air route, guarantees simultaneously in each heuristic local exchange process, between identical two air routes swap operation can not take place twice;
2) the shortest heuristic air route merges: in individuality, seek two air routes the shortest, and will be wherein one be inserted in another air route with the INM method, with the two merging;
3) the longest heuristic air route splits: the air route that cost in the individuality is maximum splits into two air routes from a random point, and insert in the empty air route with the INM method in the air route that newly splits out;
Step 6: circulation step 2～5N
tInferior, obtain optimum individual;
Step 7: optimum individual is decoded according to the coding rule in the step 1, obtain the planning air route of each AUV.
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| CN103398712A (en) * | 2013-08-02 | 2013-11-20 | 中国人民解放军63983部队 | Alternatively piloted collaborative navigation method |
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| CN112230545A (en) * | 2020-10-13 | 2021-01-15 | 西北工业大学 | AUV course angle control method based on PPGA adaptive optimization PID parameter |
| CN114167880A (en) * | 2021-12-02 | 2022-03-11 | 大连海事大学 | Time-optimal-based multi-underwater glider path planning system |
| CN114326809A (en) * | 2021-12-29 | 2022-04-12 | 广州极飞科技股份有限公司 | Method and device for determining backsight point, electronic equipment and readable storage medium |
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