CN101387888A - Mobile robot path planning method based on binary quanta particle swarm optimization - Google Patents
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Abstract
The invention discloses a mobile robot path planning method based on the binary quantum particle swarm optimization, which is characterized by comprising steps of 1, simplifying a robot into a point moving in a two-dimensional space, and then sensing the present position of the robot and the present positions of obstacles via the visual system, 2, processing all the obstacles sensed by the visual system of the robot into convex polygons, 3, discretizing the two-dimensional space into series grids, and performing binary encoding for eight probable motion directions of the robot at each grid, 4, defining the distance of the path between the starting point and the destination point as a target function required to be solved by the method, and 5, overall optimizing the target function in the step 4 by utilizing the binary quantum particle swarm optimization aiming to the discrete characteristics of robot path planning problems to obtain the optimum mobile robot path. The invention has the advantages of simple process, easy realizing, good robustness, high solving efficiency and the like.
Description
Technical field
The invention discloses a kind of method for planning path for mobile robot based on binary quanta particle swarm optimization.
Background technology
Path planning is meant between robot is from the starting point to the impact point and finds a safety not have the path of bumping, is robot field's important topic.Understanding by to environment knowledge can be divided into the path planning under known environment and the circumstances not known.No matter which kind of classification the robot path planning belongs to, and adopts which kind of planning algorithm, all will follow following steps basically: 1) set up environmental model, be about to the residing real world of robot and carry out the relevant model of abstract back foundation; 2) search nothing is bumped the path, promptly seeks the searching algorithm in the path of eligible in the space of certain model.For the known global path planning of environmental information, existing at present many solutions, but develop and next numerical value potential field method as Artificial Potential Field method sight method and by Artificial Potential Field.Closely during the decade, along with the artificial intelligence study constantly makes progress, many intelligent algorithms also are used in mobile robot's the path planning, comprise fuzzy logic and strengthen learning algorithm, neural network, genetic algorithm and ant group algorithm etc.
Mobile robot's motion planning algorithm is accompanied by developing into of mobile robot to be satisfied the machine human needs and develops.Now, unmanned ground, under water, the development of aerial robot rapidly, robot soccer competition is like a raging fire, and robot just towards microminaturization and multirobot cooperation part to development.Along with the needs of celestial body detecting and unmanned war, the research of robot is more and more paid attention in accidental relief and existed independent navigation in the dyskinetic complex environment.In order to satisfy the needs of mobile robot development, motion planning and will be to higher-dimension degree of freedom robot, multi-robot coordination, the dynamic planning and development in the circumstances not known.
Summary of the invention
The objective of the invention is to overcome the deficiencies in the prior art, provide a kind of and can solve the mobile robot and generate in real time the path planning keeping away the barrier path problems, can solve mobile robot under the uncertain environment, and can realize mobile robot's navigation and keep away the method for planning path for mobile robot based on binary quanta particle swarm optimization of barrier problem.
According to technical scheme provided by the invention, described method for planning path for mobile robot based on binary quanta particle swarm optimization comprises following steps:
Step 1: robot is simplified to a point, and in two-dimensional space, moves, by can perception oneself the present pose of vision system and the position of barrier;
Step 2: all barriers that robotic vision system is perceived are processed into convex polygon;
Step 3: turn to a series of grid with two-dimensional space is discrete, and may direction of motion carry out binary coding eight of each grid place to the mobile robot;
Step 4: the objective function that the length in the path of definition from the starting point to the impact point need be found the solution for this method, promptly obtain by the accumulation of the grid on the path, computing method are specially:
Wherein d represents two paths between grid;
Step 5: at the discrete features of robot path planning's problem, utilize binary quanta particle swarm optimization that the objective function in the step 4 is carried out global optimization to obtain optimum mobile robot path, specifically comprise:
(5.1) determining of particle distance: in order to represent two distances between the particle, introduced Hamming distance from defining it, define two particle (X, Y), they have respectively two decision variables (Xi1, Xi2), (Yi1, Yi2), each decision variable is by five binary codings, and then the distance of two particles can have Hamming distance from representing:
|X-Y|＝d
H(X，Y) (2)
Its value is the number of the different value of the corresponding position of two bit strings;
(5.2) determining of optimum median location (mbest): the value of mbest is by the value decision of the identical bits of each particle bit string in the particle colony, by adding up the size of the probability of binary-coded each appearance 0,1 of statistics particle in each particle colony, occur 0 often, then the mbest correspondence is 0, otherwise, then be 1;
(5.3) the sub-P of local attraction
iDetermine: utilize the multiple spot interlace operation to obtain local attraction's; Promptly calculate the local optimum and the global optimum in each generation earlier, calculate local attraction's by the mode of intersecting;
(5.4) determining of particle position:
b＝α*d
H(X
id，mbest
d)*ln(1/u)，u＝rand() (3)
D wherein
H(X
Id, P
Id) be the X of particle i d dimension
IdP with local attractor d dimension
IdHamming distance from.
The present invention compared with the prior art, process is simple, realizes easily, robustness is good, find the solution the efficient advantages of higher.Therefore, the invention solves mobile robot's generation in real time and keep away the barrier path problems.The invention solves the path planning of mobile robot under the uncertain environment, realized mobile robot's navigation and kept away the barrier problem, realized the real-time route planning and the control of robot preferably, for mobile robot path planning provides an effective method.
Description of drawings
Fig. 1 grid rules are drawn the region of search synoptic diagram.
Fig. 2 direction encoding synoptic diagram.
The binary coding figure of Fig. 3 particle position.
The multiple spot intersection synoptic diagram of Fig. 4 binary coding particle.
The path planning synoptic diagram that Fig. 5 iteration obtained after 15 generations.
The optimizing planning path synoptic diagram that Fig. 6 iteration obtained after 42 generations.
The path planning synoptic diagram that Fig. 7 iteration obtained after 70 generations.
The optimal programming path synoptic diagram that Fig. 8 iteration obtained after 276 generations.
The path planning synoptic diagram that Fig. 9 iteration obtained after 180 generations.
The optimal programming path synoptic diagram that Figure 10 iteration obtained after 227 generations.
In all figure, all asterisk point " * " expression robot ambulation paths, solid dot is represented free space, square is represented barrier.
Embodiment
The invention will be further described below in conjunction with concrete drawings and Examples.
Embodiment 1: the method for planning path for mobile robot that the present invention is based on binary quanta particle swarm optimization comprises following steps:
1, robot is simplified to the point that an energy moves in two-dimensional space, it passes through the present pose of vision system energy perception oneself and the position of barrier; And robot can perception barrier be processed into convex polygon in the two-dimensional space.
2, determine that according to the size of robot self people of grid granularity is little, set up the environmental model of robot, be i.e. discrete state space, the pattern after the foundation such as Fig. 1 with the grid method;
3, determine the coded system of eight directions on the discrete space, promptly adopt binary coding, as Fig. 2;
4, the objective function of problem identificatioin promptly defines the objective function that the length in the path from the starting point to the impact point need be found the solution for this method, computing method such as formula (1); The target function value of particle position is as adaptive value fitness in the binary quanta particle swarm optimization;
5, the step of the following binary quanta particle swarm optimization of enforcement is optimized to obtain optimum mobile robot path objective function;
6, set the detail parameters of binary quanta particle swarm optimization, i.e. the length of population size, binary string, maximum iteration time and search volume; Population size is generally 20～50, and the length of binary string is generally got 4 * 4 * n (n for and the length of side of people workspace), and maximum iteration time and search volume are determined on a case-by-case basis.
The local optimum pbest of 7, the particle position variable of initialization binary quanta particle swarm optimization, and particle, and obtain the global optimum gbest of colony;
8, calculate the intermediate value optimal location mbest of population;
9, illustrate to calculate the sub-P of local attraction by Fig. 4
i
10, calculate particle position X by formula (3), (4)
Id
11, calculate the fitness fitness (x of particle reposition
i(t+1));
12, if the current optimal location of new particle more is i.e.: fitness (x
i(t+1))＜fitness (pbest
i(t)), pbest then
i(t+1)=x
i(t+1), otherwise, pbest
i(t+1)=pbest
i(t);
13, upgrade the optimal location of colony, if i.e.: fitness (pbest
i(t+1))＜fitness (gbest (t)), then gbest (t+1)=pbest
i(t+1);
14, circulation step 8～13, finish after being to reach maximum iteration time, export the position gbest of global optimum of colony then, are mobile robot's optimal path.
Shown in Fig. 6,7, the perform region of robot is 16 * 16 clear space, and the starting point in path is (1,1), and terminal point is (16,16), and it is 30 that particle colony is set in the algorithm, and the length of binary string is 4 * 4 * 16=256, and α is made as 0.6.
Shown in Fig. 7,8, the perform region of robot be 16 * 16 the barrier space arranged, the starting point in path is (1,1), terminal point is (16,16), it is 30,4 * 4 * 16=256 that particle colony is set in the algorithm, α is made as 0.6.
Embodiment 3
Shown in Fig. 9,10, the perform region of robot be 16 * 16 the barrier space arranged, the starting point in path is (4,12), terminal point is (16,16), it is 30,4 * 4 * 16=256 that particle colony is set in the algorithm, α is made as 0.6.
Claims (2)
1, a kind of method for planning path for mobile robot based on binary quanta particle swarm optimization is characterized in that this method comprises following steps:
Step 1: robot is simplified to a point, and in two-dimensional space, moves, by can perception oneself the present pose of vision system and the position of barrier;
Step 2: all barriers that robotic vision system is perceived are processed into convex polygon;
Step 3: turn to a series of grid with two-dimensional space is discrete, and may direction of motion carry out binary coding eight of each grid place to the mobile robot;
Step 4: the objective function that the length in the path of definition from the starting point to the impact point need be found the solution for this method, promptly obtain by the accumulation of the grid on the path, computing method are specially:
Wherein d represents two paths between grid;
Step 5: at the discrete features of robot path planning's problem, utilize binary quanta particle swarm optimization that the objective function in the step 4 is carried out global optimization to obtain optimum mobile robot path, specifically comprise:
(5.1) determining of particle distance: in order to represent two distances between the particle, introduced Hamming distance from defining it, define two particle (X, Y), they have respectively two decision variables (Xi1, Xi2), (Yi1, Yi2), each decision variable is by five binary codings, and then the distance of two particles can have Hamming distance from representing:
|X-Y|＝d
H(X，Y)(2)
Its value is the number of the different value of the corresponding position of two bit strings;
(5.2) determining of optimum median location (mbest): the value of mbest is by the value decision of the identical bits of each particle bit string in the particle colony, by adding up the size of the probability of binary-coded each appearance 0,1 of statistics particle in each particle colony, occur 0 often, then the mbest correspondence is 0, otherwise, then be 1;
(5.3) the sub-P of local attraction
iDetermine: utilize the multiple spot interlace operation to obtain local attraction's; Promptly calculate the local optimum and the global optimum in each generation earlier, calculate local attraction's by the mode of intersecting;
(5.4) determining of particle position:
b＝α*d
H(X
id，mbest
d)*ln(1/u)，u＝rand() (3)
D wherein
H(X
Id, P
Id) be the X of particle i d dimension
IdP with local attractor d dimension
IdHamming distance from.
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