Mobile robot path planning method based on binary quanta particle swarm optimization
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A quantum particle swarm and mobile robot technology, applied in the field of mobile robot path planning, can solve problems such as uncertain mobile robot path planning, and achieve the effect of simple process, good robustness, and easy realization
Inactive Publication Date: 2009-03-18
JIANGNAN UNIV
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[0004] The purpose of the present invention is to overcome the deficiencies in the prior art, to provide a mobile robot that can solve the problem of generating obstacle avoidance paths in real time, can solve the pa...
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Embodiment 1
[0035] Embodiment 1: the mobile robot path planning method based on the binary quantum particle swarm algorithm of the present invention comprises the following steps:
[0036] 1. Simplify the robot into a point that can move in two-dimensional space. It can perceive its current posture and the position of obstacles through the visual system; and the obstacles that the robot can perceive are processed into two-dimensional space. Convex polygon.
[0037] 2. The person who determines the grid granularity according to the size of the robot itself is small, and uses the grid method to establish the environment model of the robot, that is, the discrete state space. The established style is as follows figure 1 ;
[0038] 3. Determine the encoding method of the eight directions in the discrete space, that is, use binary encoding, such as figure 2 ;
[0039] 4. Determine the objective function of the problem, that is, define the length of the path from the starting point to the ta...
Embodiment 2
[0052] Such as Figure 7 , 8 As shown, the working area of the robot is a 16×16 moving space with obstacles, the starting point of the path is (1, 1), and the end point is (16, 16). In the algorithm, the particle population is set to 30, 4×4×16= 256, α is set to 0.6.
Embodiment 3
[0054] Such as Figure 9 , 10 As shown, the working area of the robot is a 16×16 moving space with obstacles, the starting point of the path is (4, 12), the end point is (16, 16), and the particle population is set to 30 in the algorithm, 4×4×16= 256, α is set to 0.6.
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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 [0001] The invention discloses a mobile robot path planning method based on binary quantum particle swarm algorithm. Background technique [0002] Path planning refers to finding a safe and collision-free path for the robot from the starting point to the target point, which is an important topic in the field of robotics. According to the understanding of environmental knowledge, it can be divided into path planning in known environment and unknown environment. Regardless of which category the robot path planning belongs to and which planning algorithm is used, the following steps must basically be followed: 1) Establish an environmental model, that is, abstract the real world where the robot lives and then establish a related model; 2) Search for a collision-free path , which is a search algorithm for finding qualified paths in the space of a certain model. For global path planning with known environmental information, there are many solutions, such as arti...
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