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Distributed multi-target tracking method for swarm robots on the basis of PHD (Probability Hypothesis Density) filtering

A multi-target tracking and swarm robot technology, which is applied in the field of distributed multi-target tracking of swarm robots, can solve the problems of not proposing a robot multi-target tracking dynamic model, and not considering the global path planning problem of group robot coordination control

Inactive Publication Date: 2019-02-01
CHONGQING UNIV
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Problems solved by technology

Although these technologies solve the deficiency of a certain aspect of multi-target tracking, they do not consider how to realize the coordinated control among swarm robots and the global path planning in complex environments, nor do they propose a dynamic model for multi-target tracking of robots.

Method used

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  • Distributed multi-target tracking method for swarm robots on the basis of PHD (Probability Hypothesis Density) filtering
  • Distributed multi-target tracking method for swarm robots on the basis of PHD (Probability Hypothesis Density) filtering
  • Distributed multi-target tracking method for swarm robots on the basis of PHD (Probability Hypothesis Density) filtering

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Embodiment Construction

[0069] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0070] A method for multi-target search and tracking of swarm robots based on PHD filtering, comprising the following steps:

[0071] In the first step, assuming that in the initial state, the swarm robots are randomly distributed within a given square constraint boundary, and according to the position coordinates and weights of the multi-robots, a Power graph is constructed to divide the search area.

[0072] The Power diagram is a weighted Voronoi diagram, as attached figure 2 , the Voronoi diagram divides the space according to the position of the discrete points, so that the distance between the points in each Voronoi area and the corresponding discrete points is the smallest, and the Power diagram assigns weight to each discrete point, redefining the concept of distance in the Voronoi diagram, optimizing The traditional Voronoi diagr...

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Abstract

The invention relates to a distributed multi-target tracking method for swarm robots on the basis of PHD (Probability Hypothesis Density) filtering. The method comprises the following steps that: S1:under an initial state, swarm robots are randomly distributed in a given constrained boundary, a Power graph is constructed according to the position coordinates and the weights of multiple robots, and a search area is divided; S2: the weighted centroid of a Power unit corresponding to each robot is solved; S3: all robots begin to move to corresponding hi(0) from a current position X, relevant measurement information is collected, and a normalization constant is calculated to update the Power graph; S4: according to measurement data, the PHD is updated, new PHD is taken as weight, and S2 is executed to obtain a new centroid position coordinate; S5: S3 and S4 are repeated until a target is in the presence in the visual field of the robot; and S6: after the robot finds the target, the movement state of the target is observed, and a function relationship between current radar measurement information and acceleration is used for estimating an acceleration disturbance quantity in real time,acceleration variance adaption regulation is carried out, and the target is kept at synchronous tracking.

Description

technical field [0001] The invention belongs to the technical field of robot tracking, and relates to a distributed multi-target tracking method for group robots based on PHD filtering. Background technique [0002] In recent years, with the rapid development of industrialization and informatization, robot technology has become a research hotspot of many scholars and experts at home and abroad. Among them, the research on swarm intelligence algorithm is particularly in-depth, such as filtering algorithm, genetic algorithm, ant colony algorithm, particle swarm algorithm, etc. Some swarm intelligence algorithms have been used in actual robot systems and have played a great role, such as : Foreign literature "Study of Formation Control and Obstacle Avoidance of Swarm Robots using Evolutionary Algorithms" uses bacterial foraging (BFOA) and particle swarm optimization (PSO) algorithms for formation control and obstacle avoidance of swarm robots, domestic literature "A suitable A...

Claims

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Application Information

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IPC IPC(8): G05D1/12G01C21/20
CPCG01C21/20G05D1/12
Inventor 陈刚杨传兵
Owner CHONGQING UNIV
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