A distributed multi-target tracking method for swarm robots based on phd filtering

A multi-target tracking and swarm robot technology is applied in the field of distributed multi-target tracking of swarm robots. Obstacle avoidance characteristics, high tracking accuracy and stability, and the effect of high tracking performance

Inactive Publication Date: 2021-06-15
CHONGQING UNIV
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AI Technical Summary

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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  • A distributed multi-target tracking method for swarm robots based on phd filtering
  • A distributed multi-target tracking method for swarm robots based on phd filtering
  • A distributed multi-target tracking method for swarm robots based on phd 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 group robots based on PHD filtering, including S1: in the initial state, the group robots are randomly distributed within a given constraint boundary, and a Power graph is constructed according to the position coordinates and weights of the multi-robots, Divide the search area; S2: Solve the weighted centroid of the Power unit corresponding to each robot; S3: All robots start to move from the current position X to the corresponding h i (0), collect relevant measurement information, calculate the normalization constant, and update the Power diagram; S4: update the PHD according to the measurement data and use the new PHD as a weight, execute S2 to obtain the new centroid position coordinates; S5: repeat S3 and S4, until the target appears in the robot's field of vision; S6: After the robot finds the target, observe the motion state of the target, use the functional relationship between the current radar measurement information and the acceleration, estimate the acceleration disturbance in real time, and perform adaptive adjustment of the acceleration variance , to keep track of the target synchronously.

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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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/12G01C21/20
CPCG01C21/20G05D1/12
Inventor 陈刚杨传兵
Owner CHONGQING UNIV
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