Abstract
Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.
Highlights
In recent years, with the development of robot technology, robot vision has become the forerunner of machine vision, and vision has become one of the most important sensing technologies for robots [1,2]
This paper is oriented to robot tracking and grabbing of dynamic targets based on previous work and the geometric particle filtering (GPF) framework
The low-weight particles and preliminary resampled particles are combined to reduce the difference in the particle probability distribution before and after resampling to improve particle diversity
Summary
With the development of robot technology, robot vision has become the forerunner of machine vision, and vision has become one of the most important sensing technologies for robots [1,2]. Chen et al [18] built a vision-guided grasping system, which adopted the edge particle filter algorithm to obtain parameters such as target position, speed, and acceleration It realized the tracking of plane moving objects by a robotic arm. Hussain et al [19] used geometric particle filtering (GPF) based on reflection groups to realize three-dimensional space target tracking. This method only used depth information and did not rely on texture features. A robot dynamic target capture method is proposed based on the affine group improved Gaussian resampling particle filter. A method of robot dynamic target tracking and grasping is proposed based on affine group improved Gaussian resampling particle filter.
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