Abstract
This paper improves the accuracy of a mine robot’s positioning and mapping for rapid rescue. Specifically, we improved the FastSLAM algorithm inspired by the lion swarm optimization method. Through the division of labor between different individuals in the lion swarm optimization algorithm, the optimized particle set distribution after importance sampling in the FastSLAM algorithm is realized. The particles are distributed in a high likelihood area, thereby solving the problem of particle weight degradation. Meanwhile, the diversity of particles is increased since the foraging methods between individuals in the lion swarm algorithm are different so that improving the accuracy of the robot’s positioning and mapping. The experimental results confirmed the improvement of the algorithm and the accuracy of the robot.
Highlights
Introduction and Mapping for Rescue RobotsMine rescue robot plays a critical role in mine rescues [1,2,3]
GFA-FastSLAM2.0 algorithm has a higher filtering accuracy, because it acts on the particles through the gravitational field to distribute the particles in the high-likelihood area, which effectively alleviates the problem of particle degradation, and improves the filtering accuracy of the robot
Tively solves the problem of particle weight degradation, and GFA-FastSLAM2.0 algoAfter comparing the average error and variance of the positioning accuracy of the three rithm. This phenomenon shows that the algorithm proposed in this paper efficiently opalgorithms, it is obvious that the improved algorithm has improved the robot positioning timizes particle set and solve the problem of particle weight degradation
Summary
In this process, each particle corresponds to a robot path estimation and environmental features. The FastSLAM1.0 algorithm uses the robot motion model as the particle sampling function, so when the motion control input error is large, the estimated state of the system will be inaccurate. In the FastSLAM2.0 algorithm, a complete EKF iterative process is first adopted, in which the latest moment of control input and landmark characteristics measurement values are integrated, and the posterior estimates of robot pose state are used as the particle sampling function, improving the estimation accuracy of the robot. The FastSLAM2.0 algorithm adopted a new importance density function, but it still exists particle degradation problems Aiming to solve this problem, we introduce the resampling strategy.
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