Robot path planning method based on two cooperative competition particle swarms and Ferguson spline

Abstract

The invention discloses a robot path planning method based on two cooperative competition particle swarms and Ferguson spline, mainly solving the problem of the prior art that the smoothness of the planned path is poor and the convergence rate is slow. The planning method comprises the following steps: modeling the motion environment of a mobile robot; performing three Ferguson spline connections to describe the path of the mobile robot; using the endpoint of the Ferguson spline and the corresponding tangent vector to form and optimize particles; adopting shortest punish function and safety punish function to define a fitness function of which path is to be solved; using the algorithm based on two cooperative competition particle swarms performing global optimization to the fitness function, and outputting the robot path described by Ferguson spline according to the optimization result. The planning method comprehensively considers the shortest and safety of the robot path, the planned path is smooth and is easy for the motion control of the mobile robot; and the planning method achieves the aims of artificial planning and can be used for the autonomous navigation of various mobile robots.

Description

technical field [0001] The invention belongs to the technical field of robots, and in particular relates to a path planning method for mobile robots, which can be used for autonomous navigation of various mobile robots. Background technique [0002] The path planning problem is one of the key technologies of mobile robot navigation. The main task is to find a feasible, collision-free optimal or near-optimal path from the starting point S to the target point G in an environment with obstacles. The existing path planning methods for mobile robots mainly include: grid method, visual graph method, artificial potential field method and various intelligent planning algorithms. The path obtained by the grid method can obtain the optimal solution under certain conditions, but the selection of the grid size will affect the quality of the solution, and the storage space required when the search space is large is also large. The visual graph method needs to continuously reconstruct th...

AI Technical Summary

Problems solved by technology

The path obtained by the grid method can obtain the optimal solution under certain conditions, but the selection of the grid size will affect the quality of the solution, and the storage space required when the search space is large is also large

The visual graph method needs to continuously reconstruct the visual graph, and the search efficiency is relatively low

On the other hand, the path planned by the grid method and the visualization method is generally a broken line, which is not conducive to the motion control of the robot.

The artificial potential field method has a simple structure and is easy to implement. The planned path is relatively smooth and safe, and has been widely used, but it also has major defects: such as local extreme points, unreachable targets, oscillations in front of obstacles, and non-global optimal paths. excellent

This kind of intelligent path planning method has the following disadvantages: (1) This kind of algorithm generally uses grid or link graph to model, and then uses optimization algorithm to optimize the path, which inherits the shortcomings of grid method and link graph method. The path is generally a broken line, which is not conducive to robot motion control, and path smoothing is often required; (2) the calculation time is generally too long, and it is difficult to meet the requirements of real-time path planning; (3) the existing intelligent algorithm still cannot avoid the problem of premature convergence

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