Method and device for route programming in dynamic unknown environment

Abstract

The invention provides a method and device for route programming in a dynamic unknown environment. In the moving process of a robot along a preset optimal route, a collision-free optimal route is obtained through calculation by using a linear programming gradient method and through environment information acquired by a preset rolling window, and then partial routes are obtained through calculation carried out on the obtained collision-free optimal route and acquired perceptual information in the rolling window. In the calculation process, the rolling window is used for reducing calculation amount and improving efficiency, and the linear programming gradient method is applied to ensure global convergence and to prevent from being caught in the problems of local minimum and oscillation.

Description

technical field [0001] The present application relates to the technical field of autonomous navigation, in particular to a path planning method and device. Background technique [0002] The path planning in an unknown environment is mainly to directly map the sensor data to the action through the planning method of the local path. The path planning methods in the unknown environment commonly used in the prior art mainly include the following types: [0003] 1), the grid method, the grid method mainly divides the robot workspace into grid units with binary information, representing free space and obstacles respectively, but the division of the grid directly affects its planning results, if the grid division If it is too large, the environmental information storage capacity will be small, the resolution will decrease, and the planning ability will be poor; if the grid division is too small, the planning time will be long, and the requirements for information storage capacity w...

AI Technical Summary

Problems solved by technology

However, this method has the following obvious defects: a, the robot is easy to fall into the trap area generated by the local minimum point

e. When the target point is very close to the obstacle, the target is unreachable, so the use of artificial potential field method often leads to the failure of planning

[0005] 3) Genetic algorithm, genetic algorithm is a randomized search algorithm that draws on natural selection and natural genetic mechanisms in the biological world. It has the advantages of simplicity, implicit parallelism, and global optimization. It has advantages for traditional search methods and nonlinear problems. Good applicability, but the operation speed of the genetic algorithm is not fast, and the evolution of many plans will occupy a large storage space and operation time, and due to some defects in the conventional genetic algorithm itself (such as the premature phenomenon of the solution, poor local optimization ability, etc. ), which cannot guarantee the efficiency and reliability of path planning

Although this method can quickly and accurately plan the robot path when the environment is unknown or changes, its disadvantage is that when the number of obstacles increases, the calculation amount of this method will be very large, which will affect the planning results.

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