Local path planning method and device, robot and storage medium

A local path planning and local path technology, applied in the field of robotics, can solve the problems of poor robot operation safety and robot obstacle avoidance performance, and achieve the effect of improving space coverage, improving obstacle avoidance performance, and increasing the total number

Pending Publication Date: 2022-04-29
GUANGZHOU SAITE INTELLIGENCE TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The existing DWA algorithm has the problem of poor obstacle avoidance performance of the robot, resulting in poor robot operation safety

Method used

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  • Local path planning method and device, robot and storage medium
  • Local path planning method and device, robot and storage medium
  • Local path planning method and device, robot and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0061] Figure 1A It is a flow chart of a local path planning method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of robot local path planning. This method can be executed by the local path planning device provided by the embodiment of the present invention. The device can Realized by software and / or hardware, and configured in the robot provided by the embodiment of the present invention, such as Figure 1A As shown, the method specifically includes the following steps:

[0062] S101. Determine the prediction duration of the dynamic window method to predict the local path of the robot.

[0063] As mentioned above, the dynamic window method refers to sampling multiple times at a preset sampling frequency in the constrained velocity space to obtain multiple sets of sampling velocities (including linear velocity and angular velocity), and to simulate the robot at these sampling velocities within a certain period of time. (that...

Embodiment 2

[0077] figure 2 It is a flowchart of a local path planning method provided by Embodiment 2 of the present invention. This embodiment is further refined on the basis of the above-mentioned Embodiment 1, and describes in detail the specific process of each step in the local path planning method, as shown in figure 2 As shown, the method includes:

[0078] S201. Determine the prediction duration of the dynamic window method to predict the local path of the robot.

[0079] Exemplarily, as described in the foregoing embodiments, the robot may read configuration parameters during each start-up process, acquire and determine the prediction duration of the robot's local path predicted by the dynamic window method.

[0080] S202. Divide the forecast duration into at least two sub-segments.

[0081] Exemplarily, in the embodiment of the present invention, the predicted duration is divided into at least two sub-segments of equal duration by using an equal division method.

[0082] S...

Embodiment 3

[0144] image 3 A schematic structural diagram of a local path planning device provided in Embodiment 3 of the present invention, as shown in image 3 As shown, the device includes:

[0145] The prediction duration determination module 301 is used to determine the prediction duration of the dynamic window method to predict the local path of the robot;

[0146] A segmentation module 302, configured to segment the predicted duration into at least two sub-segments;

[0147] A path generation module 303, configured to, in the i-th subsection, for each of the at least two first paths of the i-1th subsection, use the first path The end point of is the starting point, and the dynamic window method is used to generate at least two second paths;

[0148] The target path determination module 304 is configured to evaluate a plurality of partial paths composed of the first path and the second path, and select a partial path with the highest score from the plurality of partial paths as ...

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Abstract

The invention discloses a local path planning method and device, a robot and a storage medium. The method comprises the following steps: segmenting prediction duration of predicting local paths of a robot by using a dynamic window method into at least two sub-segments, generating at least two new paths by using the dynamic window method in each sub-segment by using an end point of each path in the previous sub-segment as a starting point, and evaluating a plurality of local paths formed by combining the paths of the sub-segments, and selecting the local path with the highest score from the plurality of local paths as a target path. According to the method, at least two new paths are generated by using the dynamic window method in each sub-segment by taking the end point of each path in the previous sub-segment as the starting point, the number of the paths in each sub-segment is multiplied relative to the number of the paths in the previous sub-segment, the total number of local paths in the prediction duration is increased, and the prediction accuracy is improved. The space coverage rate of the tail end of the path is increased, so that the optimal path which most conforms to the actual environment can be selected, the obstacle avoidance performance of the robot is improved, and the operation safety of the robot is improved.

Description

technical field [0001] Embodiments of the present invention relate to robot technology, and in particular to a local path planning method, device, robot and storage medium. Background technique [0002] A mobile robot is a device that intelligently controls movement to perform various tasks. With the continuous development of artificial intelligence, mobile robots have gradually been able to replace humans to do part of the work. Compared with humans, robots have the advantage of being able to work for a long time and with high efficiency. Mobile robots, especially service-type mobile robots, such as sweeping robots and delivery robots, are gradually becoming common in daily life. [0003] In the technical field of mobile robots, planning control is an important research field. The more commonly used planning algorithms include A* algorithm, D* algorithm, DWA (Dynamic Window Method) algorithm, etc. The principle of the DWA algorithm is to sample multiple sets of velocities...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0223G05D2201/0217
Inventor 赖志林李振骆增辉
Owner GUANGZHOU SAITE INTELLIGENCE TECH CO LTD
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