CN114199251A

CN114199251A - Anti-collision positioning method for robot

Info

Publication number: CN114199251A
Application number: CN202111466251.5A
Authority: CN
Inventors: 姜翠环; 骆敏舟; 卢钰
Original assignee: Institute of Intelligent Manufacturing Technology JITRI
Current assignee: Institute of Intelligent Manufacturing Technology JITRI
Priority date: 2021-12-03
Filing date: 2021-12-03
Publication date: 2022-03-18
Anticipated expiration: 2041-12-03
Also published as: CN114199251B

Abstract

The invention discloses an anti-collision positioning method of a robot, which comprises the following steps: acquiring the pose of the robot relative to a world coordinate system based on the odometer; randomly placing n pose-carrying particles near the odometer; when the robot moves for a certain distance, selecting the particles with the highest matching degree with the current scene map and updating the particles into the pose of the robot; and when the pose of the robot is updated, synchronously updating a forbidden area of the current scene map, wherein the forbidden area refers to the interior of an inaccessible obstacle area of the robot. The method of the invention can improve the positioning precision, improve the ability of the robot to walk at the edge of the barrier, and avoid colliding with the barrier and damaging the property.

Description

Anti-collision positioning method for robot

Technical Field

The invention belongs to the technical field of robot positioning, and particularly relates to an anti-collision positioning method for a robot.

Background

In the autonomous walking process of the robot, the robot needs to continuously interact with the environment so as to obtain the self pose in the environment, and the robot walks along the known route after the navigation path planning. Thus completing corresponding tasks such as patrol, distribution, guidance and the like. Positioning is the basis for navigation and task execution. However, in actual engineering, a large or small positioning error inevitably occurs, and when the robot walks around the edge of an obstacle, the pose of the robot sometimes reaches the inside of the obstacle, and this error will cause collision or even failure in subsequent navigation path planning. Therefore, the robot needs to be repositioned to obtain a more accurate pose of the robot.

At present, in the positioning level, there are usually kalman filtering method, amcl monte carlo method, and graph optimization method.

The Kalman filtering method is characterized in that Gaussian estimation of the pose of the robot is generated by fusing sensor data such as a milemeter, imu and a GPS. This method may not use laser/visual data, so the positioning result is less affected by the scene, but there is a cumulative error.

The amcl Monte Carlo is based on a probability method, the initial position of the robot is assumed to be unknown, the robot is moved for a distance, the robot pose is determined after the robot is moved for a distance through laser map matching and particle random position resampling calculation. The method needs the robot to move a certain distance to gradually position to the correct position, but many scenes do not have the robot to position by the moving method. And the probability of positioning failure is greatly improved in smaller spaces such as scenes with similar characteristics, corridors and the like.

The method comprises the steps that positioning is carried out on the basis of a laser radar landmark (or an image key frame) optimized by a graph, the robot is required to move, a transfer matrix between a pose and the landmark is obtained, and therefore the current pose of the robot is estimated. It has cumulative errors as with the kalman filtering method. In addition, the pose is adjusted through the return detection of the key frame, but when the characteristics of the key frame are not good, the return failure also exists.

Disclosure of Invention

The invention provides an anti-collision positioning method for a robot, aiming at the problems in the prior art.

The invention solves the technical problems through the following technical means:

a method of collision-resistant positioning of a robot, the method comprising:

acquiring the pose of the robot relative to a world coordinate system based on the odometer;

randomly placing n pose-carrying particles near the odometer;

when the robot moves for a certain distance, selecting the particles with the highest matching degree with the current scene map and updating the particles into the pose of the robot;

and when the pose of the robot is updated, synchronously updating a forbidden area of the current scene map, wherein the forbidden area refers to the interior of an inaccessible obstacle area of the robot.

Further, the method for performing prohibited area update on the current scene map comprises:

and registering the selected radar point cloud of the particles with the highest matching degree with the current scene map, marking the position where the radar point cloud data is not matched with the current scene map as an unreliable factor, further judging whether the unreliable factor is an obstacle area of the current scene map, and if so, adding the current positioning result into the forbidden area.

Further, the method for adding the current positioning result to the forbidden area is as follows:

calculating a measurement result of the selected radar point cloud of the particles with the highest matching degree with the current scene map, mapping corresponding points to corresponding positions on the current scene map, and adding a forbidden area to each position point to obtain a forbidden area map layer;

expanding the obstacle area on the current scene map for a certain distance to obtain an expanded area and obtain a scene expanded map layer;

and overlapping and calculating the forbidden area layer, the scene expansion layer and the current scene map so as to update the forbidden area on the current scene map.

Further, the method also comprises the following steps:

randomly placing particles in a non-obstacle area near the odometer of the updated current scene map;

checking the matching degree of the radar data of all particles and the current scene map, recording whether the matching degree and the unmatched area are in the barrier area of the static map, if so, marking the matching degree and the unmatched area as an unreliable factor, carrying out weighted summation on the unreliable factor and the matching degree to obtain the confidence coefficient of all particles, wherein the confidence coefficient represents the position and posture credibility of the corresponding particles, selecting the particles with the maximum reliability to obtain the optimal position and posture of the particles, and the position and posture is the positioning result.

The invention has the beneficial effects that: the method of the invention can improve the positioning precision, improve the ability of the robot to walk at the edge of the barrier, and avoid colliding with the barrier and damaging the property.

Drawings

Fig. 1 is a flowchart of an anti-collision positioning method according to the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Examples

Referring to fig. 1, the strategy of the anti-collision positioning method provided by this embodiment is as follows: and performing global positioning based on amcl, in the global positioning process, obtaining the pose of the radar coordinate system relative to the world coordinate system through the pose of the odometer relative to the robot coordinate system, randomly placing n (such as n is 1000) particles carrying the poses near the odometer, moving for a certain distance, recording the matching degree of each particle and the scene map 1, selecting the particle (namely the pose) with the highest matching degree from the particles, updating the particle as the pose of the odometer in the global positioning, and further processing and updating the map scene based on the pose updating.

The method for processing and updating the map scene comprises the following steps:

and selecting the particles with the highest matching degree from the point cloud data to perform registration of the radar point cloud and the scene map 1, and performing secondary processing on the scene map 1 in the point cloud registration process. And checking the position of the radar data which is not matched with the scene map 1, marking the position as an unreliable factor, further checking whether the unreliable factor is in the obstacle of the scene map 1, if so, adding the current positioning result into the forbidden area, and otherwise, finishing positioning updating.

More specifically:

step 1), adding a forbidden area layer. And calculating a measurement result of the current radar point cloud based on the pose obtained by positioning, mapping the corresponding point to the corresponding position on the scene map, and adding a forbidden area at each position point.

And step 2), adding a scene expansion layer. And (3) performing expansion at a certain distance on the obstacle area on the scene map to obtain an expanded area according to the principle of the step 1).

And 3) carrying out superposition calculation on the forbidden area layer, the scene expansion layer and the scene map in the step 1) and the step 2) to obtain a new scene map 2, wherein the new scene map 2 is adopted in the subsequent calculation.

And 4) randomly placing particles in the non-obstacle area of the scene map 2 near the odometer, wherein each particle represents different odometer poses.

And 5) checking the matching degree of the radar data of all the particles and the scene map 1, recording whether the matching degree and the unmatched area are in the barrier of the static map (which is unreasonable measurement), if so, marking as an unreliable factor, carrying out weighted summation on the unreliable factor and the matching degree to obtain the confidence coefficient of all the particles, wherein the confidence coefficient represents the position and posture credibility of the corresponding particles, selecting the particles with the maximum confidence degree to obtain the optimal position and posture of the particles, and the position and posture are the positioning results.

It is noted that, in this document, relational terms such as first and second, and the like, if any, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An anti-collision positioning method for a robot, the method comprising:

randomly placing n pose-carrying particles near the odometer;

2. The anti-collision positioning method for the robot according to claim 1, wherein the method for updating the forbidden area of the current scene map comprises:

3. The anti-collision positioning method for the robot according to claim 2, wherein the method for adding the current positioning result to the forbidden area is as follows:

4. The method of claim 3, further comprising: