CN113741473B - Photocatalyst mobile robot and map construction method - Google Patents

Abstract

The invention provides a photocatalyst mobile robot, which comprises a shell, a wireless communication module, a mobile assembly, a sensor module, a filter and an ROS module, wherein the shell is arranged on the mobile assembly; the sensor module comprises a depth-of-field camera and a laser radar, wherein the depth-of-field camera is arranged on the shell and is used for acquiring a local three-dimensional cloud image, and the laser radar is used for acquiring the distance between a local characteristic point and the robot; the map construction method comprises the steps of fusing a first grid map generated based on the laser radar with a second grid map generated based on the depth camera to generate an indoor map; the ROS module generates an indoor map by utilizing the collected information through the depth-of-field camera and the laser radar, so that the robot moves to a target position according to an instruction of external terminal equipment, and the purification assembly is driven to automatically purify air; the mobile robot improves the efficiency of air purification and expands the range of air purification.

Description

Photocatalytic mobile robot and map construction method

Technical Field

The present invention relates to the field of robot technology, and in particular to a photocatalyst mobile robot and a map construction method.

Background Art

In today's era, the demand for air purification in people's daily life and production process is increasing. Under this market demand background, photocatalyst air purifiers have emerged. However, in a society with rapid development of informatization, intelligent, autonomous and convenient Internet of Things devices have also become the main development direction today. Existing photocatalyst air purification products cannot move autonomously and automatically purify the air in the target area, and the area they purify is limited.

Summary of the invention

The purpose of the present invention is to provide a photocatalyst mobile robot and a map construction method, which are used to solve the problem of limited purification range of air purification products in the prior art.

On the one hand, this embodiment provides a photocatalyst mobile robot, comprising:

Shell: including an outer shell and a chassis arranged at one end of the outer shell;

A wireless communication module, disposed in the housing, for wirelessly connecting to an external terminal;

A moving component, mounted on the chassis, used for moving the robot;

A purification component, comprising an air inlet and an air outlet arranged on the surface of the housing; and an air purifier installed in the housing;

A sensor module, comprising a depth-of-field camera and a laser radar arranged on the housing, wherein the depth-of-field camera is used to obtain a local three-dimensional cloud image, and the laser radar is used to obtain a distance between a local feature point and the robot;

A filter is disposed in the housing and is used to obtain the real-time position and posture of the local feature point relative to the robot according to the motion state of the robot; the motion state of the robot includes the acceleration, angular velocity and displacement of the robot in the current state;

The ROS module is disposed in the shell and is used to construct an indoor map according to the local three-dimensional cloud image and the distance between the local feature point and the robot.

Preferably, the sensor module also includes an ultrasonic module and a cliff sensor, wherein the ultrasonic module is used to detect obstacles in front of the robot, and the cliff sensor is used to detect the height difference of the working platform.

Preferably, the ultrasonic module includes three ultrasonic detectors, and the ultrasonic detectors are arranged on the housing.

Preferably, the moving assembly includes two driving wheels, a hub motor arranged inside the driving wheels, and four auxiliary universal wheels arranged around the chassis.

In a second aspect, this embodiment provides a map construction method based on the above-mentioned photocatalyst mobile robot, and the map construction method comprises:

Get the displacement, acceleration and speed of the robot;

The distance and angle between the local feature point and the robot are obtained through the laser radar;

According to the displacement, acceleration, speed of the robot and the distance and angle between the local feature points and the robot, the real-time position and posture of the local feature points relative to the robot are obtained to generate a first grid map; the local feature points represent multiple landmarks within the detection range of the laser radar;

The local image is collected through the depth-of-field camera to generate a local three-dimensional cloud image;

Projecting the local three-dimensional cloud image to form a second grid image;

The first raster image is fused with the second raster image to generate an indoor map.

Wherein, the first grid map and the second grid map are both composed of a plurality of grids, and the status of each grid is occupied or empty;

If the states of the grids of the first grid map and the second grid map are both empty, the state of the grid is determined to be empty; in other cases, the state of the grid is determined to be occupied.

The present invention provides a photocatalyst mobile robot, which collects information through a depth-of-field camera and a laser radar, and a ROS module uses the collected information to generate an indoor map, so that the robot moves to a target position according to instructions from an external terminal device and drives a purification component to automatically purify the air; the mobile robot of the present invention improves the efficiency of air purification and expands the range of air purification.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic structural diagram of a photocatalyst mobile robot according to a first embodiment of the present invention;

FIG2 is a system framework diagram of a photocatalyst mobile robot according to Embodiment 1 of the present invention;

FIG3 is a flowchart of a map construction method according to a second embodiment of the present invention.

DETAILED DESCRIPTION

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, inside, outside, center...) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can realize it. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that such combination of technical solutions does not exist and is not within the protection scope required by the present invention.

The following embodiments are now provided:

Embodiment 1:

A photocatalyst mobile robot as shown in FIGS. 1 and 2 comprises:

Housing 1: comprises a housing 11 and a chassis 12 arranged at one end of the housing 11;

The wireless communication module 2 is arranged in the shell 1 and is used for wireless connection with an external terminal; the wireless terminal, such as a web service platform and a mobile phone APP, sends instructions through the mobile phone and the web service platform and transmits them to the wireless communication module; the wireless communication module can be a wireless network module such as the Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G network, 5G network, Bluetooth, Wi-Fi, etc.

The mobile component 3 is installed on the chassis and is used to move the robot. The mobile component includes two driving wheels, a hub motor arranged inside the driving wheels, and four auxiliary universal wheels arranged around the chassis. The hub motor is a motor that integrates the power system, transmission system and brake system of the vehicle.

The purification component 4 includes an air inlet 41 and an air outlet 42 arranged on the surface of the shell; and an air purifier (not shown) installed in the shell; after the air purifier is turned on, air is taken in through the air inlet 41 and discharged through the air outlet 42 to achieve air circulation.

The sensor module 5 includes a depth of field camera 51 and a laser radar 52 arranged on the shell. The depth of field camera 51 is used to obtain a local three-dimensional cloud map. Specifically, the depth of field camera 51 can be set in two groups, one of which can be a first camera for identifying a low-resolution depth map, and a second camera for providing stereo vision. The local three-dimensional cloud map is formed by fusing the two groups of cameras. This local three-dimensional cloud map is used to calculate the surface normal map, thereby estimating the current posture of the robot.

The laser radar 52 is used to obtain the distance between the local feature point and the robot; specifically, the laser radar 52 can output the distance between the robot and the feature point surface. Each feature point can be understood as a landmark. The local feature point can include multiple feature points. The more feature points there are, the more accurate the estimation of the relative position coordinates of the robot will be.

The filter 6 is arranged in the shell 1 and is preferably an extended Kalman filter; the filter is used to obtain the real-time position and posture of the local feature point relative to the robot according to the motion state of the robot; the motion state of the robot includes the acceleration, angular velocity and displacement of the robot in the current state.

The ROS module 7 is arranged in the shell 1, and is used to build an indoor map according to the local three-dimensional cloud map and the distance between the local feature point and the robot; the command of the external terminal moves to the target position of the indoor map to drive the purification component to purify the air. Specifically, after the indoor map is built, the indoor map can be reported to the mobile phone through the wireless communication module, and the positions of various areas of the indoor environment are marked in the mobile phone APP according to the indoor environment, such as the kitchen, living room, etc. For example, after determining the position of the kitchen area, a certain position in the kitchen area is used as the target position, and the target trajectory of air purification in the kitchen area is planned on the mobile phone APP, and then the target position instruction information is sent to the ROS module through the wireless communication module. The ROS module drives the hub motor to drive the active wheel to rotate according to the target position instruction information, thereby driving the robot to move to the kitchen area, and then purifies the air according to the target trajectory. The air purifier can be remotely turned on and off through the mobile phone APP, thereby purifying the air in the kitchen area; fully automated purification is realized, the purification range is expanded, and the purification efficiency is improved.

Preferably, the sensor module 4 also includes an ultrasonic module 53 and a cliff sensor 54. The ultrasonic module is used to detect obstacles in front of the robot, and the cliff sensor is used to detect the height difference of the working platform. When a step or a high place is detected, a signal is sent to the ROS module. The ROS module controls the robot to stop moving to prevent the robot from sliding from a high place.

Preferably, the ultrasonic module 53 includes three ultrasonic detectors, and the ultrasonic detectors are arranged on the housing.

Embodiment 2:

This embodiment provides a map construction method based on the above-mentioned photocatalyst mobile robot, as shown in FIG3 , the map construction method comprises:

Step S100: Obtain the displacement, acceleration and speed of the robot; the displacement of the robot can be obtained by the number of rotations of the hub motor, and the acceleration and speed of the robot can be calculated by the elapsed time.

Step S101: The distance and angle between the local feature point and the robot can be obtained through the laser radar 52; wherein, the measurement range of the laser radar 52 is 0 to 360°, the horizontal resolution is 0.2°, and the laser radar 52 can achieve full-circle scanning at a frequency of 10 to 100 Hz.

Step S102: acquiring the real-time position and posture of the local feature points relative to the robot according to the displacement, acceleration, speed of the robot and the distance and angle between the local feature points and the robot, so as to generate a first grid map; the local feature points represent multiple landmarks within the detection range of the laser radar 52;

Step S103: A local image is collected through the depth-of-field camera 51 to generate a local three-dimensional cloud map, wherein the local image represents a three-dimensional image within a local range that can be detected by the depth-of-field camera 51, and a surface normal map is calculated through scene surface points to estimate the real-time posture of the robot. According to the real-time posture of the robot, nonlinear optimization is performed, and then loop detection is performed; finally, a local three-dimensional cloud map is established;

Step S104: performing optimization processing in the local three-dimensional cloud map to eliminate false obstacle information, and projecting the optimized three-dimensional cloud map to form a second grid map; the first grid map and the second grid map are both 2D maps;

Step S105: fusing the first raster image with the second raster image to generate an indoor map;

Among them, the first grid map and the second grid map are both composed of multiple grids, and the status of each grid is occupied or empty; for example, in a certain grid area in the first grid map, the ratio of the occupied area to the total grid area is higher than a preset threshold (for example, 50%), which means that the status of this grid is occupied, and the ratio of the occupied area to the total grid area is lower than the preset threshold, which means that the status of this grid is empty.

If the states of the grids of the first grid map and the second grid map are both empty, the state of the grid is determined to be empty;

If the states of the grids in the first grid map and the second grid map are both occupied, the state of the grid is determined to be occupied;

If the state of the grid of the first grid map is occupied and the state of the grid of the second grid map is empty, the state of the grid is determined to be occupied;

If the state of the grid of the first grid map is empty and the state of the grid of the second grid map is occupied, the state of the grid is determined to be occupied.

When performing data association in practical applications, we may encounter the following problems:

1. Maybe a certain feature point was seen last time, but not seen next time;

2. You may see the feature point this time, but you may never see it again;

The above problems will cause serious problems for our navigation and map drawing; since both the depth of field camera 51 and the laser radar 52 monitoring have certain detection blind spots, by fusing the first raster map and the second raster map, the detection blind spots are reduced, the integrity of the detected feature points is improved, and the above two problems are greatly avoided.

The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims (4)

1. A photocatalyst mobile robot, comprising: Shell: including an outer shell and a chassis arranged at one end of the outer shell; A wireless communication module, disposed in the housing, for wirelessly connecting to an external terminal; A moving component, mounted on the chassis, for moving the robot; A purification component, comprising an air inlet and an air outlet arranged on the surface of the housing; and an air purifier installed in the housing; A sensor module, comprising a depth-of-field camera and a laser radar arranged on the housing, wherein the depth-of-field camera is used to obtain a local three-dimensional cloud image, and the laser radar is used to obtain a distance between a local feature point and the robot; A filter is disposed in the housing and is used to obtain the real-time position and posture of the local feature point relative to the robot according to the motion state of the robot; the motion state of the robot includes the acceleration, angular velocity and displacement of the robot in the current state; The ROS module is disposed in the housing and is used to construct an indoor map according to the local three-dimensional cloud map and the distance between the local feature point and the robot; and to move to a target position in the indoor map according to the instruction of the external terminal to drive the purification component to purify the air; The indoor map construction comprises: Get the displacement, acceleration and speed of the robot; The distance and angle between the local feature point and the robot are obtained through the laser radar; According to the displacement, acceleration, speed of the robot and the distance and angle between the local feature points and the robot, the real-time position and posture of the local feature points relative to the robot are obtained to generate a first grid map; the local feature points represent multiple landmarks within the detection range of the laser radar; The local image is collected through the depth-of-field camera to generate a local three-dimensional cloud image; Projecting the local three-dimensional cloud image to form a second grid image; Merging the first raster image with the second raster image to generate an indoor map; Wherein, the first grid map and the second grid map are both composed of a plurality of grids, and the status of each grid is occupied or empty; If the states of the grids of the first grid map and the second grid map are both empty, the state of the grid is determined to be empty; in other cases, the state of the grid is determined to be occupied. 2. The photocatalyst mobile robot according to claim 1 is characterized in that the sensor module also includes an ultrasonic module and a cliff sensor, the ultrasonic module is used to detect obstacles in front of the robot, and the cliff sensor is used to detect the height difference of the working platform. 3 . The photocatalyst mobile robot according to claim 2 , wherein the ultrasonic module comprises three ultrasonic detectors, and the ultrasonic detectors are arranged on the shell. 4. The photocatalytic mobile robot according to claim 1, characterized in that the mobile component comprises two driving wheels, a hub motor disposed inside the driving wheels, and four auxiliary universal wheels disposed around the chassis.