CN113515114A - Navigation obstacle avoidance trolley system - Google Patents

Abstract

The invention provides a navigation obstacle avoidance trolley system which comprises a trolley body, a single chip microcomputer, an optical flow module, a ranging module, a gyroscope, a WIFI module, a motor driving module, a power supply, an android application and an upper computer. The device is characterized in that the distance measuring module consists of two ultrasonic modules which are symmetrical left and right and rotate inwards for a certain angle, a platform for placing the ultrasonic modules, two steering engines connected with the ultrasonic modules and a steering engine connected with the platform; the android application acquires RSSI information by calling the mobile phone context, WIFI-SERVICE system SERVICE and sends the RSSI information to the upper computer for image building; the upper computer performs indoor positioning on the trolley by adopting WIFI fingerprint algorithm fusion to obtain relative displacement of the sky light stream Lucas-Kanade algorithm, performs dynamic path planning by fusing BUG2 algorithm and A-star algorithm, and controls the trolley to navigate and avoid obstacles.

Description

Navigation obstacle avoidance trolley system

Technical Field

The invention relates to the technical field of intelligent robots, in particular to an ultrasonic obstacle avoidance device, an obstacle avoidance algorithm and WIFI fingerprint indoor positioning.

Background

In the moving process of the trolley, the obstacle needs to be detected, otherwise, subsequent operations of avoiding the obstacle and the like cannot be triggered, and risks exist. There are many techniques mainly used for detecting obstacles on a trolley, such as laser radar, ultrasonic waves, depth cameras and the like, and an ultrasonic sensor is a scheme with lower cost.

The existing scheme of detecting the obstacle by adopting ultrasonic waves comprises a second scheme, wherein an ultrasonic wave module is arranged in the moving direction of the trolley, and the ultrasonic wave module sends out ultrasonic waves, reflects the ultrasonic waves after contacting the obstacle and is received by the module. When the wave speed is known, the distance between the trolley and the obstacle can be calculated according to the time of the echo. Because the measurement angle of the ultrasonic module is about 15 degrees, if the scheme is adopted, the trolley cannot react to the obstacle which is out of the measurement angle but interferes the working of the trolley, and adverse results are caused.

Secondly, a large number of ultrasonic modules are adopted to cover all angles, so that the detection of distances in all directions can be realized, but the power consumption is large, the working delay of the system is increased, and the work in a dynamic environment is not facilitated.

The Bug2 algorithm is a commonly used robot obstacle avoidance algorithm. The mechanism of the algorithm is as follows: and drawing a straight line between the current position and the target position, enabling the trolley to detour the obstacle from the left side or the right side, and when the trolley moves to any point on the straight line, considering that obstacle avoidance is finished. The Bug2 algorithm has good throughput for small obstacles, but does not perform well for large obstacles, and for processing in complex environments.

When a wireless Access Point (AP) transmits a signal, the electromagnetic wave contains many pieces of information, where the configuration information mainly includes 4 SSID (describing an AP name), BSSID (which can be understood as an AP MAC address), network address (AP digital ID), RSSI (officially called level, which describes a value of the WIFI signal strength and is key information for positioning).

WIFI positioning is an indoor positioning technology widely applied, and the main algorithm of the WIFI positioning is two, namely triangle and fingerprint position identification. The triangle algorithm determines the distance to the AP based on the signal strength (RSSI) received by the cart from the AP. When the AP is more than three, the cart can learn its approximate position. The fingerprint position identification algorithm is that the signal intensity of the AP is read by the trolley and stored in the database as the fingerprint of the position, and then the trolley goes to the next position until the fingerprints of all the positions in the area are stored in the database. When the system works, the trolley reads the signal intensity of the AP and compares the signal intensity with the fingerprints in the database, so that the current position is obtained.

For both algorithms, the more APs, the smaller the error. The triangle algorithm needs to acquire the position of the AP, and fingerprint position identification is not needed, so the latter is a better choice, but the average error is also 1-2m, and a lot of time is needed for fingerprint matching, which is not beneficial to work in a dynamic environment.

Disclosure of Invention

The invention provides a navigation obstacle avoidance trolley system. The device can be used in indoor environments such as supermarkets and warehouses, and has the functions of indoor positioning, navigation, obstacle avoidance and the like. The traditional ultrasonic ranging scheme is optimized, and the range of ultrasonic obstacle detection is wider; the obstacles are classified, and the obstacle avoidance path is shorter; the WIFI positioning and the sky light stream are integrated, the indoor positioning is more accurate, and the speed is faster.

The invention provides a navigation obstacle avoidance trolley system which comprises a trolley body.

The automobile body, including the bottom plate, roof and set up in the mecanum wheel of base side.

And the power supply module is arranged at the tail part of the bottom plate, is connected with the singlechip and is used for supplying power.

And the single chip microcomputer is arranged in the middle of the bottom plate, is connected with each sensor and the WIFI module, and drives the trolley to move according to data of each sensor and instructions of the upper computer.

And the WIFI module is arranged at the tail part of the top plate, is connected with the single chip microcomputer and is used for communication between the single chip microcomputer and an upper computer.

And the distance measuring module is arranged at the head of the top plate, is connected with the single chip microcomputer and is used for acquiring the distance between the barrier and the vehicle body. The ultrasonic module is arranged by two bilateral symmetries and rotates a certain angle inwards, the platform for arranging the ultrasonic module is arranged, the two steering gears connected with the ultrasonic module and the steering gear connected with the platform form, and the steering gears can control an object connected with the steering gears to rotate.

And the motor driving module is arranged at the head of the bottom plate, is connected with the single chip microcomputer and the motor, receives the PWM signal from the single chip microcomputer and controls the rotating speed of the motor.

The motors are arranged on two sides of the bottom plate, and are four in number, so that the Mecanum wheels are driven to realize omnidirectional movement.

The optical flow module is arranged above the camera of the top plate tail module, is connected with the single chip microcomputer and is used for acquiring the displacement of the trolley;

and the gyroscope is arranged in the middle of the top plate, is connected with the single chip microcomputer and is used for acquiring the acceleration of the trolley.

The android application is communicated with the upper computer through a route and used for building a picture and sending the expected position of the trolley to the upper computer.

The upper computer is communicated with the trolley and the android through a route, stores a scene map, plans a path according to the current position of the trolley and an expected position from the android application, obtains the path and sends the path to the trolley.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.

Fig. 1 is a schematic diagram of a whole vehicle structure of a trolley in the navigation obstacle avoidance trolley system of the invention.

Fig. 2 is a schematic structural diagram of a car roof in the navigation obstacle avoidance car system of the present invention.

FIG. 3 is a schematic structural view of a bottom plate of a dolly in the navigation obstacle avoidance dolly system of the invention.

Fig. 4 is a connection diagram of each part of a trolley in the navigation obstacle avoidance trolley system.

Fig. 5 is a schematic diagram of a distance measuring module of a trolley in the navigation obstacle avoidance trolley system of the invention.

Fig. 6 is a schematic diagram comparing a distance measurement scheme in the navigation obstacle avoidance vehicle system of the present invention with an existing distance measurement scheme.

Fig. 7 is a flowchart of a tag module program of a trolley obstacle avoidance (navigation) part in the navigation obstacle avoidance trolley system of the invention.

Fig. 8 is a flow chart of a mode module program of a trolley obstacle avoidance (navigation) part in the navigation obstacle avoidance trolley system of the invention.

Fig. 9 is a flowchart of a move module program of a trolley obstacle avoidance (navigation) part in the navigation obstacle avoidance trolley system of the invention.

Fig. 10 is a schematic diagram of a process of obstacle avoidance of a vehicle in the navigation obstacle avoidance vehicle system based on a bug2 algorithm.

Fig. 11 is a schematic diagram of an optimized obstacle avoidance scheme and an original scheme of a trolley in the navigation obstacle avoidance trolley system of the invention.

Fig. 12 is a flowchart of drawing construction by using an android application in the navigation obstacle avoidance vehicle system of the present invention.

Fig. 13 is a flow chart of indoor positioning in the navigation obstacle avoidance vehicle system of the present invention.

Fig. 14 is a flow chart of path planning in the navigation obstacle avoidance vehicle system of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention.

It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.

Optimally, the composition and the connection mode of the trolley are shown in the figures 1, 2, 3 and 4.

And the distance measuring module 1 is arranged at the head part of the top plate 5, is connected with the singlechip 7 and is used for detecting obstacles.

And the WIFI module 2 is arranged at the tail part of the top plate 5, is connected with the single chip microcomputer 7 and is used for interaction between the single chip microcomputer and an upper computer.

And the optical flow module 3 is arranged at the tail part of the top plate 5, is connected with the singlechip 7 and is used for acquiring the displacement of the trolley.

And the gyroscope 4 is arranged in the middle of the top plate, is connected with the singlechip 7 and is used for acquiring the acceleration of the trolley.

And the top plate 5 is used for placing the ranging module 1, the WIFI module 2, the optical flow module 3 and the gyroscope 4.

And the power supply 6 is arranged at the tail part of the bottom plate, is connected with the singlechip 7 and the motor driving module 8 and is used for supplying power.

And the single chip microcomputer 7 is arranged in the middle of the bottom plate, is connected with each sensor and the WIFI module 2, and drives the trolley to move according to data of each sensor and instructions of the upper computer.

And the motor driving module 8 is arranged at the head of the bottom plate, is connected with the singlechip 7 and the motor 9, receives the PWM signal from the singlechip 7 and controls the rotating speed of the motor.

And the motors 9 are arranged on two sides of the bottom plate, and are four in number, so that the Mecanum wheels 10 are driven to realize omnidirectional movement.

And a mecanum wheel 10 connected to the motor 9 for movement.

And the bottom plate 11 is used for placing the power supply 6, the singlechip 7, the motor driving module 8 and the motor 9.

And the copper column 12 is used for connecting the bottom plate 11 and the top plate 5.

Preferably, the ranging module is as shown in fig. 5.

And the ultrasonic module 1-1 is used for measuring the distance between the trolley and the obstacle.

And the platform 1-2 is used for placing the ultrasonic module.

And the steering engine 1-3 is used for controlling steering. The steering gears on the two sides can control the ultrasonic modules to steer, and the steering gear in the center can control the platform to steer.

In the distance measuring module, the steering engines on two sides deflect towards the middle. And taking the motion direction as a y axis, setting the maximum length of the vehicle body as x, and setting the safety distance expected by a user as y, wherein the deflection angle of the left steering engine is arctan (y/x), and the deflection angle of the right steering engine is arctan (180-y/x). When the motion direction changes, the steering engine in the middle part controls the platform to turn, thereby realizing omnibearing obstacle detection.

As shown in fig. 6, the distance measuring scheme has a better detection effect on the obstacle on the side surface in the moving direction of the vehicle body, the left side of the figure is the distance measuring scheme in the invention, and the right side of the figure is a conventional scheme. On the left, the measurement range 2 of the ultrasound module 1 covers an obstacle 4 located within the vehicle body 3, and on the right the measurement range 2 of the ultrasound module 1 does not cover an obstacle 4 located within the vehicle body 3.

The ranging scheme has the advantages over the conventional scheme that: visual field blind areas are reduced, and obstacles on the side surface in the motion direction can be detected; the two-dimensional coordinates of the obstacle can be determined by measuring the distance value through the ultrasonic module, so that the following obstacle avoidance operation is facilitated. The defects are that: in the direction of motion, the middle portion has a smaller range.

In the above embodiment, the obstacle avoidance program is divided into a tag module, a mode module, and a move module.

The Tag module is used to determine the obstacle roughly, see fig. 7.

Tag =0 indicates no obstacle, Tag =1 indicates one side having an obstacle, and Tag =2 indicates both sides having an obstacle. In order to prevent errors caused by small measuring range of the distance measuring module for aligning the front obstacle, when the ultrasonic distance measuring at the two sides are smaller than the safe distance, the trolley is enabled to transversely move, and the measurement is repeated once.

And the Mode module acquires the working state according to the tag value, as shown in figure 8.

Mode =0 indicates normal traffic, Mode =1 indicates traffic from the right side of the obstacle, Mode =2 indicates traffic from the left side of the obstacle, and Mode =3 indicates failure to determine the traffic direction.

For tag =1, the system judges that a detonable route exists, the distance measurement is continuously repeated within 2s, if the obstacle disappears in 2s, the system judges that the vehicle can normally pass, and extra movement of the vehicle is reduced; and if the obstacle exists continuously, judging that the obstacle avoidance is needed.

The Move module controls the motor to work according to the mode value, and the figure 9 shows.

The obstacle avoidance procedure is intended to avoid an obstacle that is not present in the map and that temporarily appears. Taking a supermarket environment as an example, passing customers or temporarily placed goods belong to temporary obstacles, and areas where goods shelves and the like cannot pass belong to known obstacles in a map and are avoided in route planning.

For the side, the invention carries out obstacle avoidance based on the Bug2 algorithm. OA _ right () and OA _ left are functions of the circumvention barrier. Due to the unique design of the distance measuring device, the two ultrasonic modules can be controlled to point to any direction. The left-hand bypass example shows the bypass process in fig. 10.

The distance measuring module utilizes a steering engine to enable the two ultrasonic modules 1 to point to the motion direction and the obstacle 4 direction respectively, and the moving direction and the speed of the trolley are calculated according to the distance measured by the two ultrasonic modules, so that the scheme can be adopted for curved obstacles.

Due to the bug of the bug2 algorithm, for an obstacle that fails to determine the direction of travel (mode = 3), the car may bypass the known obstacle in the map and cause the travel path 7 to be further away during the travel from the start point 5 to the end point 6 due to the presence of the obstacle 4, as shown in fig. 11. Under the condition, the trolley uploads the two-dimensional coordinates of the obstacle through the route, the upper computer performs route planning and then sends the route 8 to the trolley, and the trolley moves according to the route, so that the purpose of avoiding the obstacle is achieved.

The embodiment carries out mapping through android application, and the principle is that the WIFI _ STATE authority in a mobile phone is started, then the system SERVICE of context, WIFI _ SERVICE is obtained from java codes of main activities, and then a getScanResult method is called to return a List container containing the ScanResult. This class contains various information about wifi that can be scanned nearby.

Referring to fig. 12, the flow of the android application workflow is as follows.

Let there be three APs in the scene, denoted AP1, AP2, and AP 3. When a mapping instruction (the instruction consists of a flag bit and a two-dimensional coordinate (x, y)) from the upper computer is received, the wifi signals near the current point are rescanned, ScanResult is returned again, and after verification with the previously selected AP name, the RSSI values of all selected wifi are obtained. This scan time lasts 30s, RSSI is acquired every second and stored in the LIST container, and after the 30 second scan is completed, the 30 values are averaged to obtain the wifi fingerprint of the point (SS 1, SS2, SS 3).

After the fingerprint acquisition is finished, the passing difficulty of the position can be set. The pass difficulty is set to be an integer from 0 to 9. 0 means completely unobstructed and can pass through. 9 indicates complete failure. The integer between 0 and 9 indicates passable, but with different difficulty of passage. In the actual working environment, the areas with high difficulty corresponding to dense people streams and the areas with low difficulty corresponding to rare people streams.

After all work of the current point is finished, the position fingerprints and the position coordinates are integrated into an array through difficulty aggregation, the array is sent to an upper computer through a route, then the establishment of the next position point is carried out until all positions are stored in a map, and the map establishment is finished.

The embodiment adopts wifi fingerprint and Lucas-Kanade optical flow algorithm to carry out indoor positioning, and the specific flow is shown in figure 13.

Let there be three APs in the scene, denoted AP1, AP2, and AP 3. In a working state, the upper computer sends a request for obtaining RSSI to the trolley at intervals, after the request of the upper computer is received, the single chip microcomputer reads RSSI values of three APs, the RSSI values are recorded as arrays (SS 1, SS2 and SS 3), and the average value is obtained by reading for many times. The optical flow module sends position offset (offset) to the single chip microcomputer by using a Lucas-Kanade optical flow algorithm, the single chip microcomputer adds the offset into an array (SS 1, SS2, SS3 and OS), finally adds a state bit, sets a navigation mode to be state 2 (SS 1, SS2, SS3 and OS and 2), then sends the group of data to an upper computer by using a WiFi module, and the trolley and the upper computer interact through a route.

The upper computer stores the scene map and stores the scene map by using the mysql database. Each point in the map has information as: RSSI fingerprint information, two-dimensional coordinates (x, y), pass difficulty. The upper computer receives an array (SS 1, SS2, SS3, OS, 2) from a trolley, firstly judges whether the position of the previous period is empty, if not, a coordinate A (x 1, x 2) is calculated by using an OS value, then (SS 1, SS2, SS 3) is compared with RSSI fingerprint information of all points within the range of 1-2m taking A as the midpoint, a point B (x 2, x 3) with the minimum distance is selected by weighting, and then a (x 1, y 1) and B (x 2, y 2) are used for calculating the current position C (x 3, y 3), wherein the calculation method comprises the following steps: c = (K × a + B)/(K + 1).

K is a proportionality coefficient, the larger K is, the more trusted the position obtained by the optical flow is, and the smaller K is, the more trusted the position obtained by the wifi fingerprint is. Due to the fact that the illumination conditions and the signal intensity are different, the trolley works in different environments, the possible position A is more accurate, the possible position B is more accurate, and the K value can be changed on the upper computer for calibration.

If the position of the previous cycle is empty, which indicates that the current cycle is the first cycle of the system, or there is data packet loss, comparing (SS 1, SS2, SS 3) with all points in the map, and selecting the point with the minimum distance as the current position C.

The embodiment adopts an a-algorithm to perform path planning, and the specific flow is shown in fig. 14.

After the current position C is calculated, the navigation mode is determined because the status bit is 2. Two positions are defined in the navigation, a final desired position EXP and a short-term desired position EXP0. The EXP can be acquired from a mobile phone end and can also be set on the upper computer.

Firstly, judging whether the EXP is set, and if the EXP is empty, sending a standby instruction to the trolley; if the EXP is not empty, determining whether the distance between C and the EXP is acceptable.

If the vehicle is acceptable, the vehicle task is judged to be completed, and a standby instruction is sent to the vehicle; if the distance between C and EXP is not acceptable, whether EXP0 is set is judged.

If the EXP0 is empty, the optimal path point set is calculated by the A-x algorithm, and a second-order array [ (x, y) ] taking the EXP as a final term is obtained. The upper computer sends the first group of coordinates of the array to the trolley, and the coordinates are set to be short-term expected positions EXP 0; if the EXP0 is not empty, whether the distance between the C and the EXP0 is acceptable or not is judged, if not, the A-x algorithm is reused to obtain the EXP0, if the distance between the C and the EXP0 is acceptable, the EXP0 is updated to be the second item of the array [ (x, y) ], then the first item of the array is deleted, and the rest items are shifted forward by one bit.

And finally, the upper computer sends the EXP0 to the trolley, the trolley moves according to the received data, and the navigation part is completed.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. The utility model provides a navigation obstacle avoidance dolly system which characterized in that includes:

the vehicle body comprises a bottom plate, a top plate and Mecanum wheels arranged on the side face of the base;

the single chip microcomputer is arranged in the middle of the bottom plate, is connected with each sensor and drives the trolley to move according to data of each sensor and instructions of the upper computer;

the motor driving module is arranged at the head of the bottom plate, is connected with the single chip microcomputer and the motor, receives a PWM signal from the single chip microcomputer and controls the rotating speed of the motor;

the motors are arranged on two sides of the bottom plate, and the number of the motors is four, so that the Mecanum wheels are driven to realize omnidirectional movement;

the gyroscope is arranged in the middle of the top plate, is connected with the single chip microcomputer and is used for acquiring the acceleration of the trolley;

the distance measuring module is arranged at the head of the top plate, connected with the single chip microcomputer and used for obtaining the distance between the barrier and the vehicle body;

and the power supply module is arranged at the tail part of the bottom plate, is connected with the singlechip and is used for supplying power.

2. The navigation obstacle avoidance trolley system of claim 1, wherein the distance measurement module comprises two ultrasonic modules which are symmetrical left and right and rotate inwards for a certain angle, a platform for placing the ultrasonic modules, two steering engines connected with the ultrasonic modules and one steering engine connected with the platform.

3. The navigation obstacle avoidance vehicle system of claim 1, further comprising:

the WIFI module is arranged at the tail of the top plate, is connected with the single chip microcomputer and is used for communication between the single chip microcomputer and an upper computer;

the optical flow module is arranged above the camera of the top plate tail module, is connected with the single chip microcomputer and is used for acquiring the displacement of the trolley;

the android application is communicated with the upper computer through a route and used for establishing a picture and sending the expected position of the trolley to the upper computer;

the upper computer is communicated with the trolley and the android through a route, stores a scene map, plans a path according to the current position of the trolley and an expected position from the android application, obtains the path and sends the path to the trolley.

4. The navigation obstacle avoidance vehicle system of claim 3, wherein the upper computer stores a WIFI fingerprint map, each point in the map having an RSSI fingerprint, two-dimensional coordinates and a passing difficulty.

5. The navigation obstacle avoidance trolley system of claim 4, wherein the upper computer can set an expected position, and the upper computer performs dynamic path planning based on an A-x algorithm to update the expected displacement of the trolley in real time.

6. The navigation obstacle avoidance trolley system of claim 5, wherein the upper computer can obtain the desired position through android applications.

7. The navigation obstacle avoidance trolley system of claim 3, wherein the android application obtains the RSSI value through a CONTEXT.

8. The navigation obstacle avoidance trolley system of claim 3, wherein the single chip microcomputer controls the trolley to independently avoid partial small obstacles based on a bug2 algorithm; and interacting with an upper computer, and avoiding the large-sized obstacle based on an A-star algorithm.

9. The navigation obstacle avoidance trolley system of claim 3, wherein the upper computer obtains an optical flow position according to the data obtained by the optical flow module and the trolley position in the previous period, and performs data RSSI value matching by using the optical flow position as a center to obtain a WIFI fingerprint position.

10. The navigation obstacle avoidance trolley system of claim 3, wherein the upper computer obtains the current position according to the optical flow position and the WIFI fingerprint position by weighting.

Images