CN108363395A - A kind of method of AGV automatic obstacle avoidings - Google Patents

Abstract

An AGV autonomous obstacle avoidance method relates to the AGV. Set up a laser sensor, combined with the method of short-term memory, use the laser sensor to collect the obstacle distribution of the scene around the AGV in real time, design the obstacle avoidance algorithm and formulate the obstacle avoidance strategy according to the collected obstacle distribution, and control the AGV to find a feasible channel , bypass obstacles, reach the target location, and realize autonomous obstacle avoidance. AGV can automatically avoid obstacles in a more complex environment for barrier-free driving, reducing the possibility of accidents, selecting the optimal channel, and greatly improving the working efficiency of AGV.

Description

A method for autonomous obstacle avoidance of AGV

technical field

The invention relates to an AGV, in particular to a method for autonomous obstacle avoidance of the AGV.

Background technique

The first AGV was born in 1953. AGV has been used as a logistics intelligent robot for more than 60 years, and the application of AGV is becoming more and more extensive. AGV is equipped with automatic guidance devices such as electromagnetic or optical. It can drive along the specified guiding path, which greatly meets the automation needs of handling production, reduces labor costs, and improves production efficiency. However, during the operation of the car, there may be obstacles in front of the car that may affect the driving or even cause a collision. Therefore, providing a method that can automatically avoid obstacles can improve production efficiency and reduce the time of staying due to obstacles. .

Chinese patent CN106774313A discloses a multi-sensor based outdoor automatic obstacle avoidance AGV navigation method, which includes the following steps: calculate the shortest route according to the local route planning map and the target starting point and end point; use the laser radar module to detect the surrounding environment, and detect the obstacles Carry out avoidance; compare the correct driving direction of the road with the direction angle of the current head of the car obtained by the electronic compass to obtain the corrected angle θ ₁ of the driving direction of the car; use the camera module to identify the road marking line, analyze and obtain the corrected angle θ ₂ of the driving direction of the car; Process θ ₁ and θ ₂ to obtain the optimal angle θ in different environments; the industrial computer processes the relevant parameters, and makes the car move forward through the wireless module and the drive module, and coordinates planning and detection through the coordinator. The invention can realize outdoor precise automatic obstacle avoidance navigation in complex situations.

Contents of the invention

The purpose of the present invention is to provide an AGV that can accurately avoid obstacles in the presence of obstacles and drive normally around obstacles in order to solve the shortcomings of AGVs that encounter obstacles in front of them that affect driving or even cause collisions during operation. A method of autonomous obstacle avoidance.

The present invention comprises the following steps:

Set up a laser sensor, combined with the method of short-term memory, collect the obstacle distribution of the scene around the AGV in real time through the laser sensor, design the obstacle avoidance algorithm and formulate the obstacle avoidance strategy according to the collected obstacle distribution, and control the AGV to find a feasible channel , bypass obstacles, reach the target location, and realize autonomous obstacle avoidance.

The laser sensor emits laser beams to obtain the distribution information of surrounding obstacles. The fan-shaped area with a radius of 5m and 180° directly in front of the laser sensor is used as the detection range, and the fan-shaped area is simplified into a rectangular area to reduce the amount of calculation.

The data collected by the laser sensor is constantly changing, and the rectangular area can be expanded. When updating a frame of data, the grid data is selectively deleted according to the current coordinate offset, and new data is added to the grid. Update the grid data to obtain the distribution of obstacles around the AGV.

The laser sensor obtains the polar coordinates of the obstacle distribution in the surrounding scene, and obtains the Cartesian coordinates (x, y) according to the polar coordinates, and then calculates the obstacle distribution in the rectangular area. If there is no obstacle, write 0; if there is an obstacle object, write 1.

To design an obstacle avoidance algorithm and formulate an obstacle avoidance strategy, first determine the boundary scene, enter an equal-scale on-site road map, and ensure that the AGV runs within the boundary wall; then update the grid according to the navigation sensor and the obstacle avoidance laser, and the navigation laser sensor passes through the reflection The board and triangular positioning principle obtains the current AGV coordinates, performs coordinate transformation, updates the grid, and adds new obstacles to the grid; then finds an alternative channel, considering the radius R1 of the body and the minimum distance d1 between the obstacle and the body, the The size of the obstacle is expanded to R1+d1, and the grid within the 360-degree range is divided into 360/α sectors with a fixed angle α as the unit sector, and all sectors are traversed. The sectors without obstacles are reserved Select the channel; finally find the optimal channel, remove the infeasible channels from the alternative channels, construct a cost function in the remaining feasible channels, and select the optimal channel.

The outstanding effect of the present invention is: the present invention combines the method of short-term memory to collect the obstacle distribution of the scene around the AGV in real time through the laser sensor, and according to the collected obstacle distribution, design an obstacle avoidance algorithm, formulate an obstacle avoidance strategy, and control the AGV Find feasible passages independently, bypass obstacles, reach the target location, and realize autonomous obstacle avoidance. The present invention can automatically avoid obstacles for the AGV to run without obstacles in a relatively complex environment, reduces the possibility of accidents, selects the optimal channel, and greatly improves the working efficiency of the AGV.

Description of drawings

FIG. 1 is a schematic diagram of a laser scanning fan-shaped area according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of a simplified rectangular area for laser scanning according to an embodiment of the present invention.

FIG. 3 is an enlarged circular area of an obstacle according to an embodiment of the present invention.

Fig. 4 is a schematic diagram of an optional channel obtained by considering a kinematic model according to an embodiment of the present invention.

Detailed ways

The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

In the embodiment of the present invention, one laser sensor is set, combined with the method of short-term memory, the obstacle distribution of the scene around the AGV is collected in real time through the laser sensor, and the obstacle avoidance algorithm is designed according to the collected obstacle distribution, and the obstacle avoidance strategy is formulated to control AGV independently finds feasible channels, bypasses obstacles, reaches the target location, and realizes autonomous obstacle avoidance.

The laser sensor can emit laser beams to obtain the distribution information of surrounding obstacles. The fan-shaped area 180° in front of the laser sensor with a radius of 5m is used as the detection range, as shown in Figure 1. Since the fan-shaped area has a large amount of calculation when designing the algorithm, the fan-shaped area is simplified into a rectangular area, and a rectangular area is expanded outward according to the size of the AGV (for example, the rectangle is 5m long and 5m wide), and the rectangular area is divided into grids ( For example, a grid with a side length of 10cm) is shown in Figure 2. According to the data collected by the laser, the obstacles are mapped to the grid one by one, which is a static frame of data.

The AGV is constantly moving, and the navigation laser sensor obtains the current AGV coordinates through the principle of reflectors and triangulation, transforms the coordinates, updates the grid, and adds new obstacles to the grid. The laser sensor obtains the polar coordinates of the obstacle distribution in the surrounding scene, and obtains the Cartesian coordinates (x, y) according to the polar coordinates, and then calculates the obstacle distribution in the rectangular area. Write 0 if there is no obstacle, and write 1 if there is an obstacle.

Considering the radius R1 of the vehicle body and the minimum distance d1 between the obstacle and the vehicle body, the size of the obstacle is expanded to R1+d1. Taking the obstacle as the base point, the enlarged circular area is considered as the obstacle area, as shown in Figure 3. Set all the values in the circular area to 1, indicating that there are obstacles.

Divide the grid in the range of 360 degrees into 360/alpha sectors with a fixed angle alpha as the unit sector, for example, divide 5 degrees into 1 sector and divide it into 72 sectors. Traversing all sectors, find the sectors that are not blocked by obstacles. Sectors that are not blocked by obstacles are candidate channels.

According to the AGV kinematics model, the AGV cannot move in all directions and must meet the minimum turning radius limit, so some feasible channels can be removed. As shown in Figure 4, point O is the center of the circle when the AGV rotates in situ, and the radius of rotation is R. Since the arc intersects the boundary of the obstacle on the right (right), that is, the distance d from the center of the circle to the center of the obstacle is less than R+R1+d1, the passage below the obstacle on the right cannot pass. Since the arc does not intersect with the boundary of the obstacle on the left (left), that is, the distance from the center of the circle to the center of the obstacle is greater than R+R1+d1, the passage below the obstacle on the left can pass.

In the remaining feasible channels, the AGV attitude of the next sampling interval is calculated according to the current AGV attitude and the linear velocity and angular velocity of the next sampling interval. By calculating whether the AGV will collide with obstacles, the possibility of collision can be further removed. aisle.

According to the selected channel direction, the linear velocity and angular velocity of the next sampling interval are calculated, and the theoretical position of the AGV of the next sampling interval is calculated according to the linear velocity and angular velocity. Place the AGV in the matrix of obstacles. If the AGV overlaps with the obstacle, it means that the AGV will collide with the obstacle. This channel is impassable and should be discarded. If the calculated AGV theoretical position does not collide with obstacles, it means that this channel is available.

After the above layers of screening, the remaining channels are feasible channels. By constructing a cost function, the optimal channel is selected, and the driving reaches the destination site to realize autonomous obstacle avoidance.

Claims (5)

1. a kind of method of AGV automatic obstacle avoidings, it is characterised in that be including specific method：1 laser sensor is set, in conjunction with short The method of phase memory, the distribution of obstacles situation of AGV surrounding scenes is acquired by laser sensor, according to collected barrier in real time Hinder object distribution situation, design obstacle avoidance algorithm, formulate Robot dodge strategy, control AGV finds Feasible channel, and cut-through object reaches mesh Place is marked, realizes automatic obstacle avoiding.

2. a kind of method of AGV automatic obstacle avoidings as described in claim 1, it is characterised in that the laser transmitter projects laser Beam obtains the distributed intelligence of peripheral obstacle, using 180 ° immediately ahead of laser sensor, the sector region of radius 5m is as detecting model It encloses, sector region, which is reduced to rectangular area, reduces calculation amount.

3. a kind of method of AGV automatic obstacle avoidings as described in claim 1, it is characterised in that the data of the laser sensor acquisition It constantly changes, rectangular area is expanded, when updating a frame data, according to changing coordinates offset selectively removing grid Data, and grid is added in new data, it updates raster data always during exercise, obtains the distribution of obstacles feelings on the peripheries AGV Condition.

4. a kind of method of AGV automatic obstacle avoidings as described in claim 1, it is characterised in that the laser sensor obtains periphery The polar coordinates of scape distribution of obstacles obtain cartesian coordinate (x, y) according to polar coordinates, then calculate the barrier of rectangular area Distribution, if without barrier, is written 0；If there is barrier, 1 is written.

5. a kind of method of AGV automatic obstacle avoidings as described in claim 1, it is characterised in that the design obstacle avoidance algorithm, formulation are kept away Barrier strategy, it is first determined boundary scene, the site road map of typing equal proportion ensure that AGV is run in boundary wall；Then root Grid is updated according to navigation sensor and avoidance laser, navigation laser sensor obtains current by reflecting plate and triangulation location principle AGV coordinates, are coordinately transformed, and update grid, and new barrier is added in grid；Alternative channel is found again, it is contemplated that vehicle Barrier size is expanded to R1+d1 by the minimum range d1 of body radius R1 and barrier and vehicle body outward, will be within the scope of 360 degree Grid using fixed angle α as unit sector, be divided into 360/ α sector, traverse all sectors, the sector that no obstacle blocks All it is alternative channel；Finally find optimal channel, in alternative channel removal can not row of channels, construct 1 in remaining Feasible channel A cost function carries out the selection of optimal channel.