CN115097833A - Automatic obstacle avoidance method and system for pesticide application robot and storage medium - Google Patents
Automatic obstacle avoidance method and system for pesticide application robot and storage medium Download PDFInfo
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Abstract
The invention discloses an automatic obstacle avoidance method, system and storage medium of a pesticide application robot, comprising the following steps: acquiring a target pesticide application operation area plan, and acquiring path information according to the target pesticide application operation area plan; acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, performing preliminary path planning, and acquiring an optimal path according to the preliminary path planning; enabling the pesticide applying robot to reach a designated working point through the optimal path, carrying out environment perception through machine vision in pesticide applying work, judging barrier point information according to the passability of the pesticide applying robot, and updating a pesticide applying working area plan through the barrier point information; and (4) carrying out obstacle avoidance and optimal path correction according to the updated pesticide application operation area plan by the current position point of the pesticide application robot and the pesticide sprayed area. The invention realizes automatic obstacle avoidance of the pesticide applying robot by planning the path, avoids repeated spraying of pesticide and improves pesticide applying efficiency.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an automatic obstacle avoidance method and system for a pesticide application robot and a storage medium.
Background
Spraying pesticide is an effective means for preventing and controlling diseases, pests and weeds in agricultural production. Traditional manual application mode, not only the pesticide utilization ratio is low, and the pesticide that deposits simultaneously in soil can cause environmental pollution, and the pesticide that volatilizees in the air can harm health. Therefore, it is important to adopt unmanned drug application operation in facility production, and the drug application robot is one of the important means for realizing the unmanned drug application operation. During the plant protection work period, the plants in the middle and later periods of certain plants are high, the row spacing is narrow, the ground clearance is high, and the like, so that the plant protection device is difficult to develop, the obstacle avoidance effect of most of the current pesticide applying robots is not ideal, the autonomy is not strong, the purpose of obstacle avoidance can be achieved only by manual assistance, and the pesticide applying efficiency is reduced.
In order to realize automatic obstacle avoidance of the pesticide application robot and meet accurate pesticide application under complex terrain conditions, the pesticide application robot needs to be subjected to real-time path planning, and the path planning of the pesticide application robot means that the pesticide application robot autonomously avoids obstacles from the current position, realizes collision-free movement to a target place, and has the shortest path. The existing common obstacle avoidance mode mainly depends on an ultrasonic sensor or an infrared sensor to sense obstacles so as to realize obstacle avoidance, and has the problems of small number of sensors, single obstacle avoidance scheme and low efficiency. Meanwhile, the pre-stored map of the pesticide application robot is a non-real-time map, and the map is difficult to acquire in real time or update.
Disclosure of Invention
In order to solve the technical problems, the invention provides an automatic obstacle avoidance method and system of a pesticide application robot and a storage medium.
The invention provides an automatic obstacle avoidance method of a pesticide application robot, which comprises the following steps:
acquiring a target pesticide application operation area plan, and acquiring path information according to the target pesticide application operation area plan;
acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, performing primary path planning according to the starting point information, the end point information and the environment information and the path information, and acquiring an optimal path according to the primary path planning;
enabling the pesticide application robot to reach a designated working point through the optimal path, carrying out environment perception through machine vision in pesticide application work, judging barrier point information according to the passability of the pesticide application robot, and updating a target pesticide application working area plan by the barrier point information;
and correcting the optimal path through the current position point of the pesticide applying robot and the pesticide spraying area according to the updated target pesticide applying operation area plan.
In the scheme, the method comprises the steps of acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, and performing preliminary path planning according to the starting point information, the end point information and the environment information and the path information, and specifically comprises the following steps:
establishing a grid map of the target pesticide application operation area according to the target pesticide application operation area plan and the environmental information, and presetting the speed constraint, the steering constraint and the maximum safe distance between the pesticide application robot and the obstacle;
performing preliminary path planning through a D-star algorithm and constraint information according to the start point information and the end point information on the grid map, setting a priority queue according to the end point information to perform reverse search in the grid map,
when an obstacle node is detected in the searching process, correcting the path cost of the neighbor grid node, and placing the path cost in a priority queue again until the current position of the pesticide application robot is dequeued from the priority queue;
and selecting the node with the minimum path cost in the neighbor grid nodes to be connected to the starting point information for primarily planning the path, and generating the optimal path.
In the scheme, the environment is sensed through machine vision in the pesticide application work, barrier point information is judged according to the passability of the pesticide application robot, and a target pesticide application work area plan is updated by the barrier point information, specifically:
acquiring video image data in the pesticide application work, preprocessing the video image data to extract color features and texture features, and performing edge segmentation on the preprocessed video image data through the color features and the texture features;
acquiring a binary image of video image data through edge segmentation, acquiring barrier information in the surrounding environment in the pesticide application operation, and acquiring the contour data of a barrier according to the binary image;
judging the passability of the pesticide applying robot according to the contour data of the obstacle, the size information of the pesticide applying robot and a preset safety distance;
and if the pesticide application robot cannot pass through the secondary obstacle, planning an obstacle avoidance path, and updating the obstacle point information in a target pesticide application area plan.
In this scheme, judge the passability of robot of giving medicine to poor free of charge according to the profile data of barrier, the size information of robot of giving medicine to poor free of charge and preset safe distance, specifically do:
obtaining the size parameter of the obstacle according to the contour data of the obstacle, and comparing and judging the size parameter of the obstacle with the maximum width or the maximum ground clearance of the pesticide applying robot;
when the size parameter of the obstacle is larger than the maximum width value or the maximum ground clearance, judging that the obstacle cannot pass;
and when the inclination angle of the vehicle body of the pesticide applying robot is judged to be larger than the preset inclination angle, the pesticide applying robot is proved to be easy to tip over, and the obstacle is judged to be not accessible.
In this scheme, carry out the correction of optimal route through the current position point of the robot that gives medicine to poor free of charge and the regional plan view of operation of giving medicine to poor free of charge according to the operation of giving medicine to poor free of charge after the renewal, specifically do:
carrying out obstacle avoidance path planning according to the relative position of the obstacle point information in the updated target pesticide application operation area plan, acquiring current position information of the pesticide application robot, taking the current position information as a starting point of the obstacle avoidance path planning, and setting a maximum pesticide application radius reachable point behind an obstacle point on the optimal path as an end point of the obstacle avoidance path planning;
acquiring a pesticide application area in front of a path section obstacle point of a current pesticide application robot on an optimal path, marking the pesticide application area, and judging whether a target pesticide application operation area has a pesticide non-application area according to the marked area;
if the non-pesticide-application area exists, the obstacle avoidance path planning is preferentially carried out in the non-pesticide-application area, pesticide application work is carried out in the non-pesticide-application area, and if the non-pesticide-application area does not exist, the obstacle avoidance path planning is carried out according to the shortest path principle;
acquiring an intersection region of an operation region corresponding to an obstacle avoidance path and a mark region in the obstacle avoidance path planning;
when the pesticide application robot is positioned in the intersection region, stopping pesticide application work, and increasing a preset speed increment to drive through the intersection region on the basis of the original running speed;
and updating the pesticide application area according to the obstacle avoidance path planned by the obstacle avoidance path, and correcting the optimal path according to the pesticide application area and the position of the pesticide application robot after obstacle avoidance.
In this scheme, still include:
when a plurality of pesticide applying robots exist in the target pesticide applying working area, displaying the optimal path and real-time position information of each pesticide applying robot in a plan view of the target pesticide applying working area;
judging whether collision occurs or whether the pesticide application working area is repeated or not according to the optimal path through the current position information and the motion speed information of each pesticide application robot;
when collision between the current pesticide application robot and the target pesticide application robot is detected, setting the waiting time of the current pesticide application robot according to the size information and the movement speed information of the target pesticide application robot, and waiting in situ until the target pesticide application robot passes through according to the waiting time;
if the target pesticide application robot does not have a determined motion track, all possible motion areas of the target pesticide application robot at the next moment are taken as obstacle areas, the current position of the current pesticide application robot is taken as a planning starting point, the closest point on the optimal path of the current pesticide application robot behind the obstacle area is taken as an obstacle avoidance terminal point, and obstacle avoidance path planning is carried out;
and when the working areas of the pesticide applying robots are repeated, taking the repeated areas as barrier areas of any pesticide applying robot, and performing secondary planning on the optimal path by combining the current position information of the pesticide applying robot.
The second aspect of the present invention further provides an automatic obstacle avoidance system for a drug delivery robot, the system comprising: the automatic obstacle avoidance method program of the drug delivery robot is executed by the processor to realize the following steps:
acquiring a target pesticide application operation area plan, and acquiring path information according to the target pesticide application operation area plan;
acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, performing primary path planning according to the starting point information, the end point information and the environment information and the path information, and acquiring an optimal path according to the primary path planning;
enabling the pesticide application robot to reach a designated working point through the optimal path, carrying out environment perception through machine vision in pesticide application work, judging barrier point information according to the passability of the pesticide application robot, and updating a target pesticide application working area plan by the barrier point information;
and correcting the optimal path through the current position point of the pesticide applying robot and the pesticide spraying area according to the updated target pesticide applying operation area plan.
In the scheme, the method comprises the steps of acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, and performing preliminary path planning according to the starting point information, the end point information and the environment information and the path information, and specifically comprises the following steps:
establishing a grid map of the target pesticide application operation area according to the target pesticide application operation area plan and the environmental information, and presetting the speed constraint, the steering constraint and the maximum safe distance between the pesticide application robot and the obstacle;
performing preliminary path planning through a D-star algorithm and constraint information according to the start point information and the end point information on the grid map, setting a priority queue according to the end point information to perform reverse search in the grid map,
when an obstacle node is detected in the searching process, correcting the path overhead of the neighbor grid node, and resetting the path overhead into a priority queue until the current position of the pesticide applying robot dequeues from the priority queue;
and selecting the node with the minimum path cost in the neighbor grid nodes to be connected to the starting point information for primarily planning the path, and generating the optimal path.
In this scheme, carry out the correction of optimal route through the regional plan view of operation of giving medicine to poor free of charge after the current position point of robot and the area of having sprayed insecticide according to the renewal, specifically do:
carrying out obstacle avoidance path planning according to the relative position of the obstacle point information in the updated target pesticide application operation area plan, acquiring current position information of the pesticide application robot, taking the current position information as a starting point of the obstacle avoidance path planning, and setting a maximum pesticide application radius reachable point behind an obstacle point on the optimal path as an end point of the obstacle avoidance path planning;
acquiring a pesticide application area in front of a path section obstacle point of a current pesticide application robot on an optimal path, marking the pesticide application area, and judging whether a target pesticide application operation area has a pesticide application-free area according to the marked area;
if the area without pesticide application exists, the obstacle avoidance path planning is preferentially carried out in the area without pesticide application, pesticide application work is carried out in the area without pesticide application, and if the area without pesticide application does not exist, the obstacle avoidance path planning is carried out according to the shortest path principle;
acquiring an intersection region of an operation region corresponding to an obstacle avoidance path and a mark region in the obstacle avoidance path planning;
when the pesticide applying robot is located in the intersection area, stopping pesticide applying work, and increasing a preset speed increment to drive through the intersection area on the basis of the original running speed;
and updating the pesticide application area according to the obstacle avoidance path planned by the obstacle avoidance path, and correcting the optimal path according to the pesticide application area and the position of the pesticide application robot after obstacle avoidance.
The third aspect of the present invention further provides a computer readable storage medium, where the computer readable storage medium includes a program of an automatic obstacle avoidance method for a drug delivery robot, and when the program of the automatic obstacle avoidance method for a drug delivery robot is executed by a processor, the steps of the automatic obstacle avoidance method for a drug delivery robot as described in any one of the above are implemented.
The invention discloses an automatic obstacle avoidance method, system and storage medium of a pesticide application robot, comprising the following steps: acquiring a plan view of a target pesticide application operation area; acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, and performing primary path planning to generate an optimal path; enabling the pesticide applying robot to reach a designated working point through the optimal path, performing environment perception through machine vision in pesticide applying work, judging barrier point information according to the passability of the pesticide applying robot, and updating a pesticide applying working area plan through the barrier point information; and (4) carrying out obstacle avoidance and optimal path correction according to the updated pesticide application operation area plan by the current position point of the pesticide application robot and the pesticide sprayed area. According to the invention, the environment is sensed in real time through machine vision, and the obstacle avoidance of the pesticide applying robot is realized by combining with the global planning of the path, and the obstacle avoidance path of the pesticide applying robot is optimally set, so that the repeated spraying of pesticides is avoided, and the pesticide applying efficiency is improved.
Drawings
Fig. 1 shows a flow chart of an automatic obstacle avoidance method of a pesticide application robot according to the present invention;
FIG. 2 is a flow chart illustrating a method of determining obstacle point information according to the passability of a drug delivery robot according to the present invention;
FIG. 3 is a flow chart of a method of performing optimal path modification in accordance with the present invention;
fig. 4 shows a block diagram of an automatic obstacle avoidance system of a drug delivery robot according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as specifically described herein and, therefore, the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of an automatic obstacle avoidance method of a drug delivery robot according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides an automatic obstacle avoidance method for a drug delivery robot, including:
s102, acquiring a plan view of a target pesticide application operation area, and acquiring path information according to the plan view of the target pesticide application operation area;
s104, acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, performing primary path planning according to the starting point information, the end point information and the environment information and the path information, and acquiring an optimal path according to the primary path planning;
s106, enabling the pesticide application robot to reach a designated working point through the optimal path, carrying out environment perception through machine vision in pesticide application work, judging barrier point information according to the passability of the pesticide application robot, and updating a target pesticide application working area plan by the barrier point information;
and S108, correcting the optimal path according to the updated target pesticide application operation area plan by the current position point of the pesticide application robot and the pesticide sprayed area.
It should be noted that, acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, and performing preliminary path planning according to the starting point information, the end point information and the environment information and the path information specifically includes: establishing a grid map of the target pesticide application operation area according to the target pesticide application operation area plan and the environmental information, and presetting the speed constraint, the steering constraint and the maximum safe distance between the pesticide application robot and the obstacle; the environment information comprises fixed obstacle information, existing path information and the like of a target pesticide application operation area; performing preliminary path planning through a D-algorithm and constraint information according to start point information and end point information on the grid map, setting a priority queue according to the end point information to perform reverse search in the grid map, setting all grid nodes to New at the beginning, wherein New indicates that the grid nodes are never placed in the priority queue, setting the cost estimation of the end point to 0, and continuously taking out the grid node with the minimum K value from the current priority queue, wherein the K value is a sorting basis, when one grid node is moved out from the priority queue, the grid node transmits the cost to the neighbor grid nodes thereof, the neighbor grid nodes are placed in the priority queue, the optimal path from each grid node to the end point is continuously calculated, and the current grid node of the pesticide applying robot points to the end point according to a pointer pointing to the previous grid node until the current position of the pesticide applying robot is dequeued from the priority queue, and generating an optimal path. When an obstacle node is detected in the searching process, correcting the path cost of the neighbor grid node and resetting the path cost into a priority queue; and selecting the node with the minimum path cost in the neighbor grid nodes to be connected to the starting point information for primarily planning the path, and generating the optimal path.
Fig. 2 is a flowchart illustrating a method for determining obstacle point information according to the passability of the drug delivery robot according to the present invention.
According to the technical scheme of the application, the environment is sensed through machine vision in the pesticide application work, the barrier point information is judged according to the passability of the pesticide application robot, and the barrier point information is used for updating a target pesticide application working area plan, specifically:
s202, video image data in the pesticide application work is obtained, the video image data is preprocessed to extract color features and texture features, and edge segmentation is carried out on the preprocessed video image data through the color features and the texture features;
s204, acquiring a binary image of the video image data through edge segmentation, acquiring barrier information in the surrounding environment in the pesticide application operation, and acquiring the contour data of a barrier according to the binary image;
s206, judging the passability of the pesticide applying robot according to the contour data of the barrier, the size information of the pesticide applying robot and a preset safety distance;
and S208, if the medicine application robot cannot pass through the secondary obstacle, planning an obstacle avoidance path, and updating the obstacle point information in a plan view of the target medicine application area.
It should be noted that, in the process of obtaining an environmental image through a machine vision device, the environmental image is often affected by noise, the video image data is filtered and denoised, the video image data is converted into an RGB space for processing, color features are obtained, obstacles are identified and distinguished by using differences of the color space, the video image data is subjected to ashing processing, the edges of the environmental object are extracted by using a Canny edge detection operator to generate texture features, a binary image is obtained by identifying the color features and the texture features, impurity points of the binary image are removed, and the obstacles are calibrated. Preferentially, the obstacle can be identified and judged by machine learning methods such as a neural network, and the obstacle avoidance path planning can be realized by methods such as a genetic algorithm, an ant colony algorithm, an RRT algorithm, a dynamic window and the like,
It should be noted that, the passability of the pesticide applying robot is judged according to the contour data of the obstacle, the size information of the pesticide applying robot and the preset safety distance, and the method specifically comprises the following steps: obtaining the size parameter of the obstacle according to the contour data of the obstacle, and comparing and judging the size parameter of the obstacle with the maximum width or the maximum ground clearance of the pesticide applying robot; when the size parameter of the obstacle is larger than the maximum width value or the maximum ground clearance, judging that the obstacle cannot pass; and when the inclination angle of the vehicle body of the pesticide applying robot is judged to be larger than the preset inclination angle, the pesticide applying robot is proved to be easy to tip over, and the obstacle is judged to be not accessible. In addition, the pesticide applying robot has a turning width in the turning process, and when whether the pesticide applying robot can pass or not is considered, the comparison between the turning width and the size of an obstacle needs to be considered.
FIG. 3 is a flow chart of a method of performing optimal path modification in accordance with the present invention;
according to the embodiment of the invention, the optimal path is corrected according to the updated pesticide application operation area plan by the current position point of the pesticide application robot and the pesticide sprayed area, and the method specifically comprises the following steps:
s302, obstacle avoidance path planning is carried out according to the relative position of the obstacle point information in the updated target pesticide application operation area plan, current position information of the pesticide application robot is obtained, the current position information is used as a starting point of the obstacle avoidance path planning, and a maximum pesticide application radius reachable point behind the obstacle point on the optimal path is set as an end point of the obstacle avoidance path planning;
s304, acquiring a pesticide application area in the optimal path before the path section obstacle point of the current pesticide application robot, marking the pesticide application area, and judging whether a pesticide application non-area exists in a target pesticide application operation area according to the marked area;
s306, if the non-pesticide-application area exists, the obstacle avoidance path planning is preferentially carried out in the non-pesticide-application area, pesticide application work is carried out in the non-pesticide-application area, and if the non-pesticide-application area does not exist, the obstacle avoidance path planning is carried out according to the shortest path principle;
s308, acquiring an intersection area of the operation area corresponding to the obstacle avoidance path and the mark area in the obstacle avoidance path planning;
s310, when the pesticide applying robot is located in an intersection area, stopping pesticide applying work, and increasing a preset speed increment to drive through the intersection area on the basis of the original running speed;
and S312, updating the pesticide application area according to the obstacle avoidance path planned by the obstacle avoidance path, and correcting the optimal path according to the pesticide application area and the position of the pesticide application robot after obstacle avoidance.
When a plurality of pesticide application robots exist in the target pesticide application working area, the optimal path and the real-time position information of each pesticide application robot are displayed in a plan view of the target pesticide application working area; judging whether collision occurs or whether the pesticide application working area is repeated or not according to the optimal path through the current position information and the motion speed information of each pesticide application robot; when collision between the current pesticide application robot and the target pesticide application robot is detected, setting the waiting time of the current pesticide application robot according to the size information and the movement speed information of the target pesticide application robot, and waiting in situ until the target pesticide application robot passes through according to the waiting time; if the target pesticide application robot does not have a determined motion track, all possible motion areas of the target pesticide application robot at the next moment are taken as obstacle areas, the current position of the current pesticide application robot is taken as a planning starting point, the closest point on the optimal path of the current pesticide application robot behind the obstacle area is taken as an obstacle avoidance terminal point, and obstacle avoidance path planning is carried out; and when the working areas of the pesticide applying robots are repeated, taking the repeated areas as barrier areas of any pesticide applying robot, and performing secondary planning on the optimal path by combining the current position information of the pesticide applying robot.
Fig. 4 shows a block diagram of an automatic obstacle avoidance system of a drug delivery robot according to the present invention.
The second aspect of the present invention also provides an automatic obstacle avoidance system 4 of a drug delivery robot, including: a memory 41 and a processor 42, wherein the memory includes an automatic obstacle avoidance method program of the drug delivery robot, and when the processor executes the automatic obstacle avoidance method program of the drug delivery robot, the processor implements the following steps:
acquiring a target pesticide application operation area plan, and acquiring path information according to the target pesticide application operation area plan;
acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, performing primary path planning according to the starting point information, the end point information and the environment information and the path information, and acquiring an optimal path according to the primary path planning;
enabling the pesticide application robot to reach a designated working point through the optimal path, carrying out environment perception through machine vision in pesticide application work, judging barrier point information according to the passability of the pesticide application robot, and updating a target pesticide application working area plan by the barrier point information;
and correcting the optimal path through the current position point of the pesticide applying robot and the pesticide spraying area according to the updated target pesticide applying operation area plan.
It should be noted that, acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, and performing preliminary path planning according to the starting point information, the end point information and the environment information and the path information specifically includes: establishing a grid map of the target pesticide application operation area according to the target pesticide application operation area plan and the environmental information, and presetting the speed constraint, the steering constraint and the maximum safe distance between the pesticide application robot and the obstacle; the environment information comprises fixed obstacle information, existing path information and the like of a target pesticide application operation area; performing preliminary path planning through a D-algorithm and constraint information according to start point information and end point information on the grid map, setting a priority queue according to the end point information to perform reverse search in the grid map, setting all grid nodes to New at the beginning, wherein New indicates that the grid nodes are never placed in the priority queue, setting the cost estimation of the end point to 0, and continuously taking out the grid node with the minimum K value from the current priority queue, wherein the K value is a sorting basis, when one grid node is moved out from the priority queue, the grid node transmits the cost to the neighbor grid nodes thereof, the neighbor grid nodes are placed in the priority queue, the optimal path from each grid node to the end point is continuously calculated, and the current grid node of the pesticide applying robot points to the end point according to a pointer pointing to the previous grid node until the current position of the pesticide applying robot is dequeued from the priority queue, and generating an optimal path. When an obstacle node is detected in the searching process, correcting the path cost of the neighbor grid node and resetting the path cost into a priority queue; and selecting the node with the minimum path cost in the neighbor grid nodes to be connected to the starting point information for primarily planning the path, and generating the optimal path.
According to the technical scheme, the environment is sensed through machine vision in the pesticide application work, the barrier point information is judged according to the passability of the pesticide application robot, and the barrier point information is used for updating a plan view of a target pesticide application work area, and the method specifically comprises the following steps of:
acquiring video image data in the pesticide application work, preprocessing the video image data to extract color features and texture features, and performing edge segmentation on the preprocessed video image data through the color features and the texture features;
acquiring a binary image of video image data through edge segmentation, acquiring barrier information in the surrounding environment in the pesticide application operation, and acquiring the contour data of a barrier according to the binary image;
judging the passability of the pesticide applying robot according to the contour data of the obstacle, the size information of the pesticide applying robot and a preset safety distance;
and if the pesticide application robot cannot pass through the secondary obstacle, planning an obstacle avoidance path, and updating the obstacle point information in a target pesticide application area plan.
It should be noted that, in the process of obtaining an environmental image through a machine vision device, the environmental image is often affected by noise, the video image data is filtered and denoised, the video image data is converted into an RGB space for processing, color features are obtained, obstacles are identified and distinguished by using differences of the color space, the video image data is subjected to ashing processing, the edges of the environmental object are extracted by using a Canny edge detection operator to generate texture features, a binary image is obtained by identifying the color features and the texture features, impurity points of the binary image are removed, and the obstacles are calibrated. Preferentially, the obstacle can be identified and judged by machine learning methods such as a neural network, and the obstacle avoidance path planning can be realized by methods such as a genetic algorithm, an ant colony algorithm, an RRT algorithm, a dynamic window and the like,
It should be noted that, the passability of the pesticide application robot is judged according to the contour data of the obstacle, the size information of the pesticide application robot and the preset safety distance, and the method specifically comprises the following steps: obtaining the size parameter of the obstacle according to the contour data of the obstacle, and comparing and judging the size parameter of the obstacle with the maximum width or the maximum ground clearance of the pesticide applying robot; when the size parameter of the obstacle is larger than the maximum width value or the maximum ground clearance, judging that the obstacle cannot pass; and when the inclination angle of the vehicle body of the pesticide applying robot is judged to be larger than the preset inclination angle, the pesticide applying robot is proved to be easy to tip over, and the obstacle is judged to be not accessible. In addition, the pesticide applying robot has a turning width in the turning process, and when whether the pesticide applying robot can pass or not is considered, the comparison between the turning width and the size of an obstacle needs to be considered.
According to the embodiment of the invention, the optimal path is corrected according to the updated pesticide application operation area plan by the current position point of the pesticide application robot and the pesticide sprayed area, and the method specifically comprises the following steps:
carrying out obstacle avoidance path planning according to the relative position of the obstacle point information in the updated target pesticide application operation area plan, acquiring current position information of the pesticide application robot, taking the current position information as a starting point of the obstacle avoidance path planning, and setting a maximum pesticide application radius reachable point behind an obstacle point on the optimal path as an end point of the obstacle avoidance path planning;
acquiring a pesticide application area in front of a path section obstacle point of a current pesticide application robot on an optimal path, marking the pesticide application area, and judging whether a target pesticide application operation area has a pesticide non-application area according to the marked area;
if the non-pesticide-application area exists, the obstacle avoidance path planning is preferentially carried out in the non-pesticide-application area, pesticide application work is carried out in the non-pesticide-application area, and if the non-pesticide-application area does not exist, the obstacle avoidance path planning is carried out according to the shortest path principle;
acquiring an intersection region of an operation region corresponding to an obstacle avoidance path and a mark region in the obstacle avoidance path planning;
when the pesticide applying robot is located in the intersection area, stopping pesticide applying work, and increasing a preset speed increment to drive through the intersection area on the basis of the original running speed;
and updating the pesticide application area according to the obstacle avoidance path planned by the obstacle avoidance path, and correcting the optimal path according to the pesticide application area and the position of the pesticide application robot after obstacle avoidance.
When a plurality of pesticide application robots exist in the target pesticide application working area, the optimal path and the real-time position information of each pesticide application robot are displayed in a plan view of the target pesticide application working area; judging whether collision occurs or whether the pesticide application working area is repeated or not according to the optimal path through the current position information and the motion speed information of each pesticide application robot; when collision between the current pesticide application robot and the target pesticide application robot is detected, setting the waiting time of the current pesticide application robot according to the size information and the movement speed information of the target pesticide application robot, and waiting in situ until the target pesticide application robot passes through according to the waiting time; if the target pesticide application robot does not have a determined motion track, all possible motion areas of the target pesticide application robot at the next moment are taken as obstacle areas, the current position of the current pesticide application robot is taken as a planning starting point, the closest point on the optimal path of the current pesticide application robot behind the obstacle area is taken as an obstacle avoidance terminal point, and obstacle avoidance path planning is carried out; and when the working areas of the pesticide applying robots are repeated, taking the repeated areas as barrier areas of any pesticide applying robot, and performing secondary planning on the optimal path by combining the current position information of the pesticide applying robot.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an automatic obstacle avoidance method for a drug delivery robot, and when the program of the automatic obstacle avoidance method for a drug delivery robot is executed by a processor, the steps of the automatic obstacle avoidance method for a drug delivery robot as described in any one of the above are implemented.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An automatic obstacle avoidance method of a pesticide application robot is characterized by comprising the following steps:
acquiring a target pesticide application operation area plan, and acquiring path information according to the target pesticide application operation area plan;
acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, performing primary path planning according to the starting point information, the end point information and the environment information and the path information, and acquiring an optimal path according to the primary path planning;
enabling the pesticide application robot to reach a designated working point through the optimal path, carrying out environment perception through machine vision in pesticide application work, judging barrier point information according to the passability of the pesticide application robot, and updating a target pesticide application working area plan by the barrier point information;
and correcting the optimal path through the current position point of the pesticide applying robot and the pesticide spraying area according to the updated target pesticide applying operation area plan.
2. The automatic obstacle avoidance method of the pesticide application robot according to claim 1, wherein starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot are obtained, and preliminary path planning is performed according to the starting point information, the end point information and the environment information and according to the path information, specifically:
establishing a grid map of the target pesticide application operation area according to the target pesticide application operation area plan and the environmental information, and presetting the speed constraint, the steering constraint and the maximum safe distance between the pesticide application robot and the obstacle;
performing preliminary path planning through a D-algorithm and constraint information according to the start point information and the end point information on the grid map, setting a priority queue according to the end point information to perform reverse search in the grid map,
when an obstacle node is detected in the searching process, correcting the path cost of the neighbor grid node, and placing the path cost in a priority queue again until the current position of the pesticide application robot is dequeued from the priority queue;
and selecting the node with the minimum path cost in the neighbor grid nodes to be connected to the starting point information for primarily planning the path, and generating the optimal path.
3. The automatic obstacle avoidance method of the pesticide application robot as claimed in claim 1, wherein the pesticide application robot performs environment perception through machine vision during pesticide application, judges obstacle point information according to passability of the pesticide application robot, and updates a plan view of a target pesticide application operation area by the obstacle point information, specifically:
acquiring video image data in the pesticide application work, preprocessing the video image data to extract color features and texture features, and performing edge segmentation on the preprocessed video image data through the color features and the texture features;
acquiring a binary image of video image data through edge segmentation, acquiring barrier information in the surrounding environment in the pesticide application operation, and acquiring the contour data of a barrier according to the binary image;
judging the passability of the pesticide applying robot according to the contour data of the obstacle, the size information of the pesticide applying robot and a preset safety distance;
and if the pesticide application robot cannot pass through the secondary obstacle, planning an obstacle avoidance path, and updating the obstacle point information in a target pesticide application area plan.
4. The automatic obstacle avoidance method of the pesticide application robot as claimed in claim 3, wherein the passability of the pesticide application robot is judged according to the contour data of the obstacle, the size information of the pesticide application robot and the preset safety distance, and the method comprises the following specific steps:
obtaining the size parameter of the obstacle according to the contour data of the obstacle, and comparing and judging the size parameter of the obstacle with the maximum width or the maximum ground clearance of the pesticide applying robot;
when the size parameter of the obstacle is larger than the maximum width value or the maximum ground clearance, judging that the obstacle cannot pass;
and when the inclination angle of the vehicle body of the pesticide applying robot is larger than the preset inclination angle, the pesticide applying robot is proved to be easy to tip over, and the obstacle is judged to be not to pass.
5. The automatic obstacle avoidance method of the pesticide application robot according to claim 1, wherein the correction of the optimal path is performed through the current position point of the pesticide application robot and the pesticide sprayed area according to the updated pesticide application operation area plan, and specifically comprises the following steps:
carrying out obstacle avoidance path planning according to the relative position of the obstacle point information in the updated target pesticide application operation area plan, acquiring current position information of the pesticide application robot, taking the current position information as a starting point of the obstacle avoidance path planning, and setting a maximum pesticide application radius reachable point behind an obstacle point on the optimal path as an end point of the obstacle avoidance path planning;
acquiring a pesticide application area in front of a path section obstacle point of a current pesticide application robot on an optimal path, marking the pesticide application area, and judging whether a target pesticide application operation area has a pesticide application-free area according to the marked area;
if the area without pesticide application exists, the obstacle avoidance path planning is preferentially carried out in the area without pesticide application, pesticide application work is carried out in the area without pesticide application, and if the area without pesticide application does not exist, the obstacle avoidance path planning is carried out according to the shortest path principle;
acquiring an intersection region of an operation region corresponding to an obstacle avoidance path and a mark region in the obstacle avoidance path planning;
when the pesticide application robot is positioned in the intersection region, stopping pesticide application work, and increasing a preset speed increment to drive through the intersection region on the basis of the original running speed;
and updating the pesticide application area according to the obstacle avoidance path planned by the obstacle avoidance path, and correcting the optimal path according to the pesticide application area and the position of the pesticide application robot after obstacle avoidance.
6. The automatic obstacle avoidance method of the pesticide application robot as claimed in claim 1, further comprising:
when a plurality of pesticide applying robots exist in the target pesticide applying working area, displaying the optimal path and real-time position information of each pesticide applying robot in a plan view of the target pesticide applying working area;
judging whether collision occurs or whether the pesticide application working area is repeated or not according to the optimal path through the current position information and the movement speed information of each pesticide application robot;
when collision between the current pesticide application robot and the target pesticide application robot is detected, setting the waiting time of the current pesticide application robot according to the size information and the movement speed information of the target pesticide application robot, and waiting in situ until the target pesticide application robot passes through according to the waiting time;
if the target pesticide application robot does not have a determined motion track, all possible motion areas of the target pesticide application robot at the next moment are taken as obstacle areas, the current position of the current pesticide application robot is taken as a planning starting point, the closest point on the optimal path of the current pesticide application robot behind the obstacle area is taken as an obstacle avoidance terminal point, and obstacle avoidance path planning is carried out;
and when the working areas of the pesticide applying robots are repeated, taking the repeated areas as barrier areas of any pesticide applying robot, and performing secondary planning on the optimal path by combining the current position information of the pesticide applying robot.
7. An automatic obstacle avoidance system of a pesticide application robot is characterized by comprising: the automatic obstacle avoidance method program of the drug delivery robot is executed by the processor to realize the following steps:
acquiring a target pesticide application operation area plan, and acquiring path information according to the target pesticide application operation area plan;
acquiring starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot, performing primary path planning according to the starting point information, the end point information and the environment information and the path information, and acquiring an optimal path according to the primary path planning;
enabling the pesticide application robot to reach a designated working point through the optimal path, carrying out environment perception through machine vision in pesticide application work, judging barrier point information according to the passability of the pesticide application robot, and updating a target pesticide application working area plan by the barrier point information;
and correcting the optimal path through the current position point of the pesticide applying robot and the pesticide spraying area according to the updated target pesticide applying operation area plan.
8. The automatic obstacle avoidance system of the pesticide application robot according to claim 7, wherein starting point information, end point information and environment information of a target pesticide application operation area of the pesticide application robot are acquired, and preliminary path planning is performed according to the starting point information, the end point information and the environment information and according to the path information, specifically:
establishing a grid map of the target pesticide application operation area according to the target pesticide application operation area plan and the environmental information, and presetting the speed constraint, the steering constraint and the maximum safe distance between the pesticide application robot and the obstacle;
performing preliminary path planning through a D-algorithm and constraint information according to the start point information and the end point information on the grid map, setting a priority queue according to the end point information to perform reverse search in the grid map,
when an obstacle node is detected in the searching process, correcting the path cost of the neighbor grid node, and placing the path cost in a priority queue again until the current position of the pesticide application robot is dequeued from the priority queue;
and selecting the node with the minimum path cost in the neighbor grid nodes to be connected to the starting point information for primarily planning the path, and generating the optimal path.
9. The automatic obstacle avoidance system of the pesticide application robot according to claim 7, wherein the correction of the optimal path is performed through the current position point of the pesticide application robot and the pesticide sprayed area according to the updated pesticide application operation area plan, specifically:
carrying out obstacle avoidance path planning according to the relative position of the obstacle point information in the updated target pesticide application operation area plan, acquiring current position information of the pesticide application robot, taking the current position information as a starting point of the obstacle avoidance path planning, and setting a maximum pesticide application radius reachable point behind an obstacle point on the optimal path as an end point of the obstacle avoidance path planning;
acquiring a pesticide application area in front of a path section obstacle point of a current pesticide application robot on an optimal path, marking the pesticide application area, and judging whether a target pesticide application operation area has a pesticide application-free area according to the marked area;
if the non-pesticide-application area exists, the obstacle avoidance path planning is preferentially carried out in the non-pesticide-application area, pesticide application work is carried out in the non-pesticide-application area, and if the non-pesticide-application area does not exist, the obstacle avoidance path planning is carried out according to the shortest path principle;
acquiring an intersection area of a working area corresponding to an obstacle avoidance path and a mark area in the obstacle avoidance path planning;
when the pesticide application robot is positioned in the intersection region, stopping pesticide application work, and increasing a preset speed increment to drive through the intersection region on the basis of the original running speed;
and updating the pesticide application area according to the obstacle avoidance path planned by the obstacle avoidance path, and correcting the optimal path according to the pesticide application area and the position of the pesticide application robot after obstacle avoidance.
10. A computer-readable storage medium characterized by: the computer readable storage medium comprises a program of an automatic obstacle avoidance method for a drug delivery robot, which when executed by a processor implements the steps of an automatic obstacle avoidance method for a drug delivery robot as claimed in any one of claims 1 to 6.
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| CN116588573A (en) * | 2023-04-28 | 2023-08-15 | 北京云中未来科技有限公司 | Bulk cargo grabbing control method and system of intelligent warehouse lifting system |
| CN116588573B (en) * | 2023-04-28 | 2024-02-02 | 北京云中未来科技有限公司 | Bulk cargo grabbing control method and system of intelligent warehouse lifting system |
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