CN108827309B - Robot path planning method and dust collector with same - Google Patents
Robot path planning method and dust collector with same Download PDFInfo
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- CN108827309B CN108827309B CN201810702691.8A CN201810702691A CN108827309B CN 108827309 B CN108827309 B CN 108827309B CN 201810702691 A CN201810702691 A CN 201810702691A CN 108827309 B CN108827309 B CN 108827309B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0219—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory ensuring the processing of the whole working surface
Abstract
The invention provides a robot path planning method and a dust collector with the same, wherein the method comprises the steps of extracting an effective area where a robot can walk in a 2D map, and rasterizing the effective area; connecting the middle points of the grids, generating a tree path, and performing non-target walking or target walking by the robot according to the tree path. The robot path planning method can enable the robot to rapidly and completely traverse the whole effective area when no target exists, and can also rapidly and accurately calculate the shortest path from the robot to the target when a walking target exists. When the path planning method is applied to the sweeping robot, the sweeping robot can quickly sweep the whole effective area, and the target sweeping of the sweeping robot is realized.
Description
Technical Field
The invention relates to the field of intelligent robots, in particular to a robot path planning method and a dust collector with the same.
Background
Path planning is one of the key technologies to implement control of a walking robot. The method aims to search an optimal or suboptimal safe collision-free path from a starting position to a target position under certain environmental conditions and performance index requirements. Aiming at robot path planning, scholars at home and abroad propose a plurality of planning methods, wherein the planning methods mainly comprise an artificial potential field method, a neural network adaptive planning method, a genetic algorithm, an ant colony algorithm, a particle swarm algorithm and the like. In recent years, more and more scholars pay more attention to the combination of various intelligent algorithms in the process of researching the path planning problem so as to improve the algorithm performance. For example, ImenChari and the like combine a genetic algorithm and an ant colony algorithm, the genetic algorithm is used for generating initial pheromone distribution in the former stage, and the ant colony algorithm is used for solving the optimal solution in the latter stage, so that the advantages of the two algorithms can be effectively combined, the search efficiency of the ant colony is improved, and the ant colony may be trapped into local optimization; x Wang et al propose a new route planning method based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithm, this algorithm utilizes the method of Particle Swarm environmental modeling, produce the route from starting point to goal point, then based on route distribution pheromone that is produced before, finally, use the improved Optimization Ant colony to find the optimum route, this method can shorten search time, but have higher requirements for the environment, adaptability is bad; t Zhu, G Dong, etc. propose the algorithm that combines ant colony algorithm and artificial potential field method to use, this algorithm initializes the overall route with the potential field method, optimize the route sequencing of every generation ant, and upgrade the pheromone according to the sequencing of ant route, meanwhile, under the help of pheromone of elite ant, use the intersection and variation operation of the modular factorial algorithm on every generation route, this algorithm has improved convergence rate and stability, but the potential field method is apt to fall into the local deadlock, so this algorithm is apt to fall into the local optimum while looking for the route initially.
Chinese patent application No. 201310604565.6 discloses a regular hexagon-based mobile anchor node path planning method in a wireless sensor network, where the network includes a plurality of stationary unknown nodes and a mobile anchor node, and the method includes the steps of: the walking anchor node moves at a constant speed v, at intervals of time t, with the position of the walking anchor node as the center of a circle and R as the communication radius, beacon information is broadcast, the beacon information comprises the position of the walking anchor node at the moment and a beacon ID, and the walking path of the walking anchor node is a regular hexagon. In the communication technology fields of walking cellular networks, ZigBee networks and the like, the path planning algorithm using regular hexagons as the most basic grid exists.
Chinese patent application No. 201410497805.1 discloses a robot static path planning method, which includes: setting a target point, and establishing an artificial potential field in a map range by taking the target point as a terminal point; introducing a particle swarm algorithm, arranging m particle swarms at the starting point of the robot, simulating the walking of each particle from the starting point to the end point according to the artificial potential field and the particle swarm algorithm at the flight speed of the ith particle in the t step, and forming respective motion tracks of each particle in the process of simulating the walking; most particles gradually gather and converge towards one of the tracks, and then an optimal walking path from a starting point to an end point is obtained in a map range; and finally, the robot completes the motion process from the starting point to the end point according to the optimal walking path. The potential field method, the grid method and the particle swarm method are combined, but the algorithm is complex, the path planning efficiency is low, and the improvement is urgently needed.
Disclosure of Invention
In order to solve the problems, the invention provides a robot path planning method and a dust collector with the same. The robot path planning method can enable the robot to rapidly and completely traverse the whole effective area when no target exists, and can also rapidly and accurately calculate the shortest path from the robot to the target when a walking target exists. When the path planning method is applied to the sweeping robot, the sweeping robot can quickly sweep the whole effective area, and the target sweeping of the sweeping robot is realized.
In order to realize the technical purpose, the technical scheme of the invention is as follows: a robot path planning method comprises the following steps:
s1: extracting an effective area where the robot can walk in the 2D map, and rasterizing the effective area;
s2: connecting the middle points of the grids, generating a tree path, and performing non-target walking or target walking by the robot according to the tree path.
Further, the method for extracting the effective area where the robot can walk in the 2D map in step S1 includes the following steps:
t1: the robot walks the whole environment by adopting an obstacle avoidance algorithm, and establishes a 3D model of the environment by utilizing a depth camera;
t2: extracting the bottom surface of the 3D model in the step T1 as an effective area;
preferably, the method for extracting the effective region in step S1 includes the following steps:
e1: the robot judges the obstacle by using the depth camera, walks along the obstacle from the starting point, finally returns to the starting point by taking the starting point as a target, and marks a walking route and the obstacle;
e2: and E1, calculating the closing interval of the walking route in the step E1, and removing the closing interval at the marked obstacle to obtain the effective area.
Further, the method for calculating the walking route closed interval in the step E3 includes: and calculating a second derivative of the walking route, and calculating the continuity of the second derivative in the whole interval, wherein if the second derivative is continuous, the walking route is closed.
Further, the gridding dividing method in step S1 is to generate an initial regular hexagon at the position of the robot, and to grow connected regular hexagons outside the initial regular hexagon in the effective area.
Preferably, the starting regular hexagon and the regular hexagon have the same size and are not larger than a circumcircle of the outer circumference of the robot.
Further, the method for walking without target in step S2 includes the following steps:
p1: performing hierarchical division on the tree path generated in the step S2, taking the regular hexagon externally connected to the initial regular hexagon as a primary tree path, and taking the regular hexagon externally connected to the primary tree path as a secondary tree path, until the tree path is divided to the tail end of the tree path;
p2: walking forward from the starting regular hexagon, each walk traversing each level of tree path.
Further, the method for targeted walking in step S2 includes the following steps;
d1: taking the center of the initial regular hexagon as a starting point, and making a first vector from the center of the initial regular hexagon to the center of the hexagon where the target is located;
d2: judging whether the first vector is completely in the effective area, if the effective area completely contains the first vector, the robot walks to the target in a straight line, otherwise, at least two intersection points exist between the first vector and the effective area, and performing step D3;
d3: and calculating a first intersection point and another intersection point which are close to the center of the initial regular hexagon as a second intersection point, enabling the robot to walk to the center of the regular hexagon close to the first intersection point in a straight line, and walking to the target in a straight line after walking to the second intersection point along a tree path from the first intersection point to the middle of the second intersection point.
Further, the method for walking along the tree path between the first intersection point and the second intersection point to the second intersection point in the step D3 is as follows:
making a second vector to the center of each adjacent regular hexagon at the center of the regular hexagon near the first intersection point; judging the included angle between the second vector and the first vector, and selecting the vector with a smaller included angle to walk to a second intersection point position;
or the robot lists all tree paths between the first intersection point and the second intersection point in advance, calculates the shortest tree path, and walks to the second intersection point along the shortest tree path.
A dust collector comprises the robot path planning method, and the method is used for non-target cleaning or target cleaning.
The invention has the beneficial effects that:
the robot can quickly and reliably calculate the walking effective area of the robot through the depth camera, utilizes regular hexagon rasterization, generates a tree path based on the midpoint connection of grids, and further ensures that the path can cover the whole effective area when the robot walks in a non-target manner by combining the rasterization precision. Secondly, based on the tree path and the method for grading the tree path, the robot can quickly calculate the shortest path from the robot to the target by an included angle or a path listing method.
In conclusion, the robot path planning method of the invention can make the robot quickly and completely traverse the whole effective area when no target exists, and can also quickly and accurately calculate the shortest path from the robot to the target when a walking target exists. When the path planning method is applied to the sweeping robot, the sweeping robot can quickly sweep the whole effective area, and the target sweeping of the sweeping robot is realized.
Drawings
FIG. 1 is a flow chart of a robot path planning method of the present invention;
FIG. 2 is a diagram illustrating a method for generating tree paths and hierarchical classification according to the present invention;
FIG. 3 is a schematic diagram of the inventive robot drone walking method;
FIG. 4 is one embodiment of a method for performing targeted walking by the robot of the present invention;
fig. 5 shows a second method for performing the robot walking in a targeted manner according to the present invention.
Detailed Description
The technical solution of the present invention will be clearly and completely described below.
In the present invention, "inner" means close to the robot starting point, "outer" means far from the robot starting point, and the directional terms such as "outer periphery" and the like are used to fully describe the path planning method of the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, a robot path planning method includes the following steps:
s1: extracting an effective area where the robot 1 can walk in the 2D map, and rasterizing the effective area;
s2: connecting the middle points of the grids, generating a tree path, and performing non-target walking or target walking by the robot according to the tree path.
Further, the method for extracting the effective area where the robot can walk in the 2D map in step S1 includes the following steps:
t1: the robot 1 walks the whole environment by adopting an obstacle avoidance algorithm, and establishes a 3D model of the environment by utilizing a depth camera; the obstacle avoidance algorithm is a general technical means that is easily obtained by those skilled in the art, and is not described herein.
T2: extracting the bottom surface of the 3D model in the step T1 as an effective area; it should be noted that, for a place with obstacles, the robot can only establish a 3D image of the obstacles, but cannot scan the ground, so in the 3D model of the environment established by the depth camera, the ground can be scanned as the bottom surface of the 3D model, and the robot takes the bottom surface as an effective area where the robot can walk. Or projecting the 3D model in step T1 to the ground, and removing the obstacle projection part in the projection to obtain the effective area.
Preferably, the method for extracting the effective region in step S1 includes the following steps:
e1: the robot judges the obstacle by using the depth camera, walks along the obstacle from the starting point, finally returns to the starting point by taking the starting point as a target, and marks a walking route and the obstacle;
e2: and E1, calculating the closing interval of the walking route in the step E1, and removing the closing interval at the marked obstacle to obtain the effective area. That is, in the embodiment, if the robot can form a closed section by one obstacle, the closed section after removing the closed section is the maximum effective area where the robot can travel, and the effective area is inside the rimmed area of the robot travel route. The effective area calculated by the embodiment has the advantages of high calculation speed and high reliability.
Further, the method for calculating the walking route closed interval in the step E3 includes: and calculating a second derivative of the walking route, and calculating the continuity of the second derivative in the whole interval, wherein if the second derivative is continuous, the walking route is closed.
Further, the gridding dividing in step S1 is performed by growing adjacent regular hexagons outside the initial regular hexagon with the robot 1 generating the initial regular hexagon 2 in the effective area. As shown in fig. 2, a first circle of regular hexagons is grown outside six sides of the starting regular hexagon 2, a second circle of regular hexagons is grown outside the first circle of regular hexagons, and if the regular hexagons overlap the edge of the effective area, the growth in the direction changing is stopped until the regular hexagons completely cover the effective area. Specifically, as shown in fig. 3-5, a schematic diagram of an active area that is formed by using regular hexagonal grids is shown.
Preferably, the starting regular hexagon and the regular hexagon have the same size and are not larger than a circumcircle of the outer circumference of the robot, and the rasterization precision is established under the condition that the robot path can cover the whole effective area.
Further, the method for walking without target in step S2 includes the following steps:
p1: performing hierarchical division on the tree path generated in the step S2, taking the regular hexagon externally connected to the initial regular hexagon as a primary tree path, and taking the regular hexagon externally connected to the primary tree path as a secondary tree path, until the tree path is divided to the tail end of the tree path;
p2: walking forward from the starting regular hexagon, each walk traversing each level of tree path. In the rasterized region shown in fig. 3, all the regular hexagons passed by the first circular arc 5 are the first circle of regular hexagons grown from the starting regular hexagon 2, so that the robot should have a first tree path to the center of the first circle of regular hexagons, and similarly, the second circular arc 6 passes by the second circle of regular hexagons, so that the robot has a second tree path from the center of the first circle of regular hexagons to the second circle. From the starting point, the robot 1 first follows the first arc 5 through the primary tree path, then walks to the secondary tree path and follows through the secondary tree path, and the task of walking without the target is completed. The robot of the present invention may also be used as a new obstacle avoidance algorithm by proceeding along a tree path generated after each step from the start point while performing the effective region extraction and the rasterization division in step S1. Or the robot can judge the obstacle according to the depth camera, further calculate the effective area, and grow the tree path in the visible range of the depth camera, so that the robot can avoid the obstacle and quickly traverse the whole effective area.
Further, the method for targeted walking in step S2 includes the following steps;
d1: taking the center of the initial regular hexagon 2 as a starting point, and making a first vector 7 from the center of the initial regular hexagon to the center of the hexagon where the target 9 is located;
d2: judging whether the first vector 7 is completely located in the effective area, as shown in fig. 4, if the effective area completely contains the first vector, the robot linearly walks to the target, otherwise, at least two intersection points exist between the first vector and the effective area, and performing step D3;
d3: and calculating a first intersection point and another intersection point which are close to the center of the initial regular hexagon as a second intersection point, enabling the robot to walk to the center of the regular hexagon close to the first intersection point in a straight line, and walking to the target in a straight line after walking to the second intersection point along a tree path from the first intersection point to the middle of the second intersection point. Or, when the robot of the present invention cannot reach the target in a straight line, the robot first reaches the edge of the effective area (the first intersection), then generates a tree path by using regular hexagonal rasterization and performs level division, determines the level of the tree path where the target or the second intersection is located, and walks n steps along the tree path to reach the target or the second intersection according to the level difference n.
Further, the method for walking along the tree path between the first intersection point and the second intersection point to the second intersection point in the step D3 is as follows:
as shown in fig. 5, a second vector 8 is made to the center of each adjacent regular hexagon at the center of the regular hexagon in the vicinity of the first intersection; judging the included angle between the second vector 8 and the first vector 7, and selecting the vector with a smaller included angle to walk to a second intersection point position; or, the present invention selects the tree path with the nearest direction to advance by judging the direction of the next level tree path of the first intersection point and the target, so as to advance to the target in a direction.
Or the robot lists all tree paths between the first intersection point and the second intersection point in advance, calculates the shortest tree path, and walks to the second intersection point along the shortest tree path. Regardless of the vector angle mode or the tree path list mode, the shortest path finally calculated is the shortest path with the same step number.
A dust collector comprises the robot path planning method, and the method is used for non-target cleaning or target cleaning.
It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the inventive concept thereof, and these changes and modifications can be made without departing from the spirit and scope of the invention.
Claims (8)
1. A robot path planning method is characterized by comprising the following steps:
s1: extracting an effective area where the robot can walk in the 2D map, and rasterizing the effective area;
s2: connecting the middle points of the grids to generate a tree path, and performing non-target walking or target walking by the robot according to the tree path;
the gridding dividing method in the step S1 is that the initial regular hexagon is generated at the position of the robot, and the connected regular hexagons are grown outside the initial regular hexagon in the effective area;
the method for the targeted walking in the step S2 comprises the following steps;
d1: taking the center of the initial regular hexagon as a starting point, and making a first vector from the center of the initial regular hexagon to the center of the hexagon where the target is located;
d2: judging whether the first vector is completely in the effective area, if the effective area completely contains the first vector, the robot walks to the target in a straight line, otherwise, at least two intersection points exist between the first vector and the effective area, and performing step D3;
d3: and calculating a first intersection point and another intersection point which are close to the center of the initial regular hexagon as a second intersection point, enabling the robot to walk to the center of the regular hexagon close to the first intersection point in a straight line, and walking to the target in a straight line after walking to the second intersection point along a tree path from the first intersection point to the middle of the second intersection point.
2. The robot path planning method according to claim 1, wherein the method of extracting the effective area where the robot can walk in the 2D map in step S1 includes the steps of:
t1: the robot walks the whole environment by adopting an obstacle avoidance algorithm, and establishes a 3D model of the environment by utilizing a depth camera;
t2: the bottom surface of the 3D model in step T1 is extracted as an effective region.
3. The method for planning a robot path according to claim 1, wherein the method for extracting the effective area in step S1 comprises the following steps:
e1: the robot judges the obstacle by using the depth camera, walks along the obstacle from the starting point, finally returns to the starting point by taking the starting point as a target, and marks a walking route and the obstacle;
e2: and E1, calculating the closing interval of the walking route in the step E1, and removing the closing interval at the marked obstacle to obtain the effective area.
4. The robot path planning method according to claim 3, wherein the method for calculating the closed section of the walking route in step E2 is as follows: and calculating a second derivative of the walking route, and calculating the continuity of the second derivative in the whole interval, wherein if the second derivative is continuous, the walking route is closed.
5. The robot path planning method according to claim 1, wherein the starting regular hexagon and the regular hexagon have the same size, and the starting regular hexagon is not larger than a circumcircle of the outer circumference of the robot.
6. The method for robot path planning according to claim 1, wherein the method for walking without target in step S2 comprises the following steps:
p1: performing hierarchical division on the tree path generated in the step S2, taking the regular hexagon externally connected to the initial regular hexagon as a primary tree path, and taking the regular hexagon externally connected to the primary tree path as a secondary tree path, until the tree path is divided to the tail end of the tree path;
p2: walking forward from the starting regular hexagon, each walk traversing each level of tree path.
7. The method for planning a robot path according to claim 1, wherein the step D3 of walking along the tree path between the first intersection and the second intersection to the second intersection comprises:
making a second vector to the center of each adjacent regular hexagon at the center of the regular hexagon near the first intersection point; judging the included angle between the second vector and the first vector, and selecting the vector with a smaller included angle to walk to a second intersection point position;
or the robot lists all tree paths between the first intersection point and the second intersection point in advance, calculates the shortest tree path, and walks to the second intersection point along the shortest tree path.
8. A vacuum cleaner comprising a robot path planning method according to any one of claims 1-7.
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| CN109516120A (en) * | 2019-01-14 | 2019-03-26 | 山东省科学院自动化研究所 | A kind of the omnidirectional's conveyer system and working method of three-wheel drive |
| CN110108292B (en) * | 2019-06-12 | 2020-03-27 | 山东师范大学 | Vehicle navigation path planning method, system, device and medium |
| CN110442128B (en) * | 2019-07-20 | 2022-08-16 | 河北科技大学 | AGV path planning method based on characteristic point extraction ant colony algorithm |
| CN110631601B (en) * | 2019-11-13 | 2021-04-27 | 中国电子科技集团公司第二十八研究所 | Path planning method based on non-display topological vector map |
| CN111132003B (en) * | 2019-11-27 | 2020-11-24 | 桂林电子科技大学 | UWSN sensor node positioning method based on dynamic path planning |
| CN111121795B (en) * | 2020-03-26 | 2020-07-07 | 腾讯科技（深圳）有限公司 | Road network generation method, navigation device, equipment and storage medium |
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