阅读说明：本技术 一种基于机器人体积的移动机器人路径规划方法 (Mobile robot path planning method based on robot volume ) 是由 杨金铎 王元峰 曾惜 王冕 赖劲舟 张羿 顾行健 蒋天柱 于 2021-07-15 设计创作，主要内容包括：本发明公开了一种基于机器人体积的移动机器人路径规划方法,包括：采集所使用的移动机器人的外形尺寸信息,实时测量移动机器人运动过程中与障碍物的距离；基于所述尺寸信息以及所述距离数据,实时计算移动机器人的运动代价,将所述运动代价加入A*算法的总代价函数中,得到优化后的A*算法代价函数；根据所述优化后的A*算法代价函数对具有所述障碍物的环境进行路径规划,得到与所述障碍物保持预设距离的路径。本发明可以提前开始对障碍物进行避让距离判断,可以在缩小算法时间复杂度的基础上有效的避免与障碍物的冲突。(The invention discloses a mobile robot path planning method based on robot volume, which comprises the following steps: collecting the overall dimension information of the used mobile robot, and measuring the distance between the mobile robot and an obstacle in the motion process in real time; calculating the motion cost of the mobile robot in real time based on the size information and the distance data, and adding the motion cost into a total cost function of an A-algorithm to obtain an optimized A-algorithm cost function; and planning a path of the environment with the obstacle according to the optimized A-x algorithm cost function to obtain a path keeping a preset distance from the obstacle. The method can start to judge the avoidance distance of the barrier in advance, and can effectively avoid the conflict with the barrier on the basis of reducing the time complexity of the algorithm.)

1. A mobile robot path planning method based on robot volume is characterized by comprising the following steps:

collecting the overall dimension information of the used mobile robot, and measuring the distance between the mobile robot and an obstacle in the motion process in real time;

calculating the motion cost of the mobile robot in real time based on the size information and the distance data, and adding the motion cost into a total cost function of an A-algorithm to obtain an optimized A-algorithm cost function;

and planning a path of the environment with the obstacle according to the optimized A-x algorithm cost function to obtain a path keeping a preset distance from the obstacle.

2. The robot volume-based mobile robot path planning method of claim 1, wherein: and measuring the distance between the mobile robot and the obstacle in the moving process in real time by using a distance measuring sensor.

3. The robot volume-based mobile robot path planning method of claim 1, wherein: the total cost function of the a-algorithm includes,

F(n)＝G(n)+H(n)

wherein g (n) represents the cost from the initial node to any node n, and h (n) represents the heuristically evaluated cost from node n to the target node.

4. A method for mobile robot path planning based on robot volume according to claim 1 or 3, characterized by: the a-x algorithm describes its cost with + ∞forobstacles that cannot be passed through.

5. The robot volume-based mobile robot path planning method of claim 1, wherein: defining a cost of motion of a mobile robot for a certain node in the vicinity of the obstacle comprises,

wherein d represents the distance from the center of the robot to the obstacle, d₀Denotes the outer dimension of the robot, w₁The weight coefficient is expressed, e is a natural base number, and σ is a standard deviation and represents the curve width.

6. A method for mobile robot path planning based on robot volume according to any of claims 1, 3, 5, characterized by: adding the motion cost into the total cost function of the A-algorithm to obtain an optimized A-algorithm cost function,

F(n)＝G(n)+H(n)+R₁(n)。

7. the robot volume based mobile robot path planning method of claim 6, wherein: the criterion for judging whether the mobile robot collides with the obstacle includes,

when the distance to the obstacle is smaller than the size of the mobile robot, the cost is + ∞, namely the mobile robot can firstly collide with the obstacle under the condition;

when the distance between the mobile robot and the obstacle is larger than the size of the mobile robot, the cost value of the mobile robot is attenuated by a Gaussian function, namely, the mobile robot is far away from the obstacle within a certain range.

8. The robot volume-based mobile robot path planning method according to claim 1 or 5, wherein: the preset distance includes being greater than d₀。

Technical Field

The invention relates to the technical field of path planning, signal processing and Internet of things, in particular to a mobile robot path planning method based on robot volume.

Background

The cost of the robot in operation should be considered from the following aspects: the size of the robot is only one point on a map in an ideal environment, and the influence brought by the size is not considered, however, in practical application, the robot is expected to avoid a barrier and keep a certain safe distance with the barrier, in a classical path search algorithm, in order to ensure that a search result is the shortest path, the situation that the final path is closely attached to the barrier or a wall and the like is often caused, and the path has the shortest movement distance, but brings the danger of collision between the robot and the edge of the barrier, and is not beneficial to the movement actions of the robot such as steering and the like; the motion distance of the robot can be known from common knowledge, under the condition that the motion environments are basically consistent, the motion energy consumption of the robot is in positive correlation with the motion distance, namely the longer the motion distance is, the more energy is consumed by the robot, for a mobile robot system, the motion distance of the robot is expected to be reduced as far as possible on the premise of completing the motion target of the robot, and the point can be achieved by using a classical algorithm of path planning.

In addition, compared with Dijkstra algorithm, although the a-algorithm adds a heuristic function to greatly improve the path search efficiency, the a-algorithm still shows unsatisfactory search efficiency in practical application for more complex or large-range motion environments; the method is characterized in that the heuristic function in the classic A-x algorithm is an estimation of the distance between the current node and the target node, if the estimation is completely accurate under the ideal barrier-free condition, the A-x algorithm can be completely searched according to the optimal path, and the searching efficiency is highest under the condition; however, in an actual environment with obstacles or a complex environment, the heuristic function often cannot accurately estimate the distance, and if the estimated distance value of the heuristic function is larger than the actual distance value, the path given by the algorithm may not be the shortest path; on the contrary, if the estimated distance value of the heuristic function is smaller than the actual distance value, the algorithm can give the most solution, but the smaller the heuristic function is, the more nodes the algorithm needs to search are, and the lower the overall time efficiency is.

Disclosure of Invention

This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.

The present invention has been made in view of the above-mentioned conventional problems.

Therefore, the technical problem solved by the invention is as follows: in the prior art, the mobile robot has low working efficiency, is easy to unnecessarily collide with an obstacle, and particularly, at a corner, the mobile robot with a certain size almost always collides with the corner of the obstacle.

In order to solve the technical problems, the invention provides the following technical scheme: collecting the overall dimension information of the used mobile robot, and measuring the distance between the mobile robot and an obstacle in the motion process in real time; calculating the motion cost of the mobile robot in real time based on the size information and the distance data, and adding the motion cost into a total cost function of an A-algorithm to obtain an optimized A-algorithm cost function; and planning a path of the environment with the obstacle according to the optimized A-x algorithm cost function to obtain a path keeping a preset distance from the obstacle.

As a preferable solution of the robot volume-based mobile robot path planning method of the present invention, wherein: and measuring the distance between the mobile robot and the obstacle in the moving process in real time by using a distance measuring sensor.

As a preferable solution of the robot volume-based mobile robot path planning method of the present invention, wherein: the total cost function of the a-algorithm includes,

F(n)＝G(n)+H(n)

wherein g (n) represents the cost from the initial node to any node n, and h (n) represents the heuristically evaluated cost from node n to the target node.

As a preferable solution of the robot volume-based mobile robot path planning method of the present invention, wherein: the a-x algorithm describes its cost with + ∞forobstacles that cannot be passed through.

As a preferable solution of the robot volume-based mobile robot path planning method of the present invention, wherein: defining a cost of motion of a mobile robot for a certain node in the vicinity of the obstacle comprises,

wherein d represents the distance from the center of the robot to the obstacle, d₀Denotes the outer dimension of the robot, w₁The weight coefficient is expressed, e is a natural base number, and σ is a standard deviation and represents the curve width.

As a preferable solution of the robot volume-based mobile robot path planning method of the present invention, wherein: adding the motion cost into the total cost function of the A-algorithm to obtain an optimized A-algorithm cost function,

F(n)＝G(n)+H(n)+R₁(n)。

as a preferable solution of the robot volume-based mobile robot path planning method of the present invention, wherein: the criterion for judging whether the mobile robot collides with the obstacle includes that when the distance between the mobile robot and the obstacle is smaller than the size of the mobile robot, the cost is + ∞, namely the mobile robot can firstly collide with the obstacle under the condition; when the distance between the mobile robot and the obstacle is larger than the size of the mobile robot, the cost value of the mobile robot is attenuated by a Gaussian function, namely, the mobile robot is far away from the obstacle within a certain range.

As a preferable solution of the robot volume-based mobile robot path planning method of the present invention, wherein: the preset distance includes being greater than d₀。

The invention has the beneficial effects that: the method can start to judge the avoidance distance of the barrier in advance, and can effectively avoid the conflict with the barrier on the basis of reducing the time complexity of the algorithm.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:

fig. 1 is a schematic basic flow chart of a mobile robot path planning method based on a robot volume according to an embodiment of the present invention;

fig. 2 is a schematic diagram of a robot volume and an obstacle distance of a mobile robot path planning method based on a robot volume according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a path planning result of a classical a-x algorithm based on a mobile robot path planning method for a robot volume under a grid map by using euclidean distance according to an embodiment of the present invention;

fig. 4 is a schematic diagram illustrating comparison of a path search result by a path a × algorithm after considering a robot volume in a robot volume-based mobile robot path planning method according to an embodiment of the present invention.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.

Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.

The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.

Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

Example 1

Referring to fig. 1 to 2, in an embodiment of the present invention, a method for planning a path of a mobile robot based on a robot volume is provided, including:

s1: collecting the overall dimension information of the used mobile robot, and measuring the distance between the mobile robot and an obstacle in the motion process in real time;

the distance measuring method comprises the following steps of measuring the distance between the mobile robot and an obstacle in the moving process of the mobile robot in real time by using a distance measuring sensor.

S2: calculating the motion cost of the mobile robot in real time based on the size information and the distance data, and adding the motion cost into the total cost function of the A-algorithm to obtain an optimized A-algorithm cost function;

specifically, the total cost function of the a-algorithm includes:

F(n)＝G(n)+H(n)

wherein g (n) represents the cost from the initial node to any node n, and h (n) represents the heuristically evaluated cost from node n to the target node.

The a-x algorithm describes its cost using + ∞forobstacles that cannot be passed through.

Defining the movement cost of a certain node near an obstacle to the mobile robot comprises the following steps:

wherein d represents the distance from the center of the robot to the obstacle, d₀Denotes the outer dimension of the robot, w₁Represents the weight coefficient, e represents the natural base number, σ is the standard deviation, and represents the curve width, as shown in fig. 2.

Adding the motion cost into a total cost function of the A-algorithm to obtain an optimized A-algorithm cost function, wherein the method comprises the following steps:

F(n)＝G(n)+H(n)+R₁(n)。

s3: and planning a path of the environment with the obstacle according to the optimized A-x algorithm cost function to obtain a path keeping a preset distance from the obstacle.

Specifically, the criterion for determining whether the mobile robot collides with the obstacle includes:

when the distance between the robot and the obstacle is smaller than the size of the mobile robot, the cost is + ∞, namely the mobile robot can firstly collide with the obstacle under the condition;

when the distance between the robot and the obstacle is larger than the size of the mobile robot, the cost value of the robot is attenuated in a Gaussian function mode, namely the mobile robot is far away from the obstacle within a certain range;

wherein the predetermined distance includes being greater than d₀。

When the cost is calculated, the distance is used as a unique evaluation standard, and the volume of the robot is used as an evaluation standard of the cost function, so that the optimization target is the minimum of the redefined total cost function; in addition, by optimizing the heuristic function algorithm, the heuristic function can more accurately estimate the relationship between the current node and the target node under the condition of obstacles, so that the total number of nodes required to be searched by the optimized algorithm is reduced under the same condition, and the time efficiency of the optimized algorithm is remarkably improved.

Example 2

Referring to fig. 3 to 4, another embodiment of the present invention is different from the first embodiment in that a verification test of a mobile robot path planning method based on a robot volume is provided, and to verify and explain technical effects adopted in the method, the embodiment adopts a conventional technical scheme and the method of the present invention to perform a comparison test, and compares test results by means of scientific demonstration to verify a real effect of the method.

As shown in fig. 3, the conventional classical a-algorithm gives many unnecessary turns to the path, which greatly affects the working efficiency of the robot and makes the mobile robot easily collide with the obstacle unnecessarily during the practical application; in order to verify that the method has higher working efficiency compared with the conventional method and can effectively avoid conflict with obstacles, the working efficiency of the mobile robot is measured and compared in real time by adopting the conventional a-x algorithm and the method in the embodiment. The experimental result is shown in fig. 4, it can be seen from the figure that, before the optimization of the cost function is performed, the obtained path can complete the task of bypassing the obstacle and searching for the shortest path, however, the obtained path before the optimization is advanced along the edge of the obstacle, in the practical application process, the mobile robot is easy to unnecessarily collide with the obstacle, especially at the corner (the upper right corner of the obstacle in the figure), the mobile robot with a certain size almost always collides with the corner of the obstacle, while the optimized path result (the dotted line) in the figure starts avoiding the obstacle in advance, so that the obtained path may be increased in the movement distance of the mobile robot compared with the classical a function before the optimization, but can effectively avoid the collision with the obstacle, and in addition, the optimized path only near the obstacle shows a difference from the classical a algorithm, as shown in fig. 4, the two paths before and after optimization completely coincide with each other at the front and rear ends.

It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.