CN111207756A - Mobile robot path planning method based on improved artificial potential field algorithm - Google Patents

Mobile robot path planning method based on improved artificial potential field algorithm Download PDF

Info

Publication number
CN111207756A
CN111207756A CN202010194141.7A CN202010194141A CN111207756A CN 111207756 A CN111207756 A CN 111207756A CN 202010194141 A CN202010194141 A CN 202010194141A CN 111207756 A CN111207756 A CN 111207756A
Authority
CN
China
Prior art keywords
mobile robot
force
att
potential field
current
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010194141.7A
Other languages
Chinese (zh)
Other versions
CN111207756B (en
Inventor
罗元
杨成杰
张毅
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University of Post and Telecommunications
Original Assignee
Chongqing University of Post and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing University of Post and Telecommunications filed Critical Chongqing University of Post and Telecommunications
Priority to CN202010194141.7A priority Critical patent/CN111207756B/en
Publication of CN111207756A publication Critical patent/CN111207756A/en
Application granted granted Critical
Publication of CN111207756B publication Critical patent/CN111207756B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Medical Informatics (AREA)
  • Game Theory and Decision Science (AREA)
  • Evolutionary Computation (AREA)
  • Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Manipulator (AREA)

Abstract

The invention provides a mobile robot path planning method based on an improved artificial potential field algorithm. The method comprises the following specific steps: firstly, constructing an attractive force potential field function according to the distance from a mobile robot to a target point, and constructing a repulsive force potential field function according to the distance from the mobile robot to the target point and the distance from an obstacle; then, respectively solving a negative gradient of the attraction force potential field function and the repulsion force potential field function to obtain the attraction force and the repulsion force applied to the mobile robot; and finally, the mobile robot avoids the obstacle to advance to the target point under the combined action of the attraction force and the repulsion force, and a forced interference force is generated midway to enable the robot to escape from the local minimum point influence range. Experimental results in an indoor multi-obstacle environment show that: compared with other methods, the method has higher success rate of path planning and meets the requirement of practical application.

Description

Mobile robot path planning method based on improved artificial potential field algorithm
Technical Field
The invention belongs to the field of autonomous navigation of mobile robots, and particularly relates to a mobile robot path planning method based on an improved artificial potential field algorithm.
Background
Path planning is an important step in the autonomous navigation process of a mobile robot, and refers to: the mobile robot plans a safe collision-free path from the starting point to the target point according to the actual situation. The path planning of the mobile robot in the working environment needs to reach the following three standards: (1) the planned path can connect the starting point and the target point; (2) the planned path must effectively avoid obstacles in the environment; (3) the planned path meets the requirements in terms of length, smoothness and the like.
The standard artificial potential field method has the advantages of simple principle, easy realization, small calculated amount and strong real-time property, so the method is widely applied to the field of path planning of the mobile robot. However, when path planning is performed in an indoor multi-obstacle environment using standard artificial potential field methods, target unreachability problems and the problem of falling into local minima often occur. The target unreachable problem is as follows: when the target point is within the obstacle influence range, the mobile robot loiters around the target point without staying at the target point position. The problem of trapping local minimum points is that: when the force of the mobile robot is balanced at a certain non-target point position, the robot stops moving and does not continue to advance to the target point. The occurrence of these two problems can have a serious impact on the whole path planning process of the mobile robot, and eventually lead to the failure of the path planning operation.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A mobile robot path planning method based on an improved artificial potential field method and suitable for an indoor multi-obstacle environment is provided. The method comprises the following steps:
and S1, constructing an attractive force potential field function according to the distance from the mobile robot to the target point.
And S2, constructing a repulsive potential field function according to the distance from the mobile robot to the target point and the distance from the mobile robot to the obstacle.
And S3, obtaining the current attractive force of the mobile robot by solving a negative gradient of the attractive force potential field function based on the current position of the mobile robot.
And S4, obtaining the currently applied repulsive force of the mobile robot by solving a negative gradient of the repulsive force potential field function based on the current position of the mobile robot.
S5, guiding the mobile robot to advance to the target point using the attractive force and the repulsive force calculated in steps S3 and S4, including:
if the resultant force of the current attractive force and the current repulsive force is 0 and the current position of the mobile robot is the target point, path planning is finished;
if the resultant force of the current attractive force and the current repulsive force is 0 and the current position of the mobile robot is not a target point, the mobile robot falls into a local minimum value point, a forced interference force is generated to enable the mobile robot to escape from the position influence range to reach a new position, and then path planning operation is continuously executed;
if the resultant force of the current attractive force and the current repulsive force is not 0, the mobile robot moves one step to a new position under the combined action of the current attractive force and the current repulsive force, and then the step S3 is continuously executed.
The invention has the following advantages and beneficial effects:
the invention provides a mobile robot path planning method based on an improved artificial potential field method, aiming at the problems that targets are not reachable and fall into local minimum points which often occur when a standard artificial potential field method is used for path planning in an indoor multi-obstacle environment. The method introduces the distance from the mobile robot to the target point processed by the arctangent function into the repulsive potential field function of the standard artificial potential field method to solve the problem that the target cannot be reached. Wherein the arctan function exists in the sense that: the deformation of the repulsive force potential field function caused by introducing the distance from the mobile robot to the target point is reduced, and the quality of the planned path is further ensured not to be reduced. In addition, the method solves the problem that the vehicle can not escape due to the fact that the vehicle falls into a local minimum value point by breaking the stress balance through the forced interference force constructed on the basis of the attraction force. The invention introduces the distance between the mobile robot and the target point processed by the arc tangent function and the forced interference force generated by the attraction, and the two factors act synergistically, thereby improving the success rate of path planning of the mobile robot in the indoor multi-obstacle environment and simultaneously ensuring that the path planned by the mobile robot is shorter.
Drawings
FIG. 1 is a mobile robot path planning implementation framework based on an improved artificial potential field method according to a preferred embodiment of the invention;
FIG. 2 is an exploded schematic view of an attractive force;
fig. 3 is a schematic diagram of forced disturbance force generation.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
Referring to fig. 1, the main steps of the present invention are:
s1, initializing relevant parameters, and setting a path planning starting point and a target point;
s2, constructing an attraction potential field function according to the distance from the mobile robot to the target point;
s3, constructing a repulsive potential field function according to the distance from the mobile robot to the target point and the distance from the mobile robot to the obstacle;
s4, obtaining the current attractive force of the mobile robot by solving the negative gradient of the attractive force potential field function based on the current position of the mobile robot;
s5, obtaining the repulsive force currently applied to the mobile robot by solving a negative gradient of the repulsive force potential field function based on the current position of the mobile robot;
and S6, if the resultant force of the current attractive force and the current repulsive force is 0 and the current position of the mobile robot is the target point, the path planning is completed. And if the resultant force of the current attractive force and the current repulsive force is 0 and the current position of the mobile robot is not the target point, generating a forced interference force to enable the mobile robot to escape from the position influence range to reach a new position, and then continuing to execute the path planning operation. And if the resultant force of the current attractive force and the current repulsive force is not 0, the mobile robot moves one step to a new position under the combined action of the current attractive force and the current repulsive force, and then the path planning operation is continuously executed.
The specific implementation process of step S2 is:
constructing an attractive force potential field function U according to the distance from the mobile robot to a target pointattThe concrete formula is as follows:
Figure BDA0002416966600000021
in the above equation, k represents an attraction gain coefficient.Representing the distance of the mobile robot to the target point. Wherein (x)R,yR) World coordinates representing the mobile robot; (x)G,yG) Representing world coordinates of the target point.
The specific implementation process of step S3 is:
constructing a repulsive force potential field function U according to the distance from the mobile robot to a target point and the distance from the mobile robot to an obstaclerepThe specific formula is as follows:
Figure BDA0002416966600000032
in the above equation, η denotes the repulsion gain factor.
Figure BDA0002416966600000033
Indicating the distance of the mobile robot to the obstacle. Wherein (x)o,yo) Representing world coordinates of the obstacle. RhooRepresenting the maximum impact distance of the obstacle.
Figure BDA0002416966600000034
Representing the arctan restriction function. n represents a distance adjustment coefficient.
The specific implementation process of step S4 is:
s41: according to the current position of the mobile robot, the gravitational potential field function U is selectedattObtaining the current attractive force F 'received by the mobile robot by solving the negative gradient'attThe concrete formula is as follows:
F′att＝-grad(Uatt)＝-k·(x′R-xG)i-k·(y′R-yG)j
in the above formula, (x'R,y′R) World coordinates representing the current position of the mobile robot. i and j respectively denote square along the horizontal axis of the world coordinate systemUnit vectors in the positive direction of the vertical axis.
S42: obtaining attractive force F'attIs of a size of
Figure BDA0002416966600000035
The direction is directed by the mobile robot to a target point.
The specific implementation process of step S5 is:
s51: according to the current position of the mobile robot to the repulsive force potential field function UrepObtaining the current attractive force F 'received by the mobile robot by solving the negative gradient'repThe concrete formula is as follows:
Figure BDA0002416966600000036
wherein, F'x,repAnd F'y,repThe specific expressions of (A) are as follows:
Figure BDA0002416966600000037
Figure BDA0002416966600000041
in the above two formulas, the first and second groups,
Figure BDA0002416966600000042
indicating the distance of the current mobile robot to the target point.
Figure BDA0002416966600000043
Indicating the distance of the current mobile robot to the obstacle.
S52: obtaining repulsive force F'repIs of a size of
Figure BDA0002416966600000044
The direction is directed by the mobile robot to a target point.
The specific implementation process of step S6 is:
s61: if the resultant force of the current attractive force and the current repulsive force is 0 and the current position of the mobile robot is the target point, path planning is finished;
s62: and if the resultant force of the current attractive force and the current repulsive force is 0 and the current position of the mobile robot is not the target point, the mobile robot sinks into a local minimum value point. At this time, (1) to (8) are executed in order;
(1) storing the current position (i.e. the local minimum point position) of the mobile robot in a world coordinate system XwOwYwThe coordinate of (5) is marked as a;
(2) using the current position of the mobile robot as an origin and the current attraction force F'attThe direction deviation (including left deviation and right deviation) theta is the positive direction of the horizontal axis, and a rectangular coordinate system X is establishedNONYN. Wherein theta is in the value range of
Figure BDA0002416966600000045
Random angle of (2). In this embodiment, a rectangular coordinate system X is established with the right deviation θ as the positive direction of the horizontal axisNONYN. The same holds true to the left.
(3) In rectangular coordinate system XNONYNIn (c), attraction force F'attDecomposed along the horizontal and vertical coordinate axes to obtain two component attractive forces which are respectively marked as F'X,attAnd F'Y,att. Wherein, F'X,attIs expressed as (| F'att|cosθ,0)，F′Y,attIs expressed as (0, | F'att|sinθ)。
(4) Utilizing random coefficient to F'X,attAnd F'Y,attThe modification is carried out to obtain two new component attractive forces,
Figure BDA0002416966600000046
and
Figure BDA0002416966600000047
Figure BDA0002416966600000048
is represented by the coordinates of (α | F'att|cosθ,0)，
Figure BDA00024169666000000415
Is expressed as (0, β | F'attI sin θ), wherein α and β both represent a value range of 0.5,1.5]The random coefficient of (2). Note that two random coefficients cannot take the same value at the same time;
(5) will be provided with
Figure BDA00024169666000000416
And
Figure BDA00024169666000000417
synthesized to obtain a new attractive force which is recorded as
Figure BDA00024169666000000411
The attraction is the key to the mobile robot to escape from the local minimum point, and is called forced interference force. In a rectangular coordinate system XNONYNCoordinate of (D) is represented by (α | F'att|cosθ,β|F′att|sinθ)。
(6) According to
Figure BDA00024169666000000412
In rectangular coordinate system XNONYNThe coordinate representation in (1) is calculated to be the coordinate representation in the world coordinate system
Figure BDA00024169666000000413
The specific formula is as follows:
Figure BDA00024169666000000414
in the above formula, ∠ F'attDenotes the current attractive force F'attDeflection angle in the world coordinate system.
(7) Determination of the force of the forced disturbance
Figure BDA0002416966600000051
The size in the world coordinate system is
Figure BDA0002416966600000052
Has a deflection angle of
Figure BDA0002416966600000053
(8) Keeping the magnitude and the deflection angle of the forced interference force unchanged, enabling the mobile robot to move 5 steps under the combined action of the force, the received attractive force and the repulsive force to reach a certain position point, and recording the world coordinate of the position point as b. At this time, the total force received by the mobile robot at the b position is calculated without considering the influence of the forcible interference force. If the resultant force and the vector
Figure BDA0002416966600000054
Is greater than
Figure BDA0002416966600000055
The mobile robot has jumped out of the local minimum point. And then, removing the forced interference force, and continuing executing the path planning operation by the mobile robot. If the resultant force and the vector
Figure BDA0002416966600000056
Is less than or equal to
Figure BDA0002416966600000057
The forcible interference force is canceled and the process returns to (2) to regenerate the forcible interference force.
S63: and if the resultant force of the current attractive force and the current repulsive force is not 0, the mobile robot moves one step along the direction of the resultant force to reach a new position, and then the path planning operation is continuously executed.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (8)

1. A mobile robot path planning method based on an improved artificial potential field algorithm is characterized by comprising the following steps:
s1, constructing an attraction potential field function according to the distance from the mobile robot to the target point;
s2, constructing a repulsive potential field function according to the distance from the mobile robot to the target point and the distance from the mobile robot to the obstacle;
s3, obtaining the current attractive force of the mobile robot by solving the negative gradient of the attractive force potential field function based on the current position of the mobile robot;
s4, obtaining the repulsive force currently applied to the mobile robot by solving a negative gradient of the repulsive force potential field function based on the current position of the mobile robot;
s5, guiding the mobile robot to advance toward the target point using the attractive force and the repulsive force calculated in steps S3 and S4, including,
if the resultant force of the current attractive force and the current repulsive force is 0 and the current position of the mobile robot is the target point, path planning is finished;
if the resultant force of the current attractive force and the current repulsive force is 0 and the current position of the mobile robot is not a target point, the mobile robot falls into a local minimum value point, a forced interference force is generated to enable the mobile robot to escape from the position influence range to reach a new position, and then path planning operation is continuously executed;
if the resultant force of the current attractive force and the current repulsive force is not 0, the mobile robot moves one step to a new position under the combined action of the current attractive force and the current repulsive force, and then the step S3 is continuously executed.
2. The method for planning the path of the mobile robot based on the improved artificial potential field algorithm according to claim 1, is characterized in that: step S1 is the attraction potential field function UattThe specific formula of (A) is as follows:
Figure FDA0002416966590000011
in the formula, k represents an attraction gain coefficient,
Figure FDA0002416966590000012
represents the distance from the mobile robot to the target point, (x)R,yR) World coordinates representing a mobile robot, (x)G,yG) Representing world coordinates of the target point.
3. The method for planning the path of the mobile robot based on the improved artificial potential field algorithm according to claim 1, is characterized in that: repulsive potential field function U of step S2repThe specific formula of (A) is as follows:
Figure FDA0002416966590000013
where η denotes the rejection gain factor,
Figure FDA0002416966590000014
represents the distance from the mobile robot to the obstacle, (x)o,yo) World coordinate, p, representing an obstacleoThe maximum influence distance of the obstacle is represented,
Figure FDA0002416966590000015
represents an arctangent limiting function and n represents a distance adjustment coefficient.
4. The method for planning the path of the mobile robot based on the improved artificial potential field algorithm according to claim 1, is characterized in that: step S3 shows that the mobile robot is currently attracted by attractive force F'attThe method is calculated by the following method, and the specific formula is as follows:
F′att＝-grad(Uatt)＝-k·(x′R-xG)i-k·(y′R-yG)j
wherein (x'R,y′R) The world coordinate system comprises world coordinates representing the current position of the mobile robot, and i and j respectively represent unit vectors along the positive direction of a horizontal axis and the positive direction of a vertical axis of the world coordinate system;
and further attractive force F'attIs of a size of
Figure FDA0002416966590000021
The direction is directed by the mobile robot to a target point.
5. The method for planning the path of the mobile robot based on the improved artificial potential field algorithm according to claim 1, is characterized in that: repulsive force F 'currently applied to the mobile robot in step S4'repThe concrete formula is as follows:
Figure FDA0002416966590000022
wherein, F'x,repAnd F'y,repThe specific expressions of (A) are as follows:
Figure FDA0002416966590000023
Figure FDA0002416966590000024
in the above two formulas, the first and second groups,
Figure FDA0002416966590000025
indicating the distance of the current mobile robot to the target point,
Figure FDA0002416966590000026
indicating a distance from the current mobile robot to the obstacle;
to give a repulsive force F'repIs of a size of
Figure FDA0002416966590000027
The direction is directed by the mobile robot to a target point.
6. The method for planning the path of the mobile robot based on the improved artificial potential field algorithm according to any one of claims 1 to 5, characterized in that: s, when the mobile robot falls into the local minimum value point, the following operations are carried out:
(1) saving the current position of the mobile robot in the world coordinate system XwOwYwThe coordinate of (5) is marked as a;
(2) constructing a forced interference force by using the current attraction force;
(3) keeping the magnitude and the deflection angle of the forced interference force unchanged, enabling the mobile robot to move 5 step lengths under the combined action of the force, the received attraction force and the repulsive force to reach a certain position point, and recording the world coordinate of the position point as b;
(4) the influence of the forced interference force is not considered, and the resultant force of the attractive force and the repulsive force of the mobile robot at the position b is calculated; if the resultant force and the vector
Figure FDA0002416966590000028
Is greater than
Figure FDA0002416966590000029
Canceling the forced interference force, and continuing to execute path planning operation by the mobile robot; if the resultant force and the vector
Figure FDA0002416966590000031
Is less than or equal to
Figure FDA0002416966590000032
The forcible interference force is cancelled and the step (2) is returned to regenerate the forcible interference force.
7. The method for planning the path of the mobile robot based on the improved artificial potential field algorithm according to claim 6, characterized in that: the step of constructing the forced interference force comprises the following steps:
(2.1) with the current position of the mobile robot as the origin and the current attraction force F'attThe direction deviation theta is the positive direction of the horizontal axis, and a rectangular coordinate system X is establishedNONYNTheta is in the value range
Figure FDA0002416966590000033
Random angle of (d);
(2.2) in a rectangular coordinate system XNONYNIn (c), attraction force F'attDecomposed along the horizontal and vertical coordinate axes to obtain two component attractive forces which are respectively marked as F'X,attAnd F'Y,att，F′X,attIs expressed as (| F'att|cosθ,0)，F′Y,attIs expressed as (0, | F'att|sinθ)；
(2.3) pairing F 'by random coefficient'X,attAnd F'Y,attThe modification is carried out to obtain two new component attractive forces,
Figure FDA0002416966590000034
and
Figure FDA0002416966590000035
Figure FDA0002416966590000036
is represented by the coordinates of (α | F'att|cosθ,0)，
Figure FDA0002416966590000037
Is expressed as (0, β | F'attI sin θ), wherein α and β both represent a value range of 0.5,1.5]The random coefficient of (a);
(2.4) mixing
Figure FDA0002416966590000038
And
Figure FDA00024169665900000317
synthesized to obtain a new attractive force, which is recorded as a forced interference force
Figure FDA00024169665900000318
In a rectangular coordinate system XNONYNCoordinate of (D) is represented by (α | F'att|cosθ,β|F′att|sinθ)；
(2.5) according to
Figure FDA00024169665900000311
In rectangular coordinate system XNONYNThe coordinate representation in (1) is calculated to be the coordinate representation in the world coordinate system
Figure FDA00024169665900000312
The specific formula is as follows:
Figure FDA00024169665900000313
in the above formula, ∠ F'attDenotes the current attractive force F'attA yaw angle in a world coordinate system;
(2.6) determination of the forced disturbance force
Figure FDA00024169665900000314
The size in the world coordinate system is
Figure FDA00024169665900000315
Has a deflection angle of
Figure FDA00024169665900000316
8. The method for planning the path of the mobile robot based on the improved artificial potential field algorithm as claimed in claim 7, wherein the two random coefficients α and β cannot take the same value at the same time.
CN202010194141.7A 2020-03-19 2020-03-19 Mobile robot path planning method based on improved artificial potential field algorithm Active CN111207756B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010194141.7A CN111207756B (en) 2020-03-19 2020-03-19 Mobile robot path planning method based on improved artificial potential field algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010194141.7A CN111207756B (en) 2020-03-19 2020-03-19 Mobile robot path planning method based on improved artificial potential field algorithm

Publications (2)

Publication Number Publication Date
CN111207756A true CN111207756A (en) 2020-05-29
CN111207756B CN111207756B (en) 2022-08-23

Family

ID=70787757

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010194141.7A Active CN111207756B (en) 2020-03-19 2020-03-19 Mobile robot path planning method based on improved artificial potential field algorithm

Country Status (1)

Country Link
CN (1) CN111207756B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111736611A (en) * 2020-07-06 2020-10-02 中国计量大学 Mobile robot path planning method based on A-star algorithm and artificial potential field algorithm
CN111897328A (en) * 2020-07-17 2020-11-06 武汉理工大学 Path planning method, device and equipment based on improved artificial potential field method
CN112068560A (en) * 2020-08-28 2020-12-11 的卢技术有限公司 Robot path planning method based on improved artificial potential field method
CN112180954A (en) * 2020-07-28 2021-01-05 北京理工大学 Unmanned aerial vehicle obstacle avoidance method based on artificial potential field
CN112907187A (en) * 2021-03-13 2021-06-04 深圳市土地公网络科技有限公司 Distribution route planning method and device, electronic equipment and storage medium
CN117369482A (en) * 2023-12-06 2024-01-09 华润数字科技有限公司 Path planning method, device and equipment for mobile robot and storage medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120158220A1 (en) * 2010-12-20 2012-06-21 Selex Systemi Integrati S.P.A. Fast vertical trajectory prediction method for air traffic management, and relevant atm system
CN107065866A (en) * 2017-03-24 2017-08-18 北京工业大学 A kind of Mobile Robotics Navigation method based on improvement optical flow algorithm
CN107643764A (en) * 2017-10-20 2018-01-30 中国人民解放军海军航空工程学院 A kind of unmanned vehicle barrier-avoiding method based on bispin Lyapunov vector fields
CN108693879A (en) * 2018-04-28 2018-10-23 上海理工大学 Method for planning path for mobile robot based on modified embedded-atom method
CN110471429A (en) * 2019-09-19 2019-11-19 华南农业大学 Grass-removing robot Real-time Obstacle Avoidance Method based on modified embedded-atom method
CN110531762A (en) * 2019-08-21 2019-12-03 东南大学 A kind of robot path planning method based on modified embedded-atom method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120158220A1 (en) * 2010-12-20 2012-06-21 Selex Systemi Integrati S.P.A. Fast vertical trajectory prediction method for air traffic management, and relevant atm system
CN107065866A (en) * 2017-03-24 2017-08-18 北京工业大学 A kind of Mobile Robotics Navigation method based on improvement optical flow algorithm
CN107643764A (en) * 2017-10-20 2018-01-30 中国人民解放军海军航空工程学院 A kind of unmanned vehicle barrier-avoiding method based on bispin Lyapunov vector fields
CN108693879A (en) * 2018-04-28 2018-10-23 上海理工大学 Method for planning path for mobile robot based on modified embedded-atom method
CN110531762A (en) * 2019-08-21 2019-12-03 东南大学 A kind of robot path planning method based on modified embedded-atom method
CN110471429A (en) * 2019-09-19 2019-11-19 华南农业大学 Grass-removing robot Real-time Obstacle Avoidance Method based on modified embedded-atom method

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
EMAM FATHY MOHAMED: "An improved Tangent Bug method integrated with artificial potential field for multi-robot path planning", 《2011 INTERNATIONAL SYMPOSIUM ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS》 *
SHAORONG XIE: "The obstacle avoidance planning of USV based on improved artificial potential field", 《2014 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA)》 *
YUANCHEN ZHAO: "UAV formation control with obstacle avoidance using improved artificial potential fields", 《2017 36TH CHINESE CONTROL CONFERENCE (CCC)》 *
耿以才: "基于动态人工势场法无人驾驶汽车路径规划研究", 《中国优秀博硕士学位论文全文数据库（硕士）工程科技Ⅱ辑》 *
胡运强: "仿人机器人运动姿态的动态多目标优化研究", 《中国优秀博硕士学位论文全文数据库（硕士）信息科技辑》 *
赵梦媛: "基于人工势能场的水下滑翔机局部路径规划方法研究", 《中国优秀博硕士学位论文全文数据库（硕士）工程科技Ⅱ辑》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111736611A (en) * 2020-07-06 2020-10-02 中国计量大学 Mobile robot path planning method based on A-star algorithm and artificial potential field algorithm
CN111897328A (en) * 2020-07-17 2020-11-06 武汉理工大学 Path planning method, device and equipment based on improved artificial potential field method
CN111897328B (en) * 2020-07-17 2022-02-15 武汉理工大学 Path planning method, device and equipment based on improved artificial potential field method
CN112180954A (en) * 2020-07-28 2021-01-05 北京理工大学 Unmanned aerial vehicle obstacle avoidance method based on artificial potential field
CN112068560A (en) * 2020-08-28 2020-12-11 的卢技术有限公司 Robot path planning method based on improved artificial potential field method
CN112907187A (en) * 2021-03-13 2021-06-04 深圳市土地公网络科技有限公司 Distribution route planning method and device, electronic equipment and storage medium
CN117369482A (en) * 2023-12-06 2024-01-09 华润数字科技有限公司 Path planning method, device and equipment for mobile robot and storage medium
CN117369482B (en) * 2023-12-06 2024-03-12 华润数字科技有限公司 Path planning method, device and equipment for mobile robot and storage medium

Also Published As

Publication number Publication date
CN111207756B (en) 2022-08-23

Similar Documents

Publication Publication Date Title
CN111207756B (en) Mobile robot path planning method based on improved artificial potential field algorithm
CN110471429B (en) Mowing robot real-time obstacle avoidance method based on improved artificial potential field method
CN111897328B (en) Path planning method, device and equipment based on improved artificial potential field method
CN106708054B (en) Routing planning method for inspection robot by combining map grids and potential field method obstacle avoidance
CN104526695B (en) A kind of space manipulator method for planning track minimizing pedestal collision disturbance
Sun et al. A review of robot control with visual servoing
CN109976347B (en) Visual servo path planning method based on rapid expansion random tree and potential field method
CN110977989B (en) Motion planning and real-time obstacle avoidance method and system for cooperative robot
CN107966907B (en) Obstacle avoidance solution applied to redundant manipulator
CN111522351B (en) Three-dimensional formation and obstacle avoidance method for underwater robot
CN108247631B (en) Mechanical arm autonomous robust singularity avoiding method for improving path tracking performance
CN110561420B (en) Arm profile constraint flexible robot track planning method and device
Liang et al. Unmanned aerial transportation system with flexible connection between the quadrotor and the payload: modeling, controller design, and experimental validation
CN112068560A (en) Robot path planning method based on improved artificial potential field method
WO2023116129A1 (en) Compliant force control method and system for collaborative robot
Mechali et al. Finite-time attitude control of uncertain quadrotor aircraft via continuous terminal sliding-mode-based active anti-disturbance approach
CN113199481B (en) Robot motion control method, device, electronic apparatus, and medium
Guo et al. Obstacle avoidance planning for quadrotor UAV based on improved adaptive artificial potential field
Liu et al. Trajectory Planning with Pose Feedback for a Dual‐Arm Space Robot
CN114029940B (en) Motion path planning method, device, equipment, medium and mechanical arm
Qiao et al. Fast trajectory tracking control of underactuated autonomous underwater vehicles
CN114355771A (en) Cooperative robot force and position hybrid control method and system
CN114167725A (en) Cooperative robot trajectory tracking control method and system
Jin et al. A Synthetic Algorithm for Tracking a Moving Object in a Multiple‐Dynamic Obstacles Environment Based on Kinematically Planar Redundant Manipulators
TWI829561B (en) Method and system for path planning of robot arm in dynamic environment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant