CN107643752B - Omnidirectional mobile robot path planning algorithm based on pedestrian trajectory prediction - Google Patents
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Abstract
The invention provides an omnidirectional mobile robot path planning algorithm based on pedestrian track prediction, which comprises the following steps: s1, performing kinematic modeling by using a robot chassis and Mecanum wheels installed on the chassis; s2, planning a path by the robot according to the current coordinate and the target position coordinate; s3, the robot predicts the behavior track of the pedestrian in the visual field through the laser radar module, and calculates the possible range of the pedestrian within a certain time; and S4, planning the pedestrian avoiding path by the robot according to the possible range of the pedestrian. The omnidirectional mobile robot path planning algorithm based on pedestrian track prediction can enable the robot to move more flexibly; and tracking the pedestrians in the environment in real time through the laser radar module, and prejudging the positions of the pedestrians at the next moment according to the tracking result of the pedestrians.
Description
Technical Field
The invention belongs to the field of robot path planning, and particularly relates to an omnidirectional mobile robot path planning algorithm based on pedestrian trajectory prediction.
Background
Since the advent of the traditional industrial robots, robotics has made significant progress in as little as 60 years. Nowadays, robots are no longer limited to industrial applications, and the research focus and development direction thereof are more oriented towards daily life applications, such as catering, welcoming, entertainment, companions, and the like. Among many forms of mobile robots, omni-directional wheeled mobile robots are also receiving attention for their flexible kinematic performance. With the rapid development of modern sensing technology, computer technology and artificial intelligence technology, path planning of wheeled mobile robots and obstacle avoidance technology in the motion process also become research hotspots of robot technology.
The path planning problem can be divided into global path planning where the environmental information is completely known and local path planning where the environmental information is partially or even completely unknown. When the environmental information is partially or completely unknown, the robot senses the external environmental information through a sensing device mounted on the robot, and then reaches the destination in an approximate optimal path without collision. In the traditional local path planning, the robot is usually only enabled to avoid external obstacles in real time in the walking process, and the movement of external moving obstacles such as pedestrians is not pre-judged.
Disclosure of Invention
In view of the above, the present invention aims to provide a pedestrian trajectory prediction-based path planning algorithm for an omnidirectional mobile robot, so as to solve the problem that the current mobile robot has a single function and cannot make a motion prediction.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an omnidirectional mobile robot path planning algorithm based on pedestrian track prediction comprises the following steps:
s1, performing kinematic modeling by using a robot chassis and Mecanum wheels installed on the chassis;
s2, planning a path by the robot according to the current coordinate and the target position coordinate;
s3, the robot predicts the behavior track of the pedestrian in the visual field through the laser radar module, and calculates the possible range of the pedestrian within a certain time;
and S4, planning the pedestrian avoiding path by the robot according to the possible range of the pedestrian.
Further, in step S1, the robot chassis is equipped with three mecanum wheels, and the mecanum wheels are left-handed mecanum wheels.
Further, in step S1, the robot kinematics modeling method specifically includes:
s101, establishing a robot body coordinate system on a robot chassis, and establishing a wheel coordinate system on wheels of the robot;
s102, setting the X axis of the robot body coordinate system to each wheel coordinate system XiIncluded angle of axis being gammaiThe included angle between the rolling shaft of each wheel and the wheel shaft is alpha, and the connecting line between the central point O and the central point of each wheel is LiX-axis to L in robot body coordinate systemiIs thetai；
S103, setting the rotation angular velocities of the three wheel shafts to
The central movement speed of the robot is [ v ]x,vy,w]TThe radius of the outer wheel is R, and the radius of the roller is R;
s104, the inverse kinematics formula can be expressed as:
wherein,
the angle between the rolling axis of each wheel and the axle
Further, in step S2, the robot path planning implements path planning according to a path planning algorithm, the robot path planning includes global path planning and local path planning, the global path planning performs path planning according to a global grid map, current coordinates, and target position coordinates, an optimal global path from a start point to a destination point is correctly obtained, and after the optimal global path is determined, local path planning is started.
Furthermore, the local path planning adopts a DWA algorithm, the DWA algorithm collects multiple groups of speed data in a speed space, simulates tracks of the robot at the speeds within a certain time, then performs scoring evaluation on the tracks according to a scoring function, selects an optimal track, and generates a robot speed control command.
Further, the specific method for local path planning is as follows:
s201, because the robot moves in all directions, the position relation between a starting point and an end point needs to be analyzed first, a space quadrant of a speed space is determined, and the current moving speed of the robot is set to be (v)cx,vcy,wc)：
S202, setting the robot to have kinematic speed limit, and setting an x-direction speed limit, a y-direction speed limit and a w-rotation speed limit as follows respectively: v. ofx_min、vx_max、vy_min、vy_max、wmin、wmax
Vs＝{vx∈[vxmin,vxmax],vy∈[vymin,vymax],w∈[wmin,wmax]}
S203, influenced by the performance and the motion stability of the motor, the robot has the maximum acceleration in the motion, so that a cubic area V exists in the forward simulation period (delta t) of the track of the robotdThe speed in the cubic region is the speed that the robot can actually reach:
s204, the robot must stop before colliding with the obstacle due to safety limitation, so that an anti-collision speed area V can be obtaineda：
Wherein docc(vx,vyW) is the velocity (v)x,vyAnd w) the closest distance to the obstacle on the simulated trajectory.
The search space V of the DWA algorithm improved by the steps S202, S203 and S204 can be obtainedr＝Vs∩Vd∩Va。
S205, after obtaining the velocity space, in order to optimize the local path, each velocity (v) in Vr needs to be scored according to a certain scoring standardx,vyW) scoring;
the scoring function is as follows:
score＝α*dpath+β*dgoal+γ*docc
wherein, alpha, beta and gamma are respectively a path fitting weight, a distance target point position weight and a distance weight with an obstacle; doccIs velocity (v)x,vyW) maximum obstacle distance value on trajectory, dgoalIs velocity (v)x,vyW) distance of the trajectory end point to the global target point, dpathIs the degree of conformance of the trajectory to the global path.
Further, in step S3, the pedestrian trajectory prediction is implemented by a behavior trajectory prediction algorithm, when the system finds a person, the system outputs a relative coordinate between the pedestrian and the lidar module, and the relative coordinate is located in the robot body coordinate system, which is specifically implemented as follows:
let the coordinates of the current and the previous three pedestrians in the global coordinate system be pk＝(xk,yk)，pk-1＝(xk-1,yk-1)，pk-2＝(xk-2,yk-2)，pk-3＝(xk-3,yk-3) The time when the system acquires the coordinates is t4，t3，t2，t1. From these four sets of data, the average velocity of the robot over this period of time can be calculated:
let the predicted time be
Then
Position coordinates p at which a person may be after a timepre_k＝(xpre_k,ypre_k) Can be expressed as:
with ppre_kAs the center of a circle, drawing a circle with radius r, and within the circle, predicting time
The range S in which the person is likely to bek；
When a person is lost or retraced, the loop needs to be restarted and all variables initialized.
Further, in step S4, the pedestrian-avoiding path planning is implemented by an algorithm, which specifically includes:
s301, according to a local path planning algorithm, the robot plans an optimal track of future delta t time, and sets a last point p of the trackiThe coordinate is (x)i,yi). If the pedestrian tracking result exists, the coordinate point p of the pedestrian is assumed to be predicted at the momentpreThe coordinate is (x)pre,ypre)。
S302, if
The robot path planning point is not in the radiation circle of the pedestrian prediction point, the robot is relatively safe, and the robot can plan to walk according to the optimal track;
s303, if
The robot path planning point is located inside or at the edge of the radiation circle, and the next moment may touch the pedestrian, and then the path planning for fast escaping from the radiation circle is performed.
Further, when the robot is located in or at the edge of the radiation ring of the pedestrian prediction point, the moving linear speed of the robot escapes from the radiation ring at the fastest speed along the tangential direction of the circle, and the specific method is as follows:
let the coordinate of the last point of the predicted point of the local path of the robot be pi＝(xi,yi) Pedestrian prediction point ppreThe coordinate is (x)pre,ypre) At this time, the distance between two points
Let the maximum linear velocity of robot motion be vmaxMinimum linear velocity of vminAnd the distance between the two points and the linear velocity have a linear relation:
therefore, the escape linear velocity of the robot at this time should be:
the speed direction is divided into two parts which are respectively v1And v2According to a rotation matrix
Unit vectors for two speed directions are obtained:
according to the formula A and the formula B, the speeds in two tangential directions are obtained
And
according to a local path planning algorithm, respectively
And
and taking positive and negative ranges of 30 degrees as the center, and taking the positive and negative ranges as the planning speed to plan the local path of the robot.
Compared with the prior art, the omni-directional mobile robot path planning algorithm based on pedestrian track prediction has the following advantages:
the omnidirectional mobile robot path planning algorithm based on pedestrian track prediction can enable the robot to move more flexibly; tracking the pedestrians in the environment in real time through a laser radar module, and pre-judging the positions of the pedestrians at the next moment according to the tracking result of the pedestrians; and according to the pedestrian track pre-judgment result, adopting a speed combination near the direction of the predicted point connecting line perpendicular line to re-plan the path so as to achieve the purpose of avoiding pedestrians in advance.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic diagram illustrating a kinematics modeling principle of an omnidirectional mobile robot according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the improved DWA algorithm according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a principle of avoiding a pedestrian prediction point according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The omnidirectional mobile robot path planning algorithm based on pedestrian track prediction comprises the following steps:
s1, performing kinematic modeling by using a robot chassis and Mecanum wheels installed on the chassis;
s2, planning a path by the robot according to the current coordinate and the target position coordinate;
s3, the robot predicts the behavior track of the pedestrian in the visual field through the laser radar module, and calculates the possible range of the pedestrian within a certain time;
and S4, planning the pedestrian avoiding path by the robot according to the possible range of the pedestrian.
Wherein the robot comprises three left-handed mecanum wheels mounted to a bottom of the robot.
As shown in fig. 1, the robot kinematics modeling method specifically includes the following steps:
s101, establishing a robot body coordinate system on a robot chassis, and establishing a wheel coordinate system on wheels of the robot;
s102, setting the X axis of the robot body coordinate system to each wheel coordinate system XiIncluded angle of axis being gammaiThe included angle between the rolling shaft of each wheel and the wheel shaft is alpha, and the connecting line between the central point O and the central point of each wheel is LiX-axis to L in robot body coordinate systemiIs thetai；
S103, setting the rotation angular velocities of the three wheel shafts to
The central movement speed of the robot is [ v ]x,vy,w]TThe radius of the outer wheel is R, and the radius of the roller is R;
s104, the inverse kinematics formula can be expressed as:
wherein,
the angle between the rolling axis of each wheel and the axle
When Mecanum wheel and groundIntersecting roller and LiWhen vertical, the matrix a is always invertible, i.e. the positive kinematics always has a solution.
The robot path planning algorithm comprises global path planning and local path planning, wherein the global path planning carries out path planning according to a global grid map, current coordinates and target position coordinates, an optimal global path from a starting point to a terminal point is correctly obtained, and the local path planning is started after the optimal global path is determined.
The local path planning adopts a DWA algorithm, the DWA algorithm collects multiple groups of speed data in a speed space, simulates tracks of the robot at the speeds within a certain time, then performs scoring evaluation on the tracks according to a scoring function, selects an optimal track, and generates a robot speed control command.
As shown in fig. 2, the specific method of the local path planning is as follows:
a conventional DWA (Dynamic Window Approach) algorithm is generally applied to a two-wheeled differential mobile robot, and only forward velocity vx and rotation angular velocity w need to be considered, so that a velocity Window is a rectangle in a plane. However, the movement characteristic of the omnidirectional mobile robot is omnidirectional movement in a plane, and in order to ensure the flexibility of the movement, the speed of transverse movement needs to be planned at the same time, that is, the window pane is not a plane rectangle any more, but a space cuboid. Therefore, the traditional dynamic window lattice method no longer meets the flexibility requirement of the omnidirectional mobile robot, and needs to be improved.
S201, because the robot moves in all directions, the position relation between a starting point and an end point needs to be analyzed first, a space quadrant of a speed space is determined, and the current moving speed of the robot is set to be (v)cx,vcy,wc)：
S202, setting the robot to have kinematic speed limit, and setting an x-direction speed limit, a y-direction speed limit and a w-rotation speed limit as follows respectively: v. ofx_min、vx_max、vy_min、vy_max、wmin、wmax
Vs＝{vx∈[vxmin,vxmax],vy∈[vymin,vymax],w∈[wmin,wmax]}
S203, influenced by the performance and the motion stability of the motor, the robot has the maximum acceleration in the motion, so that a cubic area V exists in the forward simulation period (delta t) of the track of the robotdThe speed in the cubic region is the speed that the robot can actually reach:
s204, the robot must stop before colliding with the obstacle due to safety limitation, so that an anti-collision speed area V can be obtaineda：
Wherein docc(vx,vyW) is the velocity (v)x,vyAnd w) the closest distance to the obstacle on the simulated trajectory.
The search space V of the DWA algorithm improved by the steps S202, S203 and S204 can be obtainedr＝Vs∩Vd∩Va。
After obtaining the velocity space, it is necessary to perform the scoring for each velocity (v) in Vr based on a certain scoring criterion in order to optimize the local path (avoid the obstacle, fit the predetermined optimal global path as much as possible, and minimize the position error from the final point)x,vyW) scoring;
the scoring function is as follows:
score＝α*dpath+β*dgoal+γ*docc
wherein, alpha, beta and gamma are respectively a path fitting weight, a distance target point position weight and a distance weight with an obstacle; doccIs velocity (v)x,vyW) maximum obstacle distance value on trajectory, dgoalIs velocity (v)x,vyW) distance of the trajectory end point to the global target point, dpathIs the degree of conformance of the trajectory to the global path.
When the system finds a person, the pedestrian track prediction algorithm outputs a relative coordinate between the pedestrian and the laser radar module, and the relative coordinate value is located in the robot body coordinate system, and the specific method comprises the following steps:
let the coordinates of the current and the previous three pedestrians in the global coordinate system be pk＝(xk,yk)，pk-1＝(xk-1,yk-1)，pk-2＝(xk-2,yk-2)，pk-3＝(xk-3,yk-3) The time when the system acquires the coordinates is t4，t3，t2，t1. From these four sets of data, the average velocity of the robot over this period of time can be calculated:
let the predicted time be
Then
Position coordinates p at which a person may be after a timepre_k＝(xpre_k,ypre_k) Can be expressed as:
with ppre_kAs the center of a circle, drawing a circle with radius r, and within the circle, predicting time
The range S in which the person is likely to bek；
When a person is lost or retraced, the loop needs to be restarted and all variables initialized.
The pedestrian-avoiding path planning algorithm specifically comprises the following steps:
s301, according to a local path planning algorithm, the robot plans an optimal track of future delta t time, and sets a last point p of the trackiThe coordinate is (x)i,yi). If the pedestrian tracking result exists, the coordinate point p of the pedestrian is assumed to be predicted at the momentpreThe coordinate is (x)pre,ypre)。
S302, if
The robot path planning point is not in the radiation circle of the pedestrian prediction point, the robot is relatively safe, and the robot can plan to walk according to the optimal track;
s303, if
The robot path planning point is located inside or at the edge of the radiation circle, and the next moment may touch the pedestrian, and then the path planning for fast escaping from the radiation circle is performed.
As shown in fig. 3, when the robot is located in or at the edge of the radiation circle of the pedestrian prediction point, the moving linear speed of the robot can fast escape from the radiation circle along the tangential direction of the circle, and the specific method is as follows:
let the coordinate of the last point of the predicted point of the local path of the robot be pi＝(xi,yi) Pedestrian prediction point ppreThe coordinate is (x)pre,ypre) At this time, the distance between two points
Let the maximum linear velocity of robot motion be vmaxMinimum linear velocity of vminAnd the distance between the two points and the linear velocity have a linear relation:
therefore, the escape linear velocity of the robot at this time should be:
the speed direction is divided into two parts which are respectively v1And v2According to a rotation matrix
Unit vectors for two velocity directions can be obtained:
based on the formulas A and B, two tangential speeds can be obtained
And
according to a local path planning algorithm, respectively
And
and taking positive and negative ranges of 30 degrees as the center, and taking the positive and negative ranges as the planning speed to plan the local path of the robot.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. The omnidirectional mobile robot path planning algorithm based on pedestrian track prediction is characterized by comprising the following steps:
s1, performing kinematic modeling by using a robot chassis and Mecanum wheels installed on the chassis;
s2, planning a path by the robot according to the current coordinate and the target position coordinate;
s3, the robot predicts the behavior track of the pedestrian in the visual field through the laser radar module, and calculates the possible range of the pedestrian within a certain time;
s4, planning a pedestrian avoiding path by the robot according to the possible range of the pedestrian;
in step S3, the pedestrian trajectory prediction is implemented by a behavior trajectory prediction algorithm, and when the system finds a person, the system outputs a relative coordinate between the pedestrian and the lidar module, and the relative coordinate is located in the robot body coordinate system, and the specific method is as follows:
let the coordinates of the current and the previous three pedestrians in the global coordinate system be pk＝(xk,yk)，pk-1＝(xk-1,yk-1)，pk-2＝(xk-2,yk-2)，pk-3＝(xk-3,yk-3) The time when the system acquires the coordinates is t4，t3，t2，t1(ii) a From these four sets of data, the average velocity of the robot over this period of time can be calculated:
let the predicted time be
Then
Position coordinates p at which a person may be after a timepre_k＝(xpre_k,ypre_k) Can be expressed as:
with ppre_kAs the center of a circle, drawing a circle with radius r, and within the circle, predicting time
The range S in which the person is likely to bek；
When a person is lost or tracked again, the circulation needs to be restarted, and all variables are initialized;
in step S4, the pedestrian avoidance path planning is implemented by an algorithm, which specifically includes:
s301, according to a local path planning algorithm, the robot plans an optimal track of future delta t time, and sets a last point p of the trackiThe coordinate is (x)i,yi) (ii) a If the pedestrian tracking result exists, the coordinate point p of the pedestrian is assumed to be predicted at the momentpreThe coordinate is (x)pre,ypre)；
S302, if
The robot path planning point is not in the radiation circle of the pedestrian prediction point, the robot is relatively safe, and the robot can plan to walk according to the optimal track;
s303, if
Machine for testingThe robot path planning point is positioned in or at the edge of the radiation ring, and the next moment is likely to touch the pedestrian, so that path planning for rapidly escaping from the radiation ring is performed;
when the robot is positioned in or at the edge of a radiation ring of a pedestrian prediction point, the moving linear speed of the robot escapes from the radiation ring fastest along the tangential direction of a circle, and the specific method is as follows:
let the coordinate of the last point of the predicted point of the local path of the robot be pi＝(xi,yi) Pedestrian prediction point ppreThe coordinate is (x)pre,ypre) At this time, the distance between two points
Let the maximum linear velocity of robot motion be vmaxMinimum linear velocity of vminAnd the distance between the two points and the linear velocity have a linear relation:
therefore, the escape linear velocity of the robot at this time should be:
the speed direction is divided into two parts which are respectively v1And v2According to a rotation matrix
Unit vectors for two speed directions are obtained:
according to the formula A and the formula B, the speeds in two tangential directions are obtained
And
according to a local path planning algorithm, respectively
And
and taking positive and negative ranges of 30 degrees as the center, and taking the positive and negative ranges as the planning speed to plan the local path of the robot.
2. The pedestrian trajectory prediction-based omnidirectional mobile robot path planning algorithm of claim 1, wherein: in step S1, the robot chassis is provided with three mecanum wheels, which are left-handed mecanum wheels.
3. The pedestrian trajectory prediction-based omnidirectional mobile robot path planning algorithm according to claim 1 or 2, wherein: in step S1, the robot kinematics modeling method specifically includes:
s101, establishing a robot body coordinate system on a robot chassis, and establishing a wheel coordinate system on wheels of the robot;
s102, setting the X axis of the robot body coordinate system to each wheel coordinate system XiIncluded angle of axis being gammaiThe included angle between the rolling shaft of each wheel and the wheel shaft is alpha, and the connecting line between the central point O and the central point of each wheel is LiX-axis to L in robot body coordinate systemiIs thetai；
S103, setting the rotation angular velocities of the three wheel shafts to
The central movement speed of the robot is [ v ]x,vy,w]ΤThe radius of the outer wheel is R, and the radius of the roller is R;
s104, the inverse kinematics formula can be expressed as:
wherein,
the angle between the rolling axis of each wheel and the axle
4. The pedestrian trajectory prediction-based omnidirectional mobile robot path planning algorithm of claim 2, wherein: in step S2, the robot path planning implements path planning according to a path planning algorithm, the robot path planning includes global path planning and local path planning, the global path planning performs path planning according to a global grid map, current coordinates, and target position coordinates, an optimal global path from a start point to an end point is correctly obtained, and after the optimal global path is determined, local path planning is started.
5. The pedestrian trajectory prediction-based omnidirectional mobile robot path planning algorithm of claim 4, wherein: the local path planning adopts a DWA algorithm, the DWA algorithm collects multiple groups of speed data in a speed space, simulates tracks of the robot at the speeds within a certain time, then carries out scoring evaluation on the tracks according to a scoring function, selects an optimal track and generates a robot speed control command.
6. The pedestrian trajectory prediction-based omnidirectional mobile robot path planning algorithm of claim 5, wherein: the specific method for local path planning is as follows:
s201, because the robot moves in all directions, the position relation between the starting point and the end point needs to be analyzed first, and the space image of the speed space is determinedLimit, set the current moving speed of the robot as (v)cx,vcy,wc)：
S202, setting the robot to have kinematic speed limit, and setting an x-direction speed limit, a y-direction speed limit and a w-rotation speed limit as follows respectively: v. ofx_min、vx_max、vy_min、vy_max、wmin、wmax
Vs＝{vx∈[vxmin,vxmax],vy∈[vymin,vymax],w∈[wmin,wmax]}
S203, influenced by the performance and the motion stability of the motor, the robot has the maximum acceleration in the motion, so that a cubic area V exists in the forward simulation period (delta t) of the track of the robotdThe speed in the cubic region is the speed that the robot can actually reach:
s204, the robot must stop before colliding with the obstacle due to safety limitation, so that an anti-collision speed area V can be obtaineda：
Wherein d isocc(vx,vyW) is the velocity (v)x,vyW) the closest distance to the obstacle on the simulated trajectory;
the search space V of the DWA algorithm improved by the steps S202, S203 and S204 can be obtainedr＝Vs∩Vd∩Va；
S205, after obtaining the velocity space, in order to optimize the local path, each velocity (v) in Vr needs to be scored according to a certain scoring standardx,vyW) scoring;
the scoring function is as follows:
score＝α*dpath+β*dgoal+γ*docc
wherein, alpha, beta and gamma are respectively a path fitting weight, a distance target point position weight and a distance weight with an obstacle; doccIs velocity (v)x,vyW) maximum obstacle distance value on trajectory, dgoalIs velocity (v)x,vyW) distance of the trajectory end point to the global target point, dpathIs the degree of conformance of the trajectory to the global path.
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| CN108681243B (en) * | 2018-05-03 | 2020-12-18 | 南京理工大学 | Robot trajectory tracking method |
| CN108961313B (en) * | 2018-06-29 | 2021-06-29 | 大连民族大学 | Overlooking pedestrian risk quantification method of two-dimensional world coordinate system |
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