CN112540602A - Robot local path optimization method and robot - Google Patents
Robot local path optimization method and robot Download PDFInfo
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- CN112540602A CN112540602A CN201911183097.3A CN201911183097A CN112540602A CN 112540602 A CN112540602 A CN 112540602A CN 201911183097 A CN201911183097 A CN 201911183097A CN 112540602 A CN112540602 A CN 112540602A
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Abstract
The application is suitable for the technical field of computers, and provides an optimization method of a local path of a robot and the robot, wherein the optimization method comprises the following steps: acquiring a local path to be optimized; determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path; and optimizing the local path according to the optimization scheme to obtain the optimized local path. In the mode, the robot configures the optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path to be optimized, and optimizes the local path according to the optimization scheme to obtain the optimized local path. Due to the fact that different schemes are configured according to the characteristics of each pose point, when the robot runs according to the optimized local path, the flexibility of speed control and obstacle avoidance is improved, and the robot can run outdoors at high speed.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to a robot local path optimization method and a robot.
Background
With the development of society, robots are applied to various fields. Therefore, path optimization for the robot is particularly important.
However, the traditional robot path optimization method has incomplete functions, cannot accurately plan an effective path for the robot to run, is inflexible in speed control and obstacle avoidance, and is not favorable for the robot to run at high speed outdoors.
Disclosure of Invention
In view of this, the embodiment of the present application provides a method for optimizing a local path of a robot and a robot, so as to solve the problems that the conventional robot path optimization method is not complete in function, cannot accurately plan an effective path for the robot to travel, is inflexible in speed control and obstacle avoidance, and is not beneficial to the high-speed outdoor operation of the robot.
A first aspect of an embodiment of the present application provides a method for optimizing a local path of a robot, including:
acquiring a local path to be optimized;
determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path;
and optimizing the local path according to the optimization scheme to obtain the optimized local path.
Further, in order to obtain a more effective optimized local path and further make the robot more stable and flexible in angle control when walking according to the optimized local path, optimizing the local path according to the optimization scheme, and obtaining the optimized local path may include: carrying out direction calibration on each pose point in the local path to obtain a state track curve;
adjusting pose points in the state track curve based on a preset speed value and a preset time interval to obtain an initial motion curve;
acquiring an angle value corresponding to each pose point in the initial motion curve;
when the fact that the angular speed deviation value between any two adjacent pose points in the initial motion curve exceeds a preset deviation range is detected, adding a new pose point between the two adjacent pose points to generate a rotary motion curve;
generating the optimized local path based on the rotational motion curve.
Further, in order to make the robot control the speed more flexibly, can carry out speed optimization to local route, this application still includes:
acquiring a current motion curve and acquiring a speed value corresponding to each pose point in the current motion curve; the current motion curve is a motion curve corresponding to a path which the robot does not walk at present;
if the difference value between the speed values corresponding to any two adjacent pose points in the current motion curve is detected to exceed a preset speed range, carrying out speed optimization on the pose points in the current motion curve to generate a speed optimization curve;
generating the optimized local path based on the velocity optimization curve.
Further, in order to make the robot more flexible in controlling the speed, if it is detected that the difference between the speed values corresponding to any two adjacent pose points in the current motion curve exceeds a preset speed range, performing speed optimization on the pose points in the current motion curve, and generating a speed optimization curve includes: adjusting the speed of each pose point in the current motion curve to obtain a translation optimization curve; performing acceleration optimization on the pose points in the translation optimization curve to obtain an acceleration optimization curve; and carrying out direction normalization processing on the pose points in the acceleration optimization curve to obtain the speed optimization curve.
Further, in order to ensure the safety of the robot and enable the robot to walk safely, the robot walking system further comprises:
when an obstacle is detected within a preset distance range, performing safety optimization on a pose point in a current motion curve to obtain a safety optimization curve;
generating the optimized local path based on the safety optimization curve.
The safety optimization curve comprises an obstacle avoidance optimization curve, and when an obstacle is detected within a preset distance range, the safety optimization is performed on the pose point in the current motion curve to obtain the safety optimization curve, wherein the safety optimization curve comprises the following steps:
and when the entity barrier is detected in the first preset distance range, carrying out obstacle avoidance optimization on the pose point in the current motion curve to obtain the obstacle avoidance optimization curve.
The safety optimization curve further comprises a curve anti-collision optimization curve, when the obstacle is detected within a preset distance range, the pose point in the current motion curve is subjected to safety optimization, and the obtained safety optimization curve further comprises:
and when the curve is detected within a second preset distance range, adjusting the position of the pose point in the current motion curve to obtain the curve anti-collision optimization curve.
Further, in order to ensure the safety of the robot and enable the robot to walk safely, the robot walking system further comprises: adjusting the speed of each pose point in the safety optimization curve to obtain a speed protection motion curve; generating the optimized local path based on the speed protection motion curve.
A second aspect of embodiments of the present invention provides a robot, including:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a local path to be optimized;
the determining unit is used for determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path;
and the optimization unit is used for optimizing the local path according to the optimization scheme to obtain the optimized local path.
Further, the optimization unit is specifically configured to:
carrying out direction calibration on each pose point in the local path to obtain a state track curve;
adjusting pose points in the state track curve based on a preset speed value and a preset time interval to obtain an initial motion curve;
acquiring an angle value corresponding to each pose point in the initial motion curve;
when the fact that the angular speed deviation value between any two adjacent pose points in the initial motion curve exceeds a preset deviation range is detected, adding a new pose point between the two adjacent pose points to generate a rotary motion curve;
generating the optimized local path based on the rotational motion curve.
Further, the robot further includes:
the speed value acquisition unit is used for acquiring a current motion curve and acquiring a speed value corresponding to each pose point in the current motion curve; the current motion curve is a motion curve corresponding to a path which the robot does not walk at present;
the detection unit is used for carrying out speed optimization on the pose points in the current motion curve to generate a speed optimization curve if the difference value between the speed values corresponding to any two adjacent pose points in the current motion curve exceeds a preset speed range;
a first generating unit, configured to generate the optimized local path based on the speed optimization curve.
Further, the detection unit is specifically configured to:
adjusting the speed of each pose point in the current motion curve to obtain a translation optimization curve;
performing acceleration optimization on the pose points in the translation optimization curve to obtain an acceleration optimization curve;
and carrying out direction normalization processing on the pose points in the acceleration optimization curve to obtain the speed optimization curve.
Further, the robot further includes:
the safety optimization unit is used for carrying out safety optimization on the pose point in the current motion curve when the barrier is detected within the preset distance range to obtain a safety optimization curve;
and the second generating unit is used for generating the optimized local path based on the safety optimization curve.
The safety optimization curve comprises an obstacle avoidance optimization curve.
Further, the safety optimization unit is specifically configured to:
and when the entity barrier is detected in the first preset distance range, carrying out obstacle avoidance optimization on the pose point in the current motion curve to obtain the obstacle avoidance optimization curve.
The safety optimization curve further comprises a curve anti-collision optimization curve.
Further, the safety optimization unit is specifically configured to:
and when the curve is detected within a second preset distance range, adjusting the position of the pose point in the current motion curve to obtain the curve anti-collision optimization curve.
Further, the robot further includes:
the adjusting unit is used for adjusting the speed of each pose point in the safety optimization curve to obtain a speed protection motion curve;
the second generating unit is specifically configured to: generating the optimized local path based on the speed protection motion curve.
A third aspect of an embodiment of the present invention provides another robot, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program that supports a terminal to execute the above method, where the computer program includes program instructions, and the processor is configured to call the program instructions to perform the following steps:
acquiring a local path to be optimized;
determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path;
and optimizing the local path according to the optimization scheme to obtain the optimized local path.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of:
acquiring a local path to be optimized;
determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path;
and optimizing the local path according to the optimization scheme to obtain the optimized local path.
The optimization method for the local path of the robot and the robot provided by the embodiment of the application have the following beneficial effects:
according to the embodiment of the application, the local path to be optimized is obtained; determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path; and optimizing the local path according to the optimization scheme to obtain the optimized local path. In the mode, the robot configures the optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path to be optimized, and optimizes the local path according to the optimization scheme to obtain the optimized local path. Due to the fact that different schemes are configured according to the characteristics of each pose point, when the robot runs according to the optimized local path, the flexibility of speed control and obstacle avoidance is improved, and the robot can run outdoors at high speed. For example, initial optimization, time optimization, angle optimization, speed optimization, obstacle avoidance optimization, speed protection optimization and the like are performed on pose points in a local path to be optimized, so that the flexibility of the robot in the aspects of speed control, turning control, obstacle avoidance and the like in the running process is further improved, and the safety of the robot in outdoor high-speed running is guaranteed.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of an implementation of a method for optimizing a local path of a robot according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a state trajectory provided herein;
FIG. 3 is a schematic view of the initial motion profile provided herein;
FIG. 4 is a schematic view of the rotational motion profile provided herein;
FIG. 5 is a graphical illustration of a translational optimization curve provided herein;
FIG. 6 is a schematic diagram of a speed optimization curve provided herein;
fig. 7 is a schematic diagram of an obstacle avoidance optimization curve provided in the present application;
FIG. 8 is a schematic view of a curve crash optimization curve provided herein;
FIG. 9 is a schematic diagram of the speed protection motion profile provided by the present application;
FIG. 10 is a schematic view of a robot provided in an embodiment of the present application;
fig. 11 is a schematic diagram of a robot according to another embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, fig. 1 is a schematic flowchart of a method for optimizing a local path of a robot according to an embodiment of the present invention. In this embodiment, the main execution body of the method for optimizing the local path of the robot is a robot, and the robot includes, but is not limited to, a service robot, an indoor robot, a sweeping robot, an outdoor robot, and the like. The optimization method of the local path of the robot shown in fig. 1 comprises the following steps:
s101: and acquiring a local path to be optimized.
The robot acquires a local path to be optimized. Specifically, the robot acquires a global planned path, and intercepts a section of path from the global planned path as a local path to be optimized based on a preset method. For example, a path with a preset length is intercepted from the global planned path according to a preset sequence to be used as a local path to be optimized. The local path to be optimized can also be sent to the machine by other terminals, and the robot receives the local path to be optimized sent by other equipment.
S102: and determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path.
And the robot determines an optimization scheme corresponding to the local path to be optimized based on the characteristics of each pose point in the local path to be optimized. Specifically, the local path to be optimized includes a plurality of pose points, and it can be understood that the local path to be optimized is composed of a plurality of pose points. The robot analyzes the characteristics of each pose point in the local path to be optimized, such as the position characteristics, the speed characteristics, the angle characteristics, the time characteristics and the like of each pose point. For example, the robot may analyze the position characteristic of each pose point in the local path to be optimized, such as analyzing the position of each pose point in the local path, the motion direction of each pose point, the distance between each pose point and the obstacle, and the like; the speed characteristic of each pose point in the local path to be optimized analyzed by the robot can be that the speed value corresponding to each pose point is analyzed, and the difference value before the speed value corresponding to each two adjacent pose points is analyzed; the robot analyzes the angular characteristics of each pose point in the local path to be optimized, wherein the angular speed value corresponding to each pose point, the angular speed deviation value between every two adjacent pose points and the like are analyzed; the robot may analyze the time characteristic of each pose point in the local path to be optimized by analyzing a time difference between every two adjacent pose points, and the like. And determining an optimization scheme corresponding to the local path to be optimized according to the characteristics of each pose point obtained by analysis, wherein the optimization scheme can comprise initial optimization, time optimization, angle optimization, speed optimization, obstacle avoidance optimization, speed limit protection optimization, curve collision avoidance optimization and the like of the local path. For example, time optimization can be configured for the local path according to the time characteristics of each pose point; according to the position characteristics of each pose point, configuring obstacle avoidance optimization, curve collision avoidance optimization and the like for the local path; according to the speed characteristics of each pose point, configuring speed optimization, speed limit protection optimization and the like for the local path; and according to the angle characteristics of each pose point, configuring angle optimization and the like for the local path.
It is worth to be noted that different optimization schemes can be configured for different local paths; one optimization scheme may include all the optimization manners described above, or may be any combination of one or more of them, and different optimization schemes may be configured according to different local paths, which is not limited herein.
S103: and optimizing the local path according to the optimization scheme to obtain the optimized local path.
And the robot optimizes the local path according to the optimization scheme corresponding to the local path to be optimized to obtain the optimized local path. For example, the optimization scheme corresponding to the local path to be optimized includes initial optimization, time optimization, and angle optimization; optimizing the local path to obtain an optimized local path, wherein each pose point in the local path is subjected to direction calibration to obtain a state trajectory curve; adjusting pose points in the state track curve based on a preset speed value and a preset time interval to obtain an initial motion curve; acquiring an angular velocity value corresponding to each pose point in the initial motion curve; when the angular speed deviation value between any two adjacent pose points in the initial motion curve is detected to exceed a preset deviation range, adding a new pose point between the two adjacent pose points to generate a rotary motion curve; an optimized local path is generated based on the rotational motion curve.
Further, in order to obtain a more effective optimized local path and further make the robot more stable and flexible in angle control when walking according to the optimized local path, S103 may include S1031 to S1035, which are specifically as follows:
s1031: and calibrating the direction of each pose point in the local path to obtain a state track curve.
And the robot carries out direction calibration on each pose point in the local path to be optimized to obtain a state track curve corresponding to the local path to be optimized. Specifically, the robot acquires the position of each pose point in the local path and acquires the orientation of each pose point in the local path, which can be understood as acquiring the motion direction of each pose point; and drawing a motion curve based on the position and the orientation corresponding to each pose point to obtain a state track curve. As shown in the state trajectory curve diagram of fig. 2, the positions and arrow orientations corresponding to the leftmost dots indicate the starting points and starting movement directions corresponding to the robot in the local path; the positions and the arrow directions corresponding to the rightmost circular points represent the corresponding end points and the end point directions of the robot in the local path; each arrow in the middle of the two dots corresponds to a pose point for representing each position and direction of motion that the robot is to pass through in the segment of the local path.
S1032: and adjusting the pose points in the state track curve based on a preset speed value and a preset time interval to obtain an initial motion curve.
The robot adjusts pose points in the state track curve based on a preset speed value and a preset time interval to obtain an initial motion curve; the speed value and the time interval can be set according to the actual situation. For example, a distance dist between two adjacent pose points is calculated according to a preset speed value Vmax and a preset time interval dt, that is, dist is Vmax × dt, pose points in the state trajectory curve are added or deleted according to the distance value, and a starting point and an end point in the state trajectory curve are ensured to be unchanged during adjustment. The initial motion curve shown in fig. 3 ensures that the starting point and the end point are unchanged, and the pose points in the state trajectory curve are subjected to addition and deletion adjustment according to the calculated distance values to obtain the initial motion curve. In the state trajectory curve, each pose point has a position coordinate and an orientation, and after the processing of S1032, a time factor is added to each pose point, so that the obtained initial motion curve has a speed, and the subsequent further optimization of the local path is facilitated.
S1033: and acquiring an angle value corresponding to each pose point in the initial motion curve.
And the robot acquires the angle value corresponding to each pose point in the initial motion curve. And the robot acquires the position coordinates corresponding to each pose point in the initial motion curve, and calculates the angle value of each pose point relative to the preset horizontal direction according to each position coordinate.
S1034: and when the angular speed deviation value between any two adjacent pose points in the initial motion curve is detected to exceed a preset deviation range, adding a new pose point between the two adjacent pose points to generate a rotary motion curve.
The robot detects whether the angular speed deviation value between any two adjacent pose points in the initial motion curve exceeds a preset deviation range. Specifically, the robot calculates an angular velocity deviation value between any two adjacent pose points in the initial motion curve according to the angular velocity value corresponding to each pose point, and compares whether the calculated angular velocity deviation value exceeds a preset deviation range. And when the angular speed deviation value between any two adjacent pose points in the initial motion curve is detected to exceed a preset deviation range, adding a new pose point between the two adjacent pose points to generate a rotary motion curve. Specifically, the robot may be implemented by the following program:
angle_diff＝Theta1-Theta2
omega＝angle_diff/dt
as shown in the rotation motion curve of fig. 4, if the angular velocity deviation value between the leftmost dot and the first pose point on the right side of the dot exceeds the preset deviation range, a plurality of new pose points are added between the two pose points. Through the processing of S1034, the rotation speed of the position with a large angular deviation is significantly slowed down, and the whole movement process becomes smoother during rotation.
S1035: generating the optimized local path based on the rotational motion curve.
And the robot generates an optimized local path according to the rotating motion curve. Specifically, the robot can draw the same motion track as the rotation motion curve as the optimized local path according to the rotation motion curve; the rotational motion profile may also be converted to a path and labeled as an optimized local path.
Further, in order to make the robot control the speed more flexible, the speed of the local path may be optimized, and S1036 to S1038 may be further included after any step of S1032 to S1034, specifically as follows:
s1036: acquiring a current motion curve and acquiring a speed value corresponding to each pose point in the current motion curve; the current motion curve is a motion curve corresponding to a path where the robot does not walk currently.
The robot acquires a current motion curve and acquires a speed value corresponding to each pose point in the current motion curve; and the current motion curve is a motion curve corresponding to a path which the robot does not walk at present. Specifically, if S1036 to S1038 is executed after any step of S1032 or S1033, the current motion curve obtained at this time is the initial motion curve, and the robot obtains a speed value corresponding to each pose point in the initial motion curve, where the speed value is a speed value preset for each pose point by the robot. If S1036 to S1038 is executed after S1034, the current motion curve obtained at this time is a rotational motion curve, and the robot obtains a speed value corresponding to each pose point in the rotational motion curve.
S1037: and if the difference value between the speed values corresponding to any two adjacent pose points in the current motion curve is detected to exceed a preset speed range, carrying out speed optimization on the pose points in the current motion curve to generate a speed optimization curve.
The robot detects whether the difference value between the speed values corresponding to any two adjacent pose points in the current motion curve exceeds a preset speed range. Specifically, the robot calculates a difference between respective corresponding speed values of any two adjacent pose points in the current motion curve according to the speed value corresponding to each pose point, and compares whether the calculated difference exceeds a preset speed range. And if the difference value between the speed values corresponding to any two adjacent pose points in the current motion curve is detected to exceed the preset speed range, performing speed optimization on the pose points in the current motion curve to generate a speed optimization curve. If the speed of each pose point in the current motion curve is adjusted, a translation optimization curve is obtained; performing acceleration optimization on pose points in the translation optimization curve to obtain an acceleration optimization curve; and carrying out direction normalization processing on the pose points in the acceleration optimization curve to obtain the speed optimization curve.
Further, in order to make the robot more flexible in controlling the speed, S1037 may include S10371-S10373, which are as follows:
s10371: and adjusting the speed of each pose point in the current motion curve to obtain a translation optimization curve.
And if the robot detects that the difference value between the speed values corresponding to any two adjacent pose points in the current motion curve exceeds a preset speed range, adjusting the speed of each pose point in the current motion curve to obtain a translation optimization curve. For example, the following procedure can be followed:
vel＝dist/dt
vector＝cos(angle_diff)*dist
vel＝vel*vector
and carrying out speed limit regulation control on the speed of each pose point in the current motion curve, controlling the speed corresponding to each pose point in a reasonable speed range, and adjusting to obtain a translation optimization curve. As shown in the translational optimization curve of fig. 5, assuming that the robot moves from the leftmost dot position to the rightmost dot position, the robot moves according to the position, speed and moving direction corresponding to each pose point in the translational optimization curve, so that the robot can move forward while rotating, and the motions are consecutive; and because the speed control is reasonable, the safety and the movement efficiency of the robot in the movement process are ensured.
S10372: and performing acceleration optimization on the pose points in the translation optimization curve to obtain an acceleration optimization curve.
And the robot carries out acceleration optimization on the pose points in the translation optimization curve to obtain an acceleration optimization curve. Specifically, an acceleration value corresponding to each pose point can be calculated according to the speed corresponding to each two adjacent pose points, the acceleration of each pose point in the translation optimization curve is adjusted according to the calculated acceleration value, a new pose point is added into the translation optimization curve, and the smoothness of the speed is further improved. For example, the acceleration optimization of the pose points in the translation optimization curve can be realized by the following procedures:
acc_vel＝(vel_1-vel_2)*2/(dt_1+dt_2)
acc_rot＝(omega_1-omega_2)*2/(dt_1+dt_2)
as shown in the acceleration optimization curve of fig. 6, the number of the pose points in the acceleration optimization curve is significantly increased compared with the number of the pose points in the translation optimization curve of fig. 5, and the speed change is more gradual due to the addition of the acceleration limit. The current actual speed of each pose point and the speed of the local path terminal point can be added, feedback closed-loop adjustment is carried out on the translation optimization curve, and the reliability and the stability of speed control are further improved.
S10373: and carrying out direction normalization processing on the pose points in the acceleration optimization curve to obtain the speed optimization curve.
And the robot carries out direction normalization processing on the pose points in the acceleration optimization curve to obtain a speed optimization curve. Specifically, as shown in the graph comparison graph of fig. 6, the first row shows the graph in which the orientation of each pose point is not consistent, and the pose points are relatively discrete. The direction normalization processing can be carried out on the pose points in the acceleration optimization curve through the following procedures:
Beta＝atan2(delta_y,delta_x)
angle_diff＝Theta1–Theta2
angle_diff_1＝Theta1–Beta
angle_diff_2＝Theta2–Beta
SinSinAngleDiff＝sin(angle_diff)*sin(angle_diff)
SinSinAngle_1＝sin(angle_diff_1)*sin(angle_diff_1)
SinSinAngle_2＝sin(angle_diff_2)*sin(angle_diff_2)
Error＝dist*(SinSinAngle_1+SinSinAngle_2+SinSinAngleDiff*3)
after the direction normalization processing is carried out on the pose points in the acceleration optimization curve, the speed optimization curve shown in the second row of the graph 6 is obtained, the relevance between the pose points becomes strong, the smoothness is improved, and the robot motion is facilitated. For example, in the position with a sharp angle on the left side in the speed optimization curve shown in the second row of fig. 6, it can be seen that when the robot moves according to the trajectory, the robot turns right first to go back and then turns right to go forward, so that the flexibility after the local path optimization is embodied, and the robot can turn direction and walk more efficiently and quickly. As shown in the third row of fig. 6, the speed optimization curve further embodies the optimization of obstacle avoidance, that is, after the direction normalization processing is performed on the pose points in the acceleration optimization curve, even in the place with the obstacle, the motion trajectory is very smooth, the whole pose points are relatively concentrated, and the robot can pass through flexibly and safely.
S1038: generating the optimized local path based on the velocity optimization curve.
And the robot generates an optimized local path according to the speed optimization curve. Specifically, the robot can draw the same motion track as the speed optimization curve according to the speed optimization curve to serve as an optimized local path; the velocity optimization curve may also be converted to a path and labeled as an optimized local path.
Further, in order to ensure the safety of the robot and enable the robot to walk safely, S1039-S10310 may be further included after any step of S1032-S1034, specifically as follows:
s1039: and when the barrier is detected within the preset distance range, performing safety optimization on the pose point in the current motion curve to obtain a safety optimization curve.
And when the robot detects the obstacle within the preset distance range, performing safety optimization on the pose point in the current motion curve to obtain a safety optimization curve. If step S1039-step S10310 is executed after step S1032 or step S1033, the current motion curve obtained at this time is the initial motion curve, and the robot performs security optimization on the pose point in the initial motion curve to obtain a security optimization curve. And if S1039-S10310 is executed after S1034, the obtained current motion curve is the rotary motion curve, and the robot performs safety optimization on the pose points in the rotary motion curve to obtain a safety optimization curve. Further, S1039-S10310 may be executed after S1037, the current motion curve obtained at this time is the speed optimization curve, and the robot performs safety optimization on the pose points in the speed optimization curve to obtain a safety optimization curve. Specifically, when the entity obstacle is detected within a first preset distance range, obstacle avoidance optimization is performed on pose points in the current motion curve to obtain an obstacle avoidance optimization curve. And when the curve is detected within the second preset distance range, adjusting the position of the pose point in the current motion curve to obtain a curve anti-collision optimization curve.
Further, in order to improve safety of the robot walking, S1039 may include: and when the entity barrier is detected in the first preset distance range, carrying out obstacle avoidance optimization on the pose point in the current motion curve to obtain the obstacle avoidance optimization curve.
The safety optimization curve may include an obstacle avoidance optimization curve; the first preset distance range can be preset according to actual terrain, environment and the like, and is not limited; the physical barrier refers to a barrier which can prevent the robot from walking in a first preset distance range, such as a roadblock, a tree, a pedestrian, an object and the like. The robot detects whether an entity obstacle exists in a first preset distance range, and when the entity obstacle is detected in the first preset distance range, the robot carries out obstacle avoidance optimization on pose points in the current motion curve to obtain an obstacle avoidance optimization curve. Specifically, the robot can perform two-layer obstacle avoidance and two-section obstacle avoidance on pose points in the current motion curve. The two layers of obstacle avoidance comprise a first layer of obstacle avoidance and a second layer of obstacle avoidance; the robot carries out different expansion processing on the obstacles, for example, one obstacle is a round object with the radius of 0.5m, the radius of the round object is expanded to 1.0m, the first layer of obstacle avoidance adjusts the position of a pose point near the obstacle, the distance between the pose point near the obstacle and the center of the obstacle is larger than or equal to 1.0m, and the robot can avoid the obstacle after the radius expansion when walking according to the adjusted track; and if the width of the channel where the robot can walk is smaller than 1.0m and close to 0.5m, obstacle avoidance can be carried out on the second layer, namely the position of the pose point near the obstacle is adjusted, so that the distance from the pose point near the obstacle to the center of the obstacle is larger than or equal to 0.5m, and the robot can avoid the obstacle when walking according to the adjusted track. As shown in fig. 7, the obstacle avoidance optimization curve has a first layer obstacle avoidance on the left side and a second layer obstacle avoidance on the right side. Obstacle avoidance optimization can be performed on the pose points in the current motion curve through the following procedures:
obstacle_dist:pose(x,y)–obstacle(x,y)
Error_1＝obstacle_dist–min_obstacle_dist
Error_2＝obstacle_dist–inflation_dist
the two-stage obstacle avoidance can comprise optimized avoidance and contour speculation; the optimized avoidance is that if the obstacle is detected to completely block a walking channel, prompt information that the current path is not passable is generated, and the current running speed of the robot is reduced to 0; the contour estimation means that whether or not a position corresponding to a pose point near an obstacle is in the actual contour of the obstacle is detected, and when the position corresponding to the pose point near the obstacle is in the actual contour of the obstacle, the current travel speed of the robot is reduced to 0. The robot can make avoidance preparation in advance through obstacle avoidance optimization, effectively avoids obstacles, and ensures the safety of the robot.
Further, in order to improve the safety of the robot walking, the safety optimization curve may further include a curve collision avoidance optimization curve, and S1039 may further include: and when the curve is detected within a second preset distance range, adjusting the position of the pose point in the current motion curve to obtain the curve anti-collision optimization curve.
The safety optimization curve can also comprise a curve anti-collision optimization curve; the second preset distance range can be preset according to actual terrain, environment and the like, and is not limited; the curve refers to a road with a large turning angle encountered by the robot in the driving process, such as a curve collision avoidance optimization curve shown in fig. 8, and two marked corners in the curve are the curves. And the robot detects whether a curve exists in a second preset distance range, and when the curve is detected in the second preset distance range, the robot acquires pose points near the curve and adjusts the positions of the pose points to obtain a curve anti-collision optimization curve. The position of the pose point in the current motion curve can be adjusted by the following procedure:
delta_x＝global_pose->x()-local_pose->x()
delta_y＝global_pose->y()-local_pose->y()
dist_square＝delta_x*delta_x+delta_y*delta_y
Error＝fabs(dist_square)/(1+fabs(dist_square))
according to the curve anti-collision optimization curve shown in fig. 8, the position of the position and posture point in the current motion curve is adjusted, so that the curve originally marked close to the curve is optimized to be the curve marked far away from the curve, the robot can not collide with the curve even when walking at a high speed, and the walking safety of the robot is improved.
S10310: generating the optimized local path based on the safety optimization curve.
And the robot generates an optimized local path according to the safety optimization curve. The robot can draw the same motion track as the safety optimization curve according to the safety optimization curve to serve as an optimized local path; the security optimization curve may also be converted to a path and labeled as an optimized local path. Specifically, when the safety optimization curve comprises an obstacle avoidance optimization curve, the robot generates an optimized local path according to the obstacle avoidance optimization curve; and when the safety optimization curve comprises a curve anti-collision optimization curve, the robot generates an optimized local path according to the curve anti-collision optimization curve.
Further, in order to ensure the safety of the robot and enable the robot to walk safely, S10311-S10312 may be further included after S1039, specifically as follows:
s10311: and adjusting the speed of each pose point in the safety optimization curve to obtain a speed protection motion curve.
And the robot adjusts the speed of each pose point in the safety optimization curve to obtain a speed protection motion curve. Specifically, the robot detects whether an obstacle exists in a driving road, acquires pose points corresponding to the obstacle-free road according to a detection result, and adjusts the speed of the pose points to be within a first preset speed range; when the solid obstacle is detected, acquiring pose points near the solid obstacle, and adjusting the speed of the pose points to be within a second preset speed range; when a curve is detected, the position and attitude points near the curve are obtained, and the speed of the position and attitude points is adjusted to be within a third preset speed range. The speed of each pose point in the safety optimization curve can be adjusted through the following procedures:
curve_angle_diff＝Theta3–Theta1
limit_turn_vel＝fabs(curve_angle_diff)
limit_costmap_vel＝vel_costmap_cost
limit_costmap_rot＝rot_costmap_cost
limit_vel_vector＝limit_turn_vel<limit_costmap_vellimit_turn_vel:limit_costmap_vel
limit_rot_vector＝limit_costmap_rot
Error_1＝dist/dt*(1-limit_vel_vector)
Error_2＝omega/dt*(1-limit_rot_vector)
the speed protection motion curve shown in fig. 9 adjusts the speed of the robot differently according to different driving environments, so that the flexibility and safety of robot walking are improved.
S10312: generating the optimized local path based on the speed protection motion curve.
And the robot generates an optimized local path according to the speed protection motion curve. The robot can draw the same motion track as the speed protection motion curve according to the speed protection motion curve to serve as an optimized local path; the speed protection motion profile may also be converted to a path and labeled as an optimized local path.
According to the embodiment of the application, the local path to be optimized is obtained; determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path; and optimizing the local path according to the optimization scheme to obtain the optimized local path. In the mode, the robot configures the optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path to be optimized, and optimizes the local path according to the optimization scheme to obtain the optimized local path. Due to the fact that different schemes are configured according to the characteristics of each pose point, when the robot runs according to the optimized local path, the flexibility of speed control and obstacle avoidance is improved, and the robot can run outdoors at high speed. For example, initial optimization, time optimization, angle optimization, speed optimization, obstacle avoidance optimization, speed protection optimization and the like are performed on pose points in a local path to be optimized, so that the flexibility of the robot in the aspects of speed control, turning control, obstacle avoidance and the like in the running process is further improved, and the safety of the robot in outdoor high-speed running is guaranteed.
Referring to fig. 10, fig. 10 is a schematic view of a robot according to an embodiment of the present disclosure. The robot comprises units for performing the steps in the corresponding embodiment of fig. 1. Please refer to fig. 1 for the corresponding embodiments. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 10, it includes:
an obtaining unit 210, configured to obtain a local path to be optimized;
a determining unit 220, configured to determine, based on a feature of each pose point in the local path, an optimization scheme corresponding to the local path;
and an optimizing unit 230, configured to optimize the local path according to the optimization scheme, so as to obtain an optimized local path.
Further, the optimization unit 230 is specifically configured to:
carrying out direction calibration on each pose point in the local path to obtain a state track curve;
adjusting pose points in the state track curve based on a preset speed value and a preset time interval to obtain an initial motion curve;
acquiring an angle value corresponding to each pose point in the initial motion curve;
when the fact that the angular speed deviation value between any two adjacent pose points in the initial motion curve exceeds a preset deviation range is detected, adding a new pose point between the two adjacent pose points to generate a rotary motion curve;
generating the optimized local path based on the rotational motion curve.
Further, the robot further includes:
the speed value acquisition unit is used for acquiring a current motion curve and acquiring a speed value corresponding to each pose point in the current motion curve; the current motion curve is a motion curve corresponding to a path which the robot does not walk at present;
the detection unit is used for carrying out speed optimization on the pose points in the current motion curve to generate a speed optimization curve if the difference value between the speed values corresponding to any two adjacent pose points in the current motion curve exceeds a preset speed range;
a first generating unit, configured to generate the optimized local path based on the speed optimization curve.
Further, the detection unit is specifically configured to:
adjusting the speed of each pose point in the current motion curve to obtain a translation optimization curve;
performing acceleration optimization on the pose points in the translation optimization curve to obtain an acceleration optimization curve;
and carrying out direction normalization processing on the pose points in the acceleration optimization curve to obtain the speed optimization curve.
Further, the robot further includes:
the safety optimization unit is used for carrying out safety optimization on the pose point in the current motion curve when the barrier is detected within the preset distance range to obtain a safety optimization curve;
and the second generating unit is used for generating the optimized local path based on the safety optimization curve.
The safety optimization curve comprises an obstacle avoidance optimization curve.
Further, the safety optimization unit is specifically configured to:
and when the entity barrier is detected in the first preset distance range, carrying out obstacle avoidance optimization on the pose point in the current motion curve to obtain the obstacle avoidance optimization curve.
The safety optimization curve further comprises a curve anti-collision optimization curve.
Further, the safety optimization unit is specifically configured to:
and when the curve is detected within a second preset distance range, adjusting the position of the pose point in the current motion curve to obtain the curve anti-collision optimization curve.
Further, the robot further includes:
the adjusting unit is used for adjusting the speed of each pose point in the safety optimization curve to obtain a speed protection motion curve;
the second generating unit is specifically configured to: generating the optimized local path based on the speed protection motion curve.
Referring to fig. 11, fig. 11 is a schematic view of a robot according to another embodiment of the present disclosure. As shown in fig. 11, the robot 3 of this embodiment includes: a processor 30, a memory 31, and computer readable instructions 32 stored in the memory 31 and executable on the processor 30. The processor 30, when executing the computer readable instructions 32, implements the steps in the above-described embodiments of the method for optimizing the local path of each robot, such as S101 to S103 shown in fig. 1. Alternatively, the processor 30, when executing the computer readable instructions 32, implements the functions of the units in the above embodiments, such as the units 210 to 230 shown in fig. 10.
Illustratively, the computer readable instructions 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to accomplish the present application. The one or more units may be a series of computer readable instruction segments capable of performing specific functions, which are used to describe the execution of the computer readable instructions 32 in the terminal 3. For example, the computer readable instructions 32 may be an acquisition unit, a determination unit, and an optimization unit, each unit having the specific functions as described above.
The robot may include, but is not limited to, a processor 30, a memory 31. Those skilled in the art will appreciate that fig. 11 is merely an example of a robot 3 and does not constitute a limitation of the robot 3 and may include more or fewer components than shown, or some components in combination, or different components, e.g., the robot may also include input output devices, network access devices, buses, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the robot 3, such as a hard disk or a memory of the robot 3. The memory 31 may also be an external storage device of the robot 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the robot 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the robot 3. The memory 31 is used to store the computer readable instructions and other programs and data required by the robot. The memory 31 may also be used to temporarily store data that has been output or is to be output.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not cause the essential features of the corresponding technical solutions to depart from the spirit scope of the technical solutions of the embodiments of the present application, and are intended to be included within the scope of the present application.
Claims (10)
1. A method for optimizing a local path of a robot, comprising:
acquiring a local path to be optimized;
determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path;
and optimizing the local path according to the optimization scheme to obtain the optimized local path.
2. The optimization method of claim 1, wherein the optimizing the local path according to the optimization scheme to obtain an optimized local path comprises:
carrying out direction calibration on each pose point in the local path to obtain a state track curve;
adjusting pose points in the state track curve based on a preset speed value and a preset time interval to obtain an initial motion curve;
acquiring an angle value corresponding to each pose point in the initial motion curve;
when the fact that the angular speed deviation value between any two adjacent pose points in the initial motion curve exceeds a preset deviation range is detected, adding a new pose point between the two adjacent pose points to generate a rotary motion curve;
generating the optimized local path based on the rotational motion curve.
3. The optimization method according to claim 2, wherein after the adjusting the pose points in the state trajectory curve based on the preset speed value and the preset time interval to obtain the initial motion curve, the method further comprises:
acquiring a current motion curve and acquiring a speed value corresponding to each pose point in the current motion curve; the current motion curve is a motion curve corresponding to a path which the robot does not walk at present;
if the difference value between the speed values corresponding to any two adjacent pose points in the current motion curve is detected to exceed a preset speed range, carrying out speed optimization on the pose points in the current motion curve to generate a speed optimization curve;
generating the optimized local path based on the velocity optimization curve.
4. The optimization method according to claim 3, wherein if it is detected that the difference between the respective corresponding speeds of any two adjacent pose points in the current motion curve exceeds a preset speed range, performing speed optimization on the pose points in the current motion curve, and generating a speed optimization curve comprises:
adjusting the speed of each pose point in the current motion curve to obtain a translation optimization curve;
performing acceleration optimization on the pose points in the translation optimization curve to obtain an acceleration optimization curve;
and carrying out direction normalization processing on the pose points in the acceleration optimization curve to obtain the speed optimization curve.
5. The optimization method according to claim 4, wherein after the adjusting the pose points in the state trajectory curve based on the preset speed value and the preset time interval to obtain the initial motion curve, the method further comprises:
when an obstacle is detected within a preset distance range, performing safety optimization on a pose point in a current motion curve to obtain a safety optimization curve;
generating the optimized local path based on the safety optimization curve.
6. The optimization method of claim 5, wherein the safety optimization curve comprises an obstacle avoidance optimization curve, and the safety optimization of the pose point in the current motion curve when the obstacle is detected within the preset distance range to obtain the safety optimization curve comprises:
and when the entity barrier is detected in the first preset distance range, carrying out obstacle avoidance optimization on the pose point in the current motion curve to obtain the obstacle avoidance optimization curve.
7. The optimization method according to claim 5 or 6, wherein the safety optimization curve further comprises a curve collision avoidance optimization curve, and the safety optimization of the pose point in the current motion curve when the obstacle is detected within the preset distance range, to obtain the safety optimization curve further comprises:
and when the curve is detected within a second preset distance range, adjusting the position of the pose point in the current motion curve to obtain the curve anti-collision optimization curve.
8. The optimization method according to claim 5, wherein when the obstacle is detected within the preset distance range, the method further comprises, after the safety optimization of the pose point in the current motion curve is performed to obtain a safety optimization curve:
adjusting the speed of each pose point in the safety optimization curve to obtain a speed protection motion curve;
the generating the optimized local path based on the safety optimization curve comprises:
generating the optimized local path based on the speed protection motion curve.
9. A robot, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a local path to be optimized;
the determining unit is used for determining an optimization scheme corresponding to the local path based on the characteristics of each pose point in the local path;
and the optimization unit is used for optimizing the local path according to the optimization scheme to obtain the optimized local path.
10. A robot comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the processor when executing the computer readable instructions implements the method of any of claims 1 to 8.
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