CN116560382A - Mobile robot path planning method based on hybrid intelligent algorithm - Google Patents
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Abstract
The invention relates to the technical field of robot path planning, in particular to a mobile robot path planning method based on a hybrid intelligent algorithm.
Description
Technical Field
The invention relates to the technical field of robot path planning, in particular to a mobile robot path planning method based on a hybrid intelligent algorithm.
Background
With the development of automation technology, mobile robots have been widely used in various fields such as logistics, medical treatment, service, etc. Path planning is a very important problem in the application of mobile robots, often considering factors such as the motion constraints of the robot, the obstacle distribution of the environment, the length and safety of the path, etc. Therefore, it is often difficult to meet the actual demand using a certain path planning algorithm alone.
The a-Star algorithm is an efficient search algorithm that can find the shortest path. However, the planned path intersects the obstacle vertices with low safety, while the path has many inflection points and is not smooth enough. The DWA algorithm is a path planning algorithm considering robot dynamics and environmental constraints, and can find a feasible path on the premise of ensuring the safety of the robot. However, the search range of DWA algorithms is narrow, globally optimal solutions cannot be found, and the run time is long.
Disclosure of Invention
In view of the above, the present invention aims to provide a mobile robot path planning method based on a hybrid intelligent algorithm, so as to solve the problems of insufficient smoothness and insufficient efficiency of the path planning of the existing algorithm.
Based on the above purpose, the invention provides a mobile robot path planning method based on a hybrid intelligent algorithm, which comprises the following steps:
s1, adding a safety threshold function into an evaluation function of a standard A-Star algorithm, adding a redundant node rejection strategy and path smoothing treatment into the A-Star algorithm to obtain an improved A-Star algorithm, and using the improved A-Star algorithm as a global path planning algorithm to obtain a global optimal path;
s2, curvature evaluation sub-functions and target point distance functions are added in evaluation functions of a standard DWA algorithm, and speed evaluation function weight coefficients are adaptively adjusted according to the shortest distance between the standard DWA algorithm and all obstacles in a local environment, so that new weight coefficients are obtained, an improved DWA algorithm is obtained, and the improved DWA algorithm is used as a local path planning algorithm to obtain a local optimal path;
s3, selecting a local target point from a global optimal path generated by improving the A-Star algorithm, and providing guidance for the movement of the mobile robot;
s4, selecting an optimal simulation track to run;
s5, judging whether the mobile robot moves to the target point, and if not, returning to the operation steps S3 and S4.
Preferably, the safety threshold function is used to evaluate the safety threshold based on the safety threshold coefficient and the distance of the current node from the nearest obstacle, wherein the obstacle grid is obtained by equally dividing the map by a raster method, and the safety threshold coefficient defining an independent free grid without any intersection with the obstacle grid is zero.
Preferably, the redundant node culling strategy comprises:
s11, eliminating redundant nodes in the middle of each path, and reserving a starting point, an inflection point and a target point;
s12, sequentially connecting the nodes from the starting point to the rear, detecting intersection of the node connecting lines and grid barriers, if the connecting lines with the current node are intersected with the barriers, reserving the previous node of the current node, wherein the node is the starting point of the next backward connecting line, and deleting the intermediate node;
s13, repeatedly executing the step S12 until the path is connected with the target point and is not intersected with the obstacle, outputting the optimized path, and ending the strategy.
Preferably, the path smoothing process includes:
using a smoothing method based on a third-order B-spline curve, a smoothed path is obtained by fitting a continuous curve between given data points.
Preferably, the B-spline mathematical expression is:；
in the method, in the process of the invention,the ith control point of the B spline curve, n is the number of control points minus 1, t is a parameter in a defined domain,for the ith k-th orderB spline basis function, is for->Linear combinations of the order B spline basis functions are calculated from the recursive formula:
；
in the method, in the process of the invention,is the i 1 st order B-spline basis function, < >>To define nodes on parameter axes within a domain.
Preferably, the curvature evaluation sub-function is used to evaluate the curvature difference of the robot motion trajectory between two moments in time from the angular velocity and the linear velocity of the robot motion.
Preferably, the distance function is used to evaluate a velocity set comprising angular velocity and linear velocity based on the euclidean distance of the end store of the simulated track to the target point generated by the DWA algorithm.
Preferably, the new weight coefficient is obtainedThe method comprises the following steps:
；
in the method, in the process of the invention,corresponding values for the shortest time of passage through obstacle-dense areas for standard DWA algorithm in combination with kinematic model, < ->For the corresponding value which passes through the narrow passage and is safest, < > for>For a deceleration threshold distance +.>Proportional to linear acceleration +.>Inversely proportional->For the shortest distance between the robot and the obstacle in the local environment, < > or->Is an adaptively adjusted coefficient.
Preferably, step S3 further comprises:
aiming at the smooth curve output by the improved A-Star algorithm in the step S1, a curvature maximum point at the inflection point of the curve is selected as a local target point of the improved DWA algorithm, and guidance is provided for robot motion.
Preferably, step S5 further comprises:
and using an improved DWA algorithm as motion control of the mobile robot, generating a simulated motion track, selecting a linear speed and an angular speed corresponding to the optimal track as control instructions of the next period according to an evaluation function, and moving the robot to a final target point.
The invention has the beneficial effects that:
(1) The improved A-Star algorithm evaluation function provided by the invention is used for planning a path to keep a certain distance from an obstacle, so that the safety is improved, and meanwhile, the path is smoothly optimized.
(2) According to the invention, two evaluation sub-functions are added in the DWA algorithm, so that the movement of the robot is smoother, the risk of the robot away from a local target point is reduced, meanwhile, the running speed of the mobile robot can be adaptively adjusted according to local obstacle information, and the efficiency is greatly improved.
(3) The validity and superiority of the mobile robot path planning method based on the hybrid intelligent algorithm are verified by Matlab simulation.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a hybrid intelligent algorithm-based path planning method implemented by the invention;
FIG. 2 is a schematic diagram illustrating the detection of the intersection of a node connection with an obstacle in accordance with the present invention;
FIG. 3 is a schematic diagram of a redundant node elimination strategy according to the present invention;
FIG. 4 is a simulation diagram of the path planning of the standard A-Star algorithm and the modified A-Star algorithm of the present invention;
FIG. 5 is a kinematic model of a mobile robot used in the present invention;
FIG. 6 is a simulation diagram of a hybrid algorithm path plan implemented in accordance with the present invention;
FIG. 7 is a fusion simulation diagram before modification;
FIG. 8 is a diagram of simulated data for pre-improvement algorithm path planning;
fig. 9 is a diagram of simulated data for the improved algorithm path planning of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1, the embodiment of the present disclosure provides a mobile robot path planning algorithm based on a hybrid intelligent algorithm, including the following steps:
s1, adding a safety threshold function into an evaluation function of a standard A-Star algorithm, adding a redundant node rejection strategy and path smoothing treatment into the A-Star algorithm to obtain an improved A-Star algorithm, and using the improved A-Star algorithm as a global path planning algorithm to obtain a global optimal path;
the evaluation function of the standard a-Star algorithm is defined as:，
where n is the nth node in the path, f (n) is the total cost of the nth node, and is used to represent the priority of the nth node, where the smaller the f (n) is, the higher the priority of the nth node, g (n) is the actual cost of the nth node from the starting point, and h (n) is a heuristic function to represent the estimated cost of the nth node from the target point.
In order to prevent the planned path from passing through the top of the obstacle, keep a certain distance from the obstacle, and improve the safety, a safety threshold function is added into the evaluation function of the A, and the safety threshold function is defined as:
；
in the method, in the process of the invention,for the safety threshold coefficient (independent free grid without any intersection with obstacle grid, safety threshold coefficient +.>The method comprises the steps of carrying out a first treatment on the surface of the Other cases->) R is the distance of the current node n from the nearest obstacle.
The improved evaluation function is as follows:
。
and adding a redundant node eliminating strategy in the A-Star algorithm, deleting redundant nodes in the path, reducing the turning times of the path, and simplifying the path so as to achieve the aim of optimization.
The method for detecting the intersection of the node connecting line and the grid obstacle comprises the following steps: knowing the coordinate information of the node A, B, as shown in fig. 2, connecting the two points AB, calculating the grid coordinates covered by the connecting lines, traversing all the covered grids, sequentially detecting whether the broken grid is a free grid or an obstacle grid (judging basis: the logic value of the obstacle grid is 1 and the logic value of the free grid is 0), and if only one obstacle grid is detected, indicating that the connecting lines intersect with the obstacle, and the path is not feasible; if all of these grids are free grids, this means that the link does not intersect with the obstacle, and the path is viable.
Eliminating redundant node strategies: and the first step, eliminating redundant nodes in the middle of each path segment, and keeping a starting point, an inflection point and a target point. And secondly, connecting the nodes from the starting point to the rear in sequence, detecting the intersection of the node connecting lines and the grid obstacle, if the connecting lines with the current node are intersected with the obstacle, reserving the previous node of the current node, wherein the node is the starting point of the next connecting line from the rear, and deleting the intermediate node. And thirdly, repeatedly executing the second step until the path is connected with the target point and is not intersected with the obstacle, outputting an optimized path, and ending the strategy. As shown in fig. 3, the specific implementation steps are as follows:
(1) Initializing all nodes of the solid line in fig. 4；
(2) Will start the pointAnd target point->Joining a new path node set +.>；
(3) From the slaveStart to->Connecting line, then carrying out the above-mentioned intersection detection of node connecting line and obstacle, line segment +.>Crossing with the obstacle, traversing the nodes in turn until the detected line segment is +.>Crossing the grid obstacle, connecting the node +.>Join set->Reject redundant node->The method comprises the steps of carrying out a first treatment on the surface of the Redundancy node is removed in the same way>。
(4) After redundant nodes are removed, an optimized path node set is formedThe algorithm is ended, wherein the algorithm only comprises a starting point, a target point and a path turning point.
In order to make the robot follow the path better, unnecessary vibration and jitter in motion are reduced, and the path needs to be smoothed. A smoothing method based on a third-order B-spline curve is used by fitting a continuous curve between given data points. The B-spline mathematical expression is defined as:
；
in the method, in the process of the invention,the ith control point of the B spline curve, n is the number of control points minus 1, t is a parameter in a defined domain,is the ith k-th order B-spline basis function, is for +.>Linear combinations of the order B spline basis functions are calculated from the recursive formula:
；
in the method, in the process of the invention,is the i 1 st order B-spline basis function, < >>To define nodes on parameter axes within a domain.
Fig. 4a shows the path planned by the standard a-algorithm, fig. 4b shows the path planned by the modified a-algorithm implemented by the present invention, and the data are shown in the following table 1:
s2, curvature evaluation sub-functions and target point distance functions are added in evaluation functions of a standard DWA algorithm, and speed evaluation function weight coefficients are adaptively adjusted according to the shortest distance between the standard DWA algorithm and all obstacles in a local environment, so that new weight coefficients are obtained, an improved DWA algorithm is obtained, and the improved DWA algorithm is used as a local path planning algorithm to obtain a local optimal path;
the evaluation function of the standard DWA algorithm is defined as:
；
in the method, in the process of the invention,for the direction evaluation function, +.>The method is characterized in that the included angle between the motion direction of the robot after the forward simulation time and the connection line between the tail end point of the simulation track and the target point is ∈>For the obstacle distance evaluation function, represent the shortest distance from the simulated track to the obstacle, < >>For the speed evaluation function, the forward speed of the current robot towards the target point is evaluated>，/>Weight coefficient for corresponding function, +.>And carrying out normalization processing on each part of the evaluation function.
As shown in fig. 5, the kinematic model of the mobile robot considers only the horizontal motion of the robot,is the world coordinate system>Is a robot body coordinate system->Is the pose of the robot in the world coordinate system. Under the condition of low-speed running, the robot meets the constraint of the Ackerman steering geometry and can be equivalently replaced by a two-wheel bicycle model. Selecting the center of the rear axle of the robot +.>As reference point +.>For the steering angle of the front wheels, R is the turning radius, L is the wheelbase of the robot, ICR is the instantaneous steering center of the model, and reference point +.>Circular arc motion is performed around the ICR.
Since the displacement of the robot is small in an extremely short sampling time Δt, the robot can be regarded as uniform linear motion in time [ t, t+Δt ], and the displacement is approximately a short straight line. Modeling the mobile robot, wherein the mathematical expression of the motion model is as follows:
；
in the method, in the process of the invention,for the pose of the robot in world coordinate system at time t,/>For the pose of the robot t+Δt in the world coordinate system, +.>And->Robot linear speed +.>Components in horizontal and vertical direction, +.>Is the angular velocity of the robot at time t.
Two evaluation sub-functions are added in the standard DWA algorithm:
(1) The track curvature can reflect the stability of the motion of a robot, and in order to prevent the robot from shaking severely, the constraint of the track curvature needs to be added into a standard evaluation function. In a period of time, the curvature of the movement track of the robot is positively correlated with the angular velocity, and is negatively correlated with the linear velocity, and the smaller the curvature of the track, the smoother the movement of the robot is indicated. New addition ofFunction:
；
(2) Newly added target point distance functionThe risk of the robot deviating from the target point can be reduced. The function calculation robot performs the speed group ++based on the Euclidean distance from the end point of the simulated track generated by the DWA algorithm to the target point>Wherein the distance calculation formula is:
；
in the method, in the process of the invention,simulating coordinates of a trajectory end point for the robot, < >>Is the coordinates of the target point,is the path distance from the origin to the target point.
Weight coefficientThe robot is large in speed, but can be close to an obstacle in operation, so that the safety is low, and even the situation that the robot cannot move to a target point and turns around nearby can occur; weight coefficient->Small, the route security is high, but the running speed slows down, influences efficiency. According to the shortest distance to all obstacles in the local environment +.>Self-adaptive adjustment of the weight coefficient of the speed evaluation function, defining a new weight coefficient +.>The method comprises the following steps:
；
in the method, in the process of the invention,corresponding values for the shortest time of passage through obstacle-dense areas for standard DWA algorithm in combination with kinematic model, < ->Is the corresponding value that passes through the narrow channel and is the safest. />For a deceleration threshold distance +.>Proportional to linear acceleration +.>Inversely proportional->Is the shortest distance between the robot and the obstacle in the local environment,is an adaptively adjusted coefficient.
S3, selecting a local target point from a global optimal path generated by improving the A-Star algorithm, and providing guidance for the movement of the mobile robot;
the method comprises the following steps: aiming at the smooth curve output by the improved A-Star algorithm in the step S1, a curvature maximum point at the inflection point of the curve is selected as a local target point of the improved DWA algorithm, and guidance is provided for robot motion.
S4, selecting an optimal simulation track to run, and storing the optimal simulation track into a variable after taking all local target points at one time;
s5, judging whether the mobile robot moves to the target point, and if not, returning to the operation steps S3 and S4.
In step S4, S5, the DWA algorithm is improved and used for motion control of the mobile robot, a simulated motion track is generated, and the motion track is evaluated according to an evaluation functionSelecting the optimum track to correspond toAs a control instruction for the next cycle, the robot moves to the final target point and the algorithm ends.
Fig. 6 is a simulation diagram of a hybrid algorithm path planning implemented by the present invention, wherein a solid line is an actual motion track, a dotted line is a planned path, and fig. 7 is a fusion simulation diagram before improvement, and it can be seen that a global path planned by the improved a-Star algorithm has a large gap and is not smooth enough in motion.
Fig. 8 and fig. 9 are simulation data graphs of the algorithm before and after the improvement, respectively, it can be seen that the data graph before the improvement has larger speed jitter, the simulation data graph after the improvement has stable speed, no larger jitter occurs, the step number is reduced, and the efficiency is improved.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (8)
1. The mobile robot path planning method based on the hybrid intelligent algorithm is characterized by comprising the following steps of:
s1, adding a safety threshold function into an evaluation function of a standard A-Star algorithm, adding a redundant node rejection strategy and path smoothing treatment into the A-Star algorithm to obtain an improved A-Star algorithm, and using the improved A-Star algorithm as a global path planning algorithm to obtain a global optimal path;
s2, curvature evaluation sub-functions and target point distance functions are added in evaluation functions of a standard DWA algorithm, and speed evaluation function weight coefficients are adaptively adjusted according to the shortest distance between the standard DWA algorithm and all obstacles in a local environment, so that new weight coefficients are obtained, an improved DWA algorithm is obtained, and the improved DWA algorithm is used as a local path planning algorithm to obtain a local optimal path;
s3, selecting a local target point from a global optimal path generated by improving the A-Star algorithm, and providing guidance for the movement of the mobile robot;
s4, selecting an optimal simulation track to run;
s5, judging whether the mobile robot moves to a target point, if not, returning to the operation step S4;
the path smoothing process includes:
using a smoothing method based on a third-order B spline curve, and obtaining a smoothed path by fitting a continuous curve between given data points;
the mathematical expression of the B spline curve is as follows:
；
in the method, in the process of the invention,the ith control point of the B spline curve, n is the number of control points minus 1, t is a parameter in a defined domain,is the ith k-th order B-spline basis function, is for +.>Linear combinations of the order B spline basis functions are calculated from the recursive formula:
；
；
in the method, in the process of the invention,is the i 1 st order B-spline basis function, < >>To define nodes on parameter axes within a domain.
2. The mobile robot path planning method based on the hybrid intelligent algorithm according to claim 1, wherein the safety threshold function is used for evaluating the safety threshold according to the safety threshold coefficient and the distance between the current node and the nearest obstacle, wherein the obstacle grid is obtained by equally dividing the map by adopting a grid method, and the safety threshold coefficient of an independent free grid which does not have any intersection with the obstacle grid is defined to be zero.
3. The hybrid intelligent algorithm-based mobile robot path planning method of claim 1, wherein the redundant node culling strategy comprises:
s11, eliminating redundant nodes in the middle of each path, and reserving a starting point, an inflection point and a target point;
s12, sequentially connecting the nodes from the starting point to the rear, detecting intersection of the node connecting lines and grid barriers, if the connecting lines with the current node are intersected with the barriers, reserving the previous node of the current node, wherein the node is the starting point of the next backward connecting line, and deleting the intermediate node;
s13, repeatedly executing the step S12 until the path is connected with the target point and is not intersected with the obstacle, outputting the optimized path, and ending the strategy.
4. The mobile robot path planning method based on the hybrid intelligent algorithm according to claim 1, wherein the curvature evaluation sub-function is used for evaluating the curvature difference of the robot motion trail between two moments according to the angular speed and the linear speed of the robot motion.
5. The hybrid intelligent algorithm-based mobile robot path planning method of claim 1, wherein the target point distance function is used to evaluate a velocity group including angular velocity and linear velocity based on the euclidean distance of the end store of the simulated track to the target point generated by the DWA algorithm.
6. The mobile robot path planning method based on the hybrid intelligent algorithm according to claim 1, wherein the new weight coefficient is obtainedThe method comprises the following steps:
；
in the method, in the process of the invention,the standard DWA algorithm is combined with the corresponding values of the kinematic model that pass through the obstacle-dense region in the shortest time,for the corresponding value which passes through the narrow passage and is safest, < > for>For a deceleration threshold distance +.>Proportional to linear acceleration +.>Inversely proportional->For the shortest distance between the robot and the obstacle in the local environment, < > or->Is an adaptively adjusted coefficient.
7. The mobile robot path planning method based on hybrid intelligent algorithm of claim 1, wherein step S3 further comprises:
aiming at the smooth curve output by the improved A-Star algorithm in the step S1, a curvature maximum point at the inflection point of the curve is selected as a local target point of the improved DWA algorithm, and guidance is provided for robot motion.
8. The mobile robot path planning method based on hybrid intelligent algorithm of claim 1, wherein step S5 further comprises:
and using an improved DWA algorithm as motion control of the mobile robot, generating a simulated motion track, selecting a linear speed and an angular speed corresponding to the optimal track as control instructions of the next period according to an evaluation function, and moving the robot to a final target point.
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