CN111121807A - Mobile robot path planning method - Google Patents
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- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
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Abstract
The invention discloses a path planning method for a mobile robot, which is characterized in that an EKF-SLAM algorithm optimized based on a Levenberg-Marquardt method, a global path planning for the mobile robot based on a potential field ant colony algorithm, and an optimal path searching method integrating the global path planning and the local path planning for the mobile robot are provided, and autonomous navigation of the mobile robot in a room is completed through a robot simulation experiment. And the improved effectiveness is verified by combining with a simulation experiment, so that a position prediction result is more accurate, and a better path plan is obtained.
Description
Technical Field
The invention relates to the technical field of robot control, in particular to a path planning method for a mobile robot.
Background
In recent years, with the development of scientific technology and the improvement of the living standard of people, robots gradually move into the lives of people. As a basis for robots to provide high-quality services for humans, robot-oriented positioning is becoming a research hotspot in this field;
in recent decades, the robotics community has emerged a variety of solutions to the SLAM problem. The EKF-SLAM method based on the Extended Kalman Filter (EKF) is widely accepted and commonly applied, and the method firstly estimates the state at a certain moment in the motion process, then obtains feedback in a mode of measuring variables (containing noise), and finally corrects the estimated value according to the feedback. Thus, the EKF-SLAM can effectively estimate the past, current and even future states of the motion without knowing the detailed nature of the robot, but the stability and positioning accuracy of the method are still insufficient.
Disclosure of Invention
In view of this, the present invention provides a method for planning a path of a mobile robot with higher stability and positioning accuracy.
Based on the above purpose, the invention provides a mobile robot path planning method, which comprises the following steps:
constructing a prediction model of the mobile robot;
acquiring environmental data observed by the mobile robot, and performing landmark extraction from the environmental data to obtain landmark information;
judging whether the landmark information is the recorded landmark information or not, and recording the landmark information if not;
estimating the position of the mobile robot according to the control mileage of the mobile robot;
updating the position estimation of the robot according to repeatedly observed landmark information, and iterating the prediction model;
optimizing the iterative process of the prediction model by using a Levenberg-Marquardt method;
the environmental data of the current moment and the current position of the mobile robot are brought into a mobile robot motion model, and the position of the mobile robot at the next moment is predicted;
and calculating the optimal path between the predicted position of the mobile robot and the position of the set target point by using an ant colony algorithm.
Preferably, after acquiring the environmental data of the mobile robot, the method further includes:
removing Gaussian noise in the environmental data.
Preferably, the determining whether the landmark information is the registered landmark information includes:
predicting landmark information which can be observed by the current position of the mobile robot;
measuring the Mahalanobis distance between the predicted landmark information and the actually observed landmark information, and comparing the Mahalanobis distance with a set threshold;
and if the Mahalanobis distance does not exceed a set threshold, considering the two landmarks as the same landmark.
Preferably, the estimating the position of the mobile robot according to the current mileage of the mobile robot comprises:
calculating the covariance of the position of the mobile robot, the covariance of the landmark and the covariance between the mobile robot and the landmark, and constructing a covariance matrix;
jacobian matrix of prediction model of mobile robot
And updating the covariance matrix according to the control mileage of the mobile robot and the Jacobian matrix of the prediction model, and predicting the current position of the mobile robot.
Preferably, the updating the robot position estimation according to the repeatedly observed landmark information and the iterating the prediction model include:
calculating the predicted values of the landmark position and the angle according to the estimated value of the current position of the robot and the recorded landmark information;
calculating a Jacobian matrix of the measurement model;
calculating the Kalman gain of a Jacobian matrix;
calculating a new state vector by using Kalman gain to obtain the current position of the robot and the positions of all landmarks;
and adding the newly observed landmark information into the prediction model, and updating the prediction model.
Preferably, the calculating an optimal path between the predicted position of the mobile robot and the position of the set target point by using the ant colony algorithm includes:
adding an artificial potential field local search optimization algorithm related to global pheromones in the calculation process, and reducing the complexity and the depth of the ant colony algorithm;
establishing a smooth pheromone diffusion model and a pheromone diffusion grid table in a potential field ant colony algorithm, and improving the pheromone concentration of the implicit global optimum space;
taking pheromone of the current path point as a diffusion source, and introducing a diffusion factor;
calculating a complete path;
optimizing a local path through a geometric optimization strategy to obtain another complete path, and finishing pheromone updating of the adjacent path;
and comparing the complete paths to obtain an optimal path.
From the above, the mobile robot path planning method provided by the invention can improve the stability and positioning accuracy of the extended kalman algorithm compared with the conventional method by providing the EKF-SLAM algorithm optimized based on the Levenberg-Marquardt method, the mobile robot global path planning based on the potential field ant colony algorithm, and the optimal path search method combining the mobile robot global path planning and the local path planning, and completing the autonomous navigation of the indoor mobile robot through the robot simulation experiment. And the improved effectiveness is verified by combining with a simulation experiment, so that a position prediction result is more accurate, and a better path plan is obtained.
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Fig. 1 is a schematic flow chart of a path planning method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two entities with the same name but different names or different parameters, and it should be noted that "first" and "second" are merely for convenience of description and should not be construed as limitations of the embodiments of the present invention, and they are not described in any more detail in the following embodiments.
A mobile robot path planning method comprises the following steps:
constructing a prediction model of the mobile robot;
acquiring environmental data observed by the mobile robot, and performing landmark extraction from the environmental data to obtain landmark information;
the environmental data can be acquired through a speedometer, a laser radar, a camera and the like, and the landmark information should meet the following requirements:
can be observed repeatedly at different angles at different positions (the observed times are more than N times);
landmarks should be special enough so that it can be distinguished from different time steps whether they are the same landmark or not;
the number of landmarks in the environment should be large;
the landmark must be stationary.
Judging whether the landmark information is the recorded landmark information or not, and recording the landmark information if not;
estimating the position of the mobile robot according to the control mileage of the mobile robot;
the control mileage can be obtained from a control command inside the robot.
Updating the position estimation of the robot according to repeatedly observed landmark information, and iterating the prediction model;
optimizing the iterative process of the prediction model by using a Levenberg-Marquardt method;
and the stability and the positioning precision of the extended Kalman algorithm are improved. And the improved effectiveness is verified by combining with a simulation experiment.
The environmental data of the current moment and the current position of the mobile robot are brought into a mobile robot motion model, and the position of the mobile robot at the next moment is predicted;
and calculating the optimal path between the predicted position of the mobile robot and the position of the set target point by using an ant colony algorithm.
The method comprises the steps of providing an EKF-SLAM algorithm optimized based on a Levenberg-Marquardt method, a potential field ant colony algorithm-based mobile robot global path planning, and an optimal path searching method combining the mobile robot global path planning and local path planning, and completing autonomous navigation of the indoor mobile robot through a robot simulation experiment. And the improved effectiveness is verified by combining with a simulation experiment, so that a position prediction result is more accurate, and a better path plan is obtained.
As an embodiment, after acquiring the environmental data of the mobile robot, the method further includes:
gaussian noise in the environmental data is removed, and the reliability of the environmental data is improved.
As one embodiment, the determining whether the landmark information is the registered landmark information includes:
predicting landmark information which can be observed by the current position of the mobile robot;
measuring the Mahalanobis distance between the predicted landmark information and the actually observed landmark information, and comparing the Mahalanobis distance with a set threshold;
and if the Mahalanobis distance does not exceed a set threshold, considering the two landmarks as the same landmark.
Mahalanobis distance, also known as normalized squared innovation, is a weighted norm of the innovation vector.
As one embodiment, estimating the position of the mobile robot according to the current mileage of the mobile robot includes:
calculating the covariance of the position of the mobile robot, the covariance of the landmark and the covariance between the mobile robot and the landmark, and constructing a covariance matrix;
jacobian matrix of prediction model of mobile robot
And updating the covariance matrix according to the control mileage of the mobile robot and the Jacobian matrix of the prediction model, and predicting the current position of the mobile robot.
Covariance (Covariance) is used in probability theory and statistics to measure the overall error of two variables. The covariance of two variables provides a measure of how well the two variables are correlated. The covariance matrix can be used to represent the probability density of the multidimensional random variable, so that the study of the multidimensional random variable can be achieved through the covariance matrix.
As an embodiment, the robot position estimation is updated according to repeatedly observed landmark information, and the prediction model is iterated, including:
calculating the predicted values of the landmark position and the angle according to the estimated value of the current position of the robot and the recorded landmark information;
calculating a Jacobian matrix of the measurement model;
calculating the Kalman gain of a Jacobian matrix;
calculating a new state vector by using Kalman gain to obtain the current position of the robot and the positions of all landmarks;
and adding the newly observed landmark information into the prediction model, and updating the prediction model.
The degree to which the position and azimuth of the landmarks represented by the jacobian matrix vary with the robot position and azimuth. The first element of the first row is a distance change value when the landmark changes along with the x coordinate of the robot; the second element is a distance change value when the y coordinate of the robot is changed; the last term is the distance change value of the landmark relative to the angle of the robot. It can be seen that the distance information of the landmark with respect to the robot does not change of course when the robot is making a pure rotational movement. The second row represents the case where the landmark is at an angle that varies with respect to the robot coordinates.
As one embodiment, the calculating an optimal path between the predicted position of the mobile robot and the position of the set target point by using an ant colony algorithm includes:
adding an artificial potential field local search optimization algorithm related to global pheromones in the calculation process, and reducing the complexity and the depth of the ant colony algorithm;
establishing a smooth pheromone diffusion model and a pheromone diffusion grid table in a potential field ant colony algorithm, and improving the pheromone concentration of the implicit global optimum space;
taking pheromone of the current path point as a diffusion source, and introducing a diffusion factor;
calculating a complete path;
optimizing a local path through a geometric optimization strategy to obtain another complete path, and finishing pheromone updating of the adjacent path;
and comparing the complete paths to obtain an optimal path.
And adding an artificial potential field local search optimization algorithm related to the global pheromone, thereby reducing the complexity and depth of the ant colony algorithm. Establishing a smooth pheromone diffusion model and a pheromone diffusion grid table in a potential field ant colony algorithm, improving the pheromone concentration of the implicit global optimal space, and introducing a diffusion factor by taking the pheromone of the current path point as a diffusion source. Through comparison of different parameter combinations, optimal matching of parameters and simulation results of different methods, the advantages of searching for an optimal path, algorithm iteration depth and algorithm time depth based on the potential field ant colony algorithm are researched.
According to different characteristics of each local segment path of the complete path searched by the ant individuals in the ant colony algorithm, the other complete path is obtained by optimizing the local path through a geometric optimization strategy, and the pheromone of the adjacent path is updated, namely, the ant individuals are endowed with the capacity of searching the two complete paths simultaneously, the space of the ant colony individuals for searching solutions is reduced, the complexity and the depth of the ant colony algorithm are reduced, and the searching efficiency of the ant individuals is improved.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, 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.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A method for mobile robot path planning, the method comprising:
constructing a prediction model of the mobile robot;
acquiring environmental data observed by the mobile robot, and performing landmark extraction from the environmental data to obtain landmark information;
judging whether the landmark information is the recorded landmark information or not, and recording the landmark information if not;
estimating the position of the mobile robot according to the control mileage of the mobile robot;
updating the position estimation of the robot according to repeatedly observed landmark information, and iterating the prediction model;
optimizing the iterative process of the prediction model by using a Levenberg-Marquardt method;
the environmental data of the current moment and the current position of the mobile robot are brought into a mobile robot motion model, and the position of the mobile robot at the next moment is predicted;
and calculating the optimal path between the predicted position of the mobile robot and the position of the set target point by using an ant colony algorithm.
2. The method for planning a path of a mobile robot according to claim 1, wherein the obtaining environmental data observed by the mobile robot further comprises:
removing Gaussian noise in the environmental data.
3. The method for planning a route of a mobile robot according to claim 1, wherein the determining whether the landmark information is already recorded landmark information includes:
predicting landmark information which can be observed by the current position of the mobile robot;
measuring the Mahalanobis distance between the predicted landmark information and the actually observed landmark information, and comparing the Mahalanobis distance with a set threshold;
and if the Mahalanobis distance does not exceed a set threshold, considering the two landmarks as the same landmark.
4. The method of claim 1, wherein estimating the location of the mobile robot based on the current range of the mobile robot comprises:
calculating the covariance of the position of the mobile robot, the covariance of the landmark and the covariance between the mobile robot and the landmark, and constructing a covariance matrix;
jacobian matrix of prediction model of mobile robot
And updating the covariance matrix according to the control mileage of the mobile robot and the Jacobian matrix of the prediction model, and predicting the current position of the mobile robot.
5. The method of claim 1, wherein the updating the robot position estimate based on repeatedly observed landmark information and iterating the prediction model comprises:
calculating the predicted values of the landmark position and the angle according to the estimated value of the current position of the robot and the recorded landmark information;
calculating a Jacobian matrix of the measurement model;
calculating the Kalman gain of a Jacobian matrix;
calculating a new state vector by using Kalman gain to obtain the current position of the robot and the positions of all landmarks;
and adding the newly observed landmark information into the prediction model, and updating the prediction model.
6. The method for planning a path of a mobile robot according to claim 1, wherein the calculating an optimal path between the predicted position of the mobile robot and the position of the set target point by using an ant colony algorithm comprises:
adding an artificial potential field local search optimization algorithm related to global pheromones in the calculation process, and reducing the complexity and the depth of the ant colony algorithm;
establishing a smooth pheromone diffusion model and a pheromone diffusion grid table in a potential field ant colony algorithm, and improving the pheromone concentration of the implicit global optimum space;
taking pheromone of the current path point as a diffusion source, and introducing a diffusion factor;
calculating a complete path;
optimizing a local path through a geometric optimization strategy to obtain another complete path, and finishing pheromone updating of the adjacent path;
and comparing the complete paths to obtain an optimal path.
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| CN113721633A (en) * | 2021-09-09 | 2021-11-30 | 南京工业大学 | Mobile robot path planning method based on pedestrian trajectory prediction |
| CN113752267A (en) * | 2021-11-10 | 2021-12-07 | 山东捷瑞数字科技股份有限公司 | Mechanical arm path planning method and system |
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| CN115328146A (en) * | 2022-08-30 | 2022-11-11 | 安徽机电职业技术学院 | Self-adaptive path inspection method of inspection robot |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112518754A (en) * | 2020-12-07 | 2021-03-19 | 安徽机电职业技术学院 | Ant colony algorithm-based path self-editing robot with obstacle avoidance and cleaning structure |
| CN112518754B (en) * | 2020-12-07 | 2023-10-31 | 安徽机电职业技术学院 | Path self-braiding robot with obstacle avoidance cleaning structure based on ant colony algorithm |
| CN113721633A (en) * | 2021-09-09 | 2021-11-30 | 南京工业大学 | Mobile robot path planning method based on pedestrian trajectory prediction |
| CN113721633B (en) * | 2021-09-09 | 2023-10-17 | 南京工业大学 | Mobile robot path planning method based on pedestrian track prediction |
| CN113752267A (en) * | 2021-11-10 | 2021-12-07 | 山东捷瑞数字科技股份有限公司 | Mechanical arm path planning method and system |
| CN115167411A (en) * | 2022-07-01 | 2022-10-11 | 安徽机电职业技术学院 | Local path optimization method and system for mobile robot |
| CN115327914A (en) * | 2022-08-24 | 2022-11-11 | 安徽机电职业技术学院 | Robot motion planning method based on artificial gravitational field motion simulation |
| CN115328146A (en) * | 2022-08-30 | 2022-11-11 | 安徽机电职业技术学院 | Self-adaptive path inspection method of inspection robot |
| CN115328146B (en) * | 2022-08-30 | 2024-04-02 | 安徽机电职业技术学院 | Self-adaptive path inspection method of inspection robot |
| CN117670184A (en) * | 2024-01-31 | 2024-03-08 | 埃罗德智能科技(辽宁)有限公司 | Robot scene simulation method and system applied to digital robot industrial chain |
| CN117670184B (en) * | 2024-01-31 | 2024-05-03 | 埃罗德智能科技(辽宁)有限公司 | Robot scene simulation method and system applied to digital robot industrial chain |
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