Robot navigation control method for dynamically adjusting DWA parameters based on 2D laser radar

Abstract

The invention relates to a robot navigation control method for dynamically adjusting DWA parameters based on a 2D laser radar. The robot navigation control method comprises the following steps: acquiring environment information around a robot in real time by using the 2D laser radar; performing data processing on the environment information around the robot, and identifying to obtain a local environment scene around the robot; according to the identified local environment scene, dynamically adjusting input parameters of a DWA algorithm, and generating a local path plan of the robot in the current environment; and correspondingly controlling the motion of the robot based on the local path planning. Compared with the prior art, the navigation effect of the robot in a complex or dynamic change environment can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of robot navigation technology, and in particular to a robot navigation control method based on a 2D laser radar to dynamically adjust DWA parameters. Background Art

[0002] In robot navigation technology, LiDAR is an important sensing technology that uses laser beams to measure the distance to objects, thereby generating a two-dimensional or three-dimensional map of the environment. Based on these maps, robots can achieve autonomous navigation and obstacle avoidance; dynamic parameter adjustment technology adjusts the robot's motion parameters, such as speed and acceleration, in real time to adapt to different environmental changes, thereby achieving smoother and more efficient navigation.

[0003] In the existing technology, local path planning algorithms based on two-dimensional laser radar are usually used, such as the Dynamic Window Approach (DWA). The DWA algorithm sets a dynamic window around the robot, searches for feasible paths within this window, and selects the optimal path according to certain optimization goals (such as the shortest path, minimum energy consumption, etc.). At the same time, the DWA algorithm also takes into account the robot's kinematic limitations, such as maximum speed, acceleration, etc., to ensure that the generated path can be actually executed by the robot. However, the existing navigation algorithms based on DWA often cannot adjust their own motion parameters well when facing complex or dynamically changing environments, resulting in poor navigation effects. For example, in a narrow passage, if the robot still moves forward at a high speed, it may not be able to pass smoothly due to space limitations; and at a large angle turn, if the robot cannot slow down or adjust the direction appropriately, it may collide or deviate from the predetermined path. In addition, when dealing with different environmental scenarios, the existing DWA algorithm usually requires a series of fixed parameters to be set manually, which limits its adaptability and flexibility to a certain extent. Therefore, how to dynamically adjust the input parameters of the DWA algorithm according to different environments to achieve smoother and more efficient navigation is a major challenge facing current robot navigation. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a robot navigation control method based on 2D laser radar to dynamically adjust DWA parameters, which can improve the navigation effect of the robot in complex or dynamically changing environments.

[0005] The object of the present invention can be achieved by the following technical solution: A robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters comprises the following steps:

[0006] S1, use 2D laser radar to obtain the environmental information around the robot in real time;

[0007] S2. Process the environmental information around the robot to identify the local environment scene around the robot;

[0008] S3, dynamically adjust the input parameters of the DWA algorithm according to the identified local environment scene, and generate the local path planning of the robot in the current environment;

[0009] S4. Based on local path planning, control the movement of the robot accordingly.

[0010] Furthermore, the step S2 specifically processes the environmental information around the robot by using a data fitting method.

[0011] Furthermore, the local environment scene identified in step S2 includes an open area, a narrow road entrance, a short narrow road, a long narrow road, a small angle turn, a large angle turn or a U-turn.

[0012] Furthermore, the dynamic adjustment of the input parameters of the DWA algorithm in step S3 specifically adjusts the maximum speed, the minimum speed, the maximum acceleration, the minimum acceleration, the deceleration, the number of sampling points or the sampling time interval.

[0013] Furthermore, the generation of the local path planning of the robot in the current environment in step S3 specifically includes the following process:

[0014] S31, setting a dynamic window based on the current position, current speed and acceleration limit of the robot;

[0015] S32, setting the control input as the linear velocity and angular velocity of the robot, for each control input, using a dynamic window to limit the velocity space of the robot, simulating the trajectory of the robot through a forward motion model, and outputting multiple trajectories;

[0016] S33, performing cost evaluation on multiple trajectories, and screening out the trajectory with the minimum cost as the planned local path.

[0017] Furthermore, the dynamic window includes a dynamic window of linear velocity and a dynamic window of angular velocity. The dynamic window range of the linear velocity is: [vx-amax*Δt, vx+amax*Δt], and the dynamic window range of the angular velocity is: [ω-dwmax*Δt, ω+dwmax*Δt], wherein vx and ω are the current linear velocity and current angular velocity of the robot respectively, amax and dwmax are the maximum linear acceleration and maximum angular acceleration respectively, and Δt is the time step, that is, the sampling interval.

[0018] Furthermore, the forward motion model in step S32 is specifically a relationship model between the robot speed, angular velocity and position.

[0019] Furthermore, the cost evaluation process in step S33 includes:

[0020] The target proximity, safety and smoothness scores corresponding to the trajectory are calculated respectively, and the evaluation score of the trajectory is obtained by weighted summation.

[0021] Furthermore, the target proximity is specifically the distance between the track and the target. The closer the track is to the target, the higher the target proximity score is.

[0022] The safety score is specifically the distance between the track and the obstacle. The farther the track is from the obstacle, the higher the safety score.

[0023] The smoothness is specifically the angular velocity change rate of the trajectory. The smaller the angular velocity change rate is, the higher the smoothness score is.

[0024] Furthermore, the step S33 specifically selects the trajectory with the highest evaluation score value as the trajectory with the minimum cost.

[0025] Compared with the prior art, the present invention has the following advantages:

[0026] The present invention uses 2D laser radar to obtain the environmental information around the robot in real time; obtains the local environmental scene around the robot through data processing and identification; and dynamically adjusts the input parameters of the DWA algorithm according to the identified local environmental scene, and generates the local path planning of the robot in the current environment to control the movement of the robot accordingly. In this way, the input parameters of the DWA algorithm can be dynamically adjusted according to different real-time environments, thereby improving the navigation effect of the robot in complex or dynamically changing environments.

[0027] The present invention dynamically adjusts the input parameters of the DWA algorithm, such as maximum and minimum speeds, maximum and minimum accelerations, decelerations, number of sampling points, sampling interval duration, etc., according to the identified local environmental scenes (such as open areas, narrow road entrances, short narrow roads, long narrow roads, small-angle turns, large-angle turns, U-turns, etc.). This enables the robot to automatically slow down or adjust its direction in narrow passages to avoid collisions or inability to pass smoothly; it enables the robot to appropriately slow down or adjust its direction at large-angle turns to ensure that the turn can be completed smoothly without collisions or deviations from the predetermined path.

[0028] The present invention utilizes the DWA algorithm to generate multiple trajectories of the robot in the current environment according to the adjusted input parameters, and performs cost evaluation on the generated path trajectories to screen out the optimal trajectory with the minimum cost. Its linear velocity and angular velocity are relatively smooth, which can effectively improve the robot's movement stability and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0030] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Example

[0032] like Figure 1 As shown, a robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters includes the following steps:

[0033] S1, use 2D laser radar to obtain the environmental information around the robot in real time;

[0034] S2. Process the environmental information around the robot to identify the local environment scene around the robot;

[0035] S3, dynamically adjust the input parameters of the DWA algorithm according to the identified local environment scene, and generate the local path planning of the robot in the current environment;

[0036] S4. Based on local path planning, control the movement of the robot accordingly.

[0037] The specific application process of the above solution includes:

[0038] 1. Environmental perception and information processing: Use 2D lidar to obtain environmental information around the robot in real time, and dynamically identify local environmental scenes around the robot through data processing algorithms, such as open areas, narrow road entrances, short narrow roads, long narrow roads, small-angle turns, large-angle turns, U-turns, etc.

[0039] Usually, environmental information refers to the surrounding environment information of the current robot obtained by various sensors (lidar, depth camera, ultrasonic radar, obstacle avoidance radar, etc.). Different data processing algorithms are used for different sensor data. For example, the 2D laser radar (model RPLIDAR A2, maximum measurement angle of 360 degrees, measurement accuracy of 0.2 degrees, and measurement distance of 20 meters) selected in this solution has a series of point cloud data as output, so a straight line or curve fitting algorithm, such as polynomial fitting, spline fitting or RANSAC, Hough transform, etc., is used to calculate the curvature or angle.

[0040] In actual use, in order to reduce the amount of calculation, the surrounding environment through which the global reference path passes can also be used as a constraint to perform corresponding local scene recognition.

[0041] 2. Dynamically adjust DWA parameters: According to the identified local environment scene, dynamically adjust the input parameters of the DWA algorithm, such as maximum and minimum speeds, maximum and minimum accelerations, decelerations, number of sampling points, sampling intervals, etc. For example, in a narrow passage, reduce the maximum speed and acceleration, increase the minimum speed and deceleration, so that the robot can automatically slow down or adjust the direction to avoid collisions or problems that prevent it from passing smoothly. In this embodiment, in a narrow passage, the maximum speed is set to 0.5m / s, the minimum speed is set to 0.2m / s, and the maximum acceleration is set to 0.2m / s 2 , the minimum acceleration is set to -0.2m / s 2 , the number of sampling points is set to 50, and the sampling interval is set to 0.1 second.

[0042] 3. Path planning and optimization: Using the DWA algorithm, the robot generates a local path plan in the current environment based on the adjusted input parameters. At the same time, the generated path trajectory is cost-evaluated and optimized to make its linear velocity and angular velocity smoother, so as to improve the robot's motion stability and comfort.

[0043] Dynamic Window Approach (DWA) is a local planning algorithm commonly used for path planning and obstacle avoidance of mobile robots, especially for real-time applications in obstacle avoidance and navigation tasks. The DWA algorithm selects the most appropriate control command by considering the robot's dynamic limitations and environmental obstacles. DWA is a trajectory generation method based on local states. It calculates reasonable control commands (such as linear velocity and angular velocity) by considering the robot's current position, velocity, target position, and surrounding obstacles. The core idea of ​​the algorithm is to generate multiple possible trajectories by searching a "dynamic window" and select the most appropriate trajectory to control the robot.

[0044] The process of generating local path planning of the robot in this scheme includes:

[0045] Define Dynamic Window - Based on the robot's current position, current velocity, and acceleration limits, determine a dynamic window within which possible control inputs (linear and angular velocities) are generated.

[0046] Trajectory Generation - For each control input, the robot's trajectory is simulated using a forward kinematic model.

[0047] Trajectory evaluation - Selecting the best control command by evaluating the cost of each trajectory (usually the proximity to the goal, the distance to obstacles, the smoothness of the trajectory, etc.).

[0048] Update control input - select the control command corresponding to the trajectory with the minimum cost as the control input at the current moment.

[0049] The input and output of DWA are:

[0050] enter:

[0051] 1. Robot state: The current position and speed of the robot (e.g. (x, y) position and (vx, vy) speed). This is usually obtained through sensors and odometers.

[0052] 2. Target position: The coordinates of the target point that the robot expects to reach.

[0053] 3. Dynamic window parameters:

[0054] Maximum linear velocity vmax and maximum angular velocity wmax: the maximum speed and angular velocity that the robot can reach.

[0055] Linear acceleration amax and angular acceleration wmax: the maximum acceleration and angular acceleration that the robot can achieve.

[0056] 4. Obstacle information: Usually through point cloud data or distance information provided by LiDAR, depth cameras or other sensors, the location information of obstacles is crucial to assessing the safety of the trajectory.

[0057] 4. Robot dynamics model: The robot's motion model usually includes the relationship between speed, angular velocity and position.

[0058] Output:

[0059] Control commands: The control inputs selected by DWA are usually the robot's linear velocity v and angular velocity w. These control commands will be sent to the robot's drive system to control the robot to move along the planned trajectory.

[0060] The dynamic window is the range of speeds that can be reached within a certain period of time, calculated by the robot's current speed and acceleration limits. This window limits the speed space that the robot can choose.

[0061] The range of a dynamic window is usually:

[0062] Dynamic window of linear velocity: [vx-amax*Δt,vx+amax*Δt];

[0063] Dynamic window of angular velocity: [ω-dwmax*Δt,ω+dwmax*Δt];

[0064] Among them, vx and ω are the current linear velocity and current angular velocity of the robot respectively, amax and dwmax are the maximum linear acceleration and maximum angular acceleration respectively, and Δt is the time step, that is, the sampling interval.

[0065] Finally, trajectory generation and evaluation

[0066] Based on the control inputs (i.e., the combination of linear velocity and angular velocity), DWA generates multiple trajectories by simulating the robot motion model. Each trajectory is evaluated and scored based on the following criteria:

[0067] Target proximity: The distance of the trajectory from the target. The closer to the target, the higher the score.

[0068] Safety: Whether the trajectory avoids obstacles. The farther away from obstacles, the higher the score.

[0069] Smoothness: The smoothness of a trajectory is usually evaluated by the rate of change of the trajectory (such as the rate of change of angular velocity).

[0070] The score of each trajectory is the weighted sum of these evaluation criteria. Finally, DWA selects the control command corresponding to the trajectory with the highest score.

[0071] Therefore, according to the different recognized scenes, the target position, maximum speed, maximum acceleration and Δt can be adjusted dynamically. This can achieve the control of the robot in different scenes. For example, in an open scene, the linear acceleration can be increased to reach the rated speed faster, while the angular velocity can be reduced to reduce angle adjustment and prevent angle oscillation and overshoot.

[0072] 4. Motion control and execution: According to the generated local path planning and control instructions, the robot's movement is controlled so that it can automatically navigate through different local environment scenes and achieve smooth movement. In this embodiment, the robot's movement speed ranges from 0.2m / s to 0.5m / s, and the acceleration range is -0.2m / s 2 Up to 0.2m / s 2 .

[0073] 5. Feedback and adjustment: During the movement of the robot, its motion state and environmental changes are monitored in real time, and dynamic adjustments are made as needed to further improve the robot's navigation effect in complex or dynamically changing environments.

[0074] Through the above technical means, the technical solution can dynamically adjust the input parameters of the DWA algorithm according to different environments, improve the navigation effect of the robot in complex or dynamically changing environments, and at the same time make the robot's motion trajectory, linear velocity and angular velocity smoother, thereby improving the robot's motion stability and comfort.

[0075] Compared with the existing technology, this solution has the following advantages:

[0076] 1. Improve navigation adaptability: This solution dynamically matches environmental information in real time to distinguish different scenarios, such as open areas, narrow road entrances, short narrow roads, long narrow roads, small angle turns, large angle turns, U-turns, etc., and dynamically adjusts DWA input parameters according to these scenarios, such as maximum and minimum speeds, maximum and minimum accelerations, decelerations, number of sampling points, sampling intervals, etc. This dynamic adjustment method enables the robot to better adapt to different environmental changes, thereby improving navigation effects.

[0077] 2. Improve motion smoothness: The path trajectory generated by this solution has relatively smooth linear velocity and angular velocity, achieving smooth robot movement. This not only improves the robot's comfort, but also reduces energy consumption and extends the battery life.

[0078] 3. Reduce computing costs: This solution uses 2D laser radar and does not require expensive computing platforms. Compared with some navigation algorithms that require 3D laser radar or high-performance computing platforms, this solution is low-cost and easy to promote and apply.

[0079] 4. Improve flexibility: This solution does not require manual setting of a series of fixed parameters, but dynamically adjusts the input parameters of the DWA algorithm according to different environments, which makes this solution more flexible and able to adapt to more environmental changes.

[0080] 5. Realize autonomous navigation: This solution uses lidar technology to generate two-dimensional maps of the environment, and realizes autonomous navigation and obstacle avoidance based on these maps. No human intervention is required, which greatly improves the autonomy and convenience of the robot.

[0081] Due to the advanced nature of this solution, it can be widely used in application fields such as robot navigation, intelligent logistics, and smart home. First, in the field of robot navigation, this solution dynamically adjusts the input parameters of the DWA algorithm so that it can dynamically adjust the robot's motion parameters according to different environments, thereby achieving smoother and more efficient navigation. This is of great significance for robots that need to navigate in high-dynamic environments. For example, in the field of warehousing and logistics, robots need to frequently cross narrow passages, bypass obstacles, and make large-angle turns. This technical solution can effectively improve the navigation efficiency and safety of robots. Secondly, in the field of intelligent logistics, this solution can be applied to the path planning and navigation of intelligent logistics vehicles. Intelligent logistics vehicles need to dynamically adjust their motion parameters according to different environments in complex logistics environments to achieve efficient cargo transportation. This solution dynamically adjusts the input parameters of the DWA algorithm so that intelligent logistics vehicles can dynamically adjust their motion parameters according to different environments, thereby achieving smoother and more efficient navigation and improving the efficiency and safety of cargo transportation. Finally, in the field of smart homes, this solution can be applied to the path planning and navigation of intelligent service robots. In a home environment, intelligent service robots need to dynamically adjust their motion parameters according to different environments to achieve efficient services. This solution dynamically adjusts the input parameters of the DWA algorithm so that the intelligent service robot can dynamically adjust its motion parameters according to different environments, thereby achieving smoother and more efficient navigation and improving service efficiency and quality. In general, this solution dynamically adjusts the input parameters of the DWA algorithm so that the robot can dynamically adjust its motion parameters according to different environments to achieve smoother and more efficient navigation. This technology has broad application prospects in the fields of robot navigation, intelligent logistics, smart home, etc., and can well meet the market's needs for efficiency, safety and intelligence.

Claims

1. A robot navigation control method based on 2D laser radar to dynamically adjust DWA parameters, characterized in that: The following steps are involved: S1, use 2D laser radar to obtain the environmental information around the robot in real time; S2. Process the environmental information around the robot to identify the local environment scene around the robot; S3, dynamically adjust the input parameters of the DWA algorithm according to the identified local environment scene, and generate the local path planning of the robot in the current environment; S4. Based on local path planning, control the movement of the robot accordingly.

2. According to claim 1, a robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters is characterized in that: The step S2 specifically processes the environmental information around the robot by using data fitting.

3. According to claim 1, a robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters is characterized in that: The local environment scene identified in step S2 includes an open area, a narrow road entrance, a short narrow road, a long narrow road, a small angle turn, a large angle turn or a U-turn.

4. According to claim 1, a robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters is characterized in that: The dynamic adjustment of the input parameters of the DWA algorithm in step S3 specifically involves adjusting the maximum speed, the minimum speed, the maximum acceleration, the minimum acceleration, the deceleration, the number of sampling points or the sampling time interval.

5. The robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters according to claim 4 is characterized in that: The step S3 of generating the local path planning of the robot in the current environment specifically includes the following process: S31, setting a dynamic window based on the current position, current speed and acceleration limit of the robot; S32, setting the control input as the linear velocity and angular velocity of the robot, for each control input, using a dynamic window to limit the velocity space of the robot, simulating the trajectory of the robot through a forward motion model, and outputting multiple trajectories; S33, performing cost evaluation on multiple trajectories, and screening out the trajectory with the minimum cost as the planned local path.

6. The robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters according to claim 5 is characterized in that: The dynamic window includes a dynamic window of linear velocity and a dynamic window of angular velocity. The dynamic window range of the linear velocity is: [vs-amax*Δt, vx+amax*Δt], and the dynamic window range of the angular velocity is: [ω-dwmax*Δt, ω+dwmax*Δt], wherein vx and ω are the current linear velocity and current angular velocity of the robot respectively, amax and dwmax are the maximum linear acceleration and maximum angular acceleration respectively, and Δt is the time step, that is, the sampling interval.

7. The robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters according to claim 5, characterized in that: The forward motion model in step S32 is specifically a relationship model between the robot's speed, angular velocity and position.

8. The robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters according to claim 5 is characterized in that: The cost evaluation process in step S33 includes: The target proximity, safety and smoothness scores corresponding to the trajectory are calculated respectively, and the evaluation score of the trajectory is obtained by weighted summation.

9. The robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters according to claim 8, characterized in that: The target proximity is specifically the distance between the track and the target. The closer the track is to the target, the higher the target proximity score is. The safety score is specifically the distance between the track and the obstacle. The farther the track is from the obstacle, the higher the safety score. The smoothness is specifically the angular velocity change rate of the trajectory. The smaller the angular velocity change rate is, the higher the smoothness score is.

10. The robot navigation control method based on 2D laser radar dynamically adjusting DWA parameters according to claim 8, characterized in that: The step S33 specifically selects the trajectory with the highest evaluation score value as the trajectory with the minimum cost.

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