Motion planning method and device, robot, readable storage medium and program product

Abstract

The invention relates to a motion planning method and device, a robot, a readable storage medium and a program product. A kinematic model corresponding to a chassis is generated based on chassis state information and virtual wheel information corresponding to wheel sets, and the speed and angle of each virtual wheel are not associated; and acquiring motion constraint information of the chassis, and performing motion planning on the chassis according to the kinematic model and the motion constraint information. Compared with a traditional method that motion planning is conducted by switching between a double Ackerman steering mode and an oblique moving mode, the method has the advantages that the limitation that all wheels in the chassis must rotate in the same angle and opposite directions is relieved, multiple pieces of virtual wheel information are constructed, the kinematics model is generated in combination with the chassis state information and the virtual wheel information, and the motion planning accuracy is improved. And motion planning is performed on the chassis by combining the kinematics model and the motion constraint information of the chassis, so that the operation efficiency of the chassis is improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a motion planning method, device, robot, computer-readable storage medium, and computer program product. Background Art

[0002] With the advancement of science and technology, the demand for automation and intelligence in the service and manufacturing industries continues to increase, and autonomous mobile robots have been put into use in many fields. Autonomous mobile robots have a mobile chassis for movement. The mobile chassis is provided with drive wheels, which can control the robot to move. At present, the chassis movement of autonomous mobile robots includes double Ackerman steering mode and oblique shift mode. The chassis realizes trajectory planning of the motion path by switching between the two modes. However, at the moment of switching between the two modes, the chassis still causes the speed of zero, resulting in the inefficient planning path.

[0003] Therefore, the current method for motion planning of the chassis of an autonomous mobile robot has the defect of low operating efficiency. Summary of the invention

[0004] Based on this, it is necessary to provide a motion planning method, device, robot, computer-readable storage medium and computer program product that can improve the operating efficiency of the chassis in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a motion planning method, comprising:

[0006] Acquire a kinematic model corresponding to a chassis; the kinematic model is generated based on chassis state information, first virtual wheel information, and second virtual wheel information of the chassis; the speeds and angles of the first virtual wheel information and the second virtual wheel information are not associated; the first virtual wheel information corresponds to a first wheel set of the chassis, and the second virtual wheel information corresponds to a second wheel set of the chassis; a vertical line corresponding to a wheel of the first wheel set points to a first center of a circle when turning; and a vertical line corresponding to a wheel of the second wheel set points to a second center of a circle when turning;

[0007] Acquiring motion constraint information of the chassis;

[0008] Performing motion planning for the chassis according to the kinematic model and the motion constraint information.

[0009] In a second aspect, the present application further provides a motion planning device, comprising:

[0010] A first acquisition module is used to acquire a kinematic model corresponding to a chassis; the kinematic model is generated based on chassis state information, first virtual wheel information, and second virtual wheel information of the chassis; the speeds and angles of the first virtual wheel information and the second virtual wheel information are not associated; the first virtual wheel information corresponds to a first wheel set of the chassis, and the second virtual wheel information corresponds to a second wheel set of the chassis; a vertical line corresponding to the wheel of the first wheel set points to a first center of a circle when turning; and a vertical line corresponding to the wheel of the second wheel set points to a second center of a circle when turning;

[0011] A second acquisition module, used to acquire motion constraint information of the chassis;

[0012] A planning module is used to perform motion planning for the chassis according to the kinematic model and the motion constraint information.

[0013] In a third aspect, the present application further provides a robot, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0015] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0016] The above-mentioned motion planning method, device, robot, computer-readable storage medium and computer program product generate a kinematic model corresponding to the chassis based on chassis state information and each virtual wheel information corresponding to each wheel group, and the speed and angle of each virtual wheel are not related; obtain the motion constraint information of the chassis, and perform motion planning for the chassis according to the kinematic model and the motion constraint information. Compared with the traditional motion planning by switching between the double Ackerman steering mode and the oblique shift mode, this solution removes the restriction that each wheel in the chassis must rotate at the same angle and in opposite directions, constructs multiple virtual wheel information, and generates a kinematic model in combination with the chassis state information and the virtual wheel information, and performs motion planning for the chassis in combination with the kinematic model and motion constraint information of the chassis, thereby improving the operating efficiency of the chassis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a schematic flow chart of a motion planning method in one embodiment;

[0019] Figure 2 is a schematic diagram of the structure of a kinematic model in one embodiment;

[0020] Figure 3 is a structural block diagram of a motion planning device in one embodiment;

[0021] Figure 4 1 is a diagram showing the internal structure of a robot in one embodiment. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] In the related art, in the application scenarios of autonomous mobile robots, two-wheel differential chassis, four-wheel Mecanum wheel chassis and four-wheel four-turn chassis each have their own unique advantages and disadvantages. The two-wheel differential chassis has a simple structure, mature control algorithm, and low cost. It is suitable for flat indoor environments and can achieve in-situ rotation and more flexible movement. However, it cannot achieve omnidirectional movement, has low motion accuracy, has high requirements for ground flatness, and has poor passability on complex or rough ground. The four-wheel Mecanum wheel chassis provides omnidirectional mobility, can achieve precise motion control, and is suitable for occasions requiring precise operation. However, its manufacturing cost is high, its load capacity is limited, its wheels are subject to wear, operating noise, and energy loss are large, and its structure is complex, and the control algorithm is difficult to implement. The four-wheel four-turn chassis combines the advantages of structure and control, with a structural design of independent steering, drive and suspension, thereby achieving the effect of omnidirectional movement, extremely high control efficiency and control accuracy, low failure rate, high stability, and provides superior passability and off-road performance, and can adapt to a variety of complex terrains and scenes. Although, compared with the other two chassis, the cost of the four-wheel four-turn chassis is higher, its excellent performance and wide range of application scenarios make it an ideal choice for many high-end and complex tasks.

[0024] In order to simplify the kinematic model of the chassis, the current motion planning method for the four-wheel four-turn chassis usually converts the chassis motion into different driving modes to achieve complex motion. Two common modes are the double Ackerman steering mode and the oblique shift mode. In the double Ackerman steering mode, the four-wheel four-turn chassis imitates the motion of the two-wheel differential. The steering angles of the front and rear virtual middle wheels are the same in size and opposite in direction. By accurately calculating the four-wheel turning angles, it is ensured that the centers of the four wheel driving paths converge at one point, that is, the instantaneous steering center, to achieve smooth driving. In the oblique shift mode, the four-wheel rotation angle and driving speed are the same, so that the chassis can achieve oblique movement and translation. This mode is particularly suitable for motion planning in narrow spaces.

[0025] In order to ensure smooth switching between the two modes and achieve efficient and safe trajectory planning, during the switching process, it is necessary to comprehensively consider factors such as the current state of the chassis, kinematic constraints, and the surrounding environment to generate a smooth trajectory that meets the switching requirements. This method not only requires the continuity and feasibility of the trajectory, but also ensures the stability and safety of the vehicle during the switching process to achieve efficient and flexible chassis control. However, at the moment of switching between the two modes, the chassis will inevitably have a speed of zero. And because the above method does not completely simplify the model, most of the chassis motion characteristics are eliminated, and the full motion capacity of the chassis cannot be exerted, which increases the energy loss of the planned trajectory. Therefore, how to plan an efficient motion trajectory is still a technical problem that needs to be solved urgently.

[0026] Based on this, the present application removes the restriction that each wheel in the chassis must rotate at the same angle and in opposite directions, constructs multiple virtual wheel information, and generates a kinematic model by combining the chassis status information and the virtual wheel information. The kinematic model and motion constraint information of the chassis are combined to perform motion planning for the chassis, thereby improving the operating efficiency of the chassis.

[0027] In one embodiment, Figure 1 As shown, a motion planning method is provided. This embodiment takes a robot executing the method as an example. The robot may be a four-wheel chassis autonomous mobile robot such as a humanoid robot. It is understandable that the method may also be applied to a system including a robot and a server, and is implemented through the interaction between the robot and the server, including the following steps S202 to S206. Among them:

[0028] Step S202, obtaining a kinematic model corresponding to the chassis; the kinematic model is generated based on chassis state information, first virtual wheel information and second virtual wheel information of the chassis; the speeds and angles of the first virtual wheel information and the second virtual wheel information are not related; the first virtual wheel information corresponds to the first wheel group of the chassis, and the second virtual wheel information corresponds to the second wheel group of the chassis; the vertical line corresponding to the wheel of the first wheel group points to the first center of the circle when turning; the vertical line corresponding to the wheel of the second wheel group points to the second center of the circle when turning.

[0029] The robot can be a four-wheel chassis autonomous mobile robot, which integrates the autonomous navigation capability of a mobile robot and the flexible operation capability of a humanoid robot. This robot not only has a mobile chassis, can autonomously locate and navigate, and can move flexibly in a complex environment, but also has a robotic arm that can perform precision operations and carry items. In order to achieve efficient motion planning, the robot needs to combine the kinematic model of the chassis to achieve motion planning.

[0030] The robot can determine the first virtual wheel information based on the first wheel group of the chassis, and determine the second virtual wheel information based on the second wheel group of the chassis. The first wheel group and the second wheel group can be wheels at different positions in the chassis of the robot. For example, the first wheel group can be the two front wheels of the chassis, and the second wheel group can be the two rear wheels of the chassis. The first virtual wheel information and the second virtual wheel information can be information of virtual wheels whose center points are on the center line of the chassis. The first virtual wheel information can be called virtual front wheel information, and the second virtual wheel information can be called virtual rear wheel information. The robot can construct a kinematic model corresponding to the chassis based on the chassis state information, the first virtual wheel information and the second virtual wheel information of the chassis, so that the robot obtains the kinematic model corresponding to the chassis.

[0031] In one embodiment, a kinematic model corresponding to a chassis is obtained, including: generating corresponding first virtual wheel information and second virtual wheel information respectively according to motion information of each wheel corresponding to a first wheel group and a second wheel group of the chassis; obtaining chassis status information of the chassis according to position information and orientation information corresponding to the chassis; and determining a corresponding kinematic model according to the chassis status information including position information and orientation information, the first virtual wheel information and the second virtual wheel information.

[0032] In this embodiment, when constructing the kinematic model, the robot can obtain the motion information of each wheel corresponding to the first wheel group of the chassis, and obtain the motion information of each wheel corresponding to the second wheel group, so that the robot generates the first virtual wheel information according to the motion information of each wheel in the first wheel group, and generates the second virtual wheel information according to the motion information of each wheel in the second wheel group. The above motion information includes information such as the speed, acceleration and rotation angle of the wheel.

[0033] The robot can also determine the chassis state information of the chassis according to the position information and orientation information corresponding to the chassis. That is, the chassis state information includes the position information and orientation information of the chassis, so that the robot can determine the corresponding kinematic model according to the chassis state information including the position information and orientation information, the first virtual wheel information, and the second virtual wheel information.

[0034] Among them, the speed and angle of the first virtual wheel information and the second virtual wheel information are not related, that is, the speed of the first virtual wheel information is not related to the speed of the second virtual wheel information, and the angle of the first virtual wheel information is not related to the angle of the second virtual wheel information, so that more complex movements can be achieved; and the vertical lines corresponding to the wheels of the first wheel group point to the first center of the circle when turning, and the wheels of the second wheel group point to the second center of the circle when turning. That is, when the wheels of the first wheel group are turning, the vertical lines of the two wheels point to the same center of the circle, and when the wheels of the second wheel group are turning, the vertical lines of the two wheels point to the same center of the circle. The first center of the circle and the second center of the circle can be the same center of the circle or different centers of the circle, so that the wheels of the first wheel group meet the Ackerman steering mode, and the wheels of the second wheel group also meet the Ackerman steering mode.

[0035] Specifically, the structure corresponding to the above kinematic model can be as follows Figure 2 As shown, Figure 2 Schematic diagram of the structure of the kinematic model in one embodiment. The chassis can be a four-wheel four-turn chassis. The robot simplifies the chassis into a bicycle model that can turn both front and rear. The two front wheels and two rear wheels of the chassis obey Ackerman steering respectively, but there is no restriction that the rotation angles of the front and rear virtual middle wheels must be the same in size and opposite in direction. This makes the chassis not only include the oblique movement mode, but also greatly increases the movement characteristics of the chassis, realizing more complex movements.

[0036] Assume that the four-wheel four-turn chassis is rigidly connected and the wheel deformation is ignored. The chassis mainly travels on the horizontal ground. The chassis control point is the four-wheel geometric center point M. In motion planning, point M represents the chassis, and the state quantity is (x, y, θ, v f ,v r ,δ f ,δ r ), mainly including chassis position information (x, y), chassis orientation information θ and other chassis status information, and also including first virtual wheel information and second virtual wheel information, wherein the first virtual wheel information may be information of a virtual front wheel, and the second virtual wheel information may be information of a virtual rear wheel, specifically including virtual front wheel speed v f , virtual rear wheel speed v r , virtual front wheel steering angle δ f 、Virtual rear wheel turning angle δ r; The control amount of the chassis is (a f ,a r ,δ f ',δ r '), mainly including the virtual front wheel acceleration a f Virtual front wheel steering speed δ f 'The first virtual wheel information, and the virtual rear wheel acceleration a r , virtual rear wheel steering speed δ r 'And other second virtual wheel information. The kinematic model is as follows:

[0037] ;

[0038] Where β represents the slip angle of the control point M, v m Expressed as the velocity of the control point M, a m It is expressed as the acceleration of control point M, Δs represents the path length of the chassis between two adjacent control points M, Δθ represents the change of the chassis orientation between two adjacent control points M, Δt represents the time consumed by the chassis between two adjacent points, Δβ represents the change of the side slip angle between two adjacent control points M, t represents the time point t, Δt represents the change of the time between two adjacent control points M, x t ,y t represents the position of the control point M at time t, v f,t represents the virtual front wheel speed at time t, v r,t represents the virtual rear wheel speed at time t, δ f,t represents the virtual front wheel turning angle at time t, δ r,t represents the virtual rear wheel turning angle at time t, θ t represents the orientation of the chassis at time t, β t represents the side slip angle of the control point M at time point t, L represents the length of the chassis, and W represents the width of the chassis. The above two adjacent points represent two adjacent trajectory points in motion planning.

[0039] Since the chassis is a four-wheel four-turn chassis, the first virtual wheel information corresponds to the two front wheels of the chassis, that is, the first wheel group, and the second virtual wheel information corresponds to the two rear wheels of the chassis, that is, the second wheel group. The robot needs to simplify the four-wheel four-turn chassis into a bicycle model that can turn both front and rear. When constructing the optimization problem of solving the planning trajectory, in order to consider the smoothness of the planning trajectory and the current state of the chassis, it is necessary to derive the four-wheel steering drive state to the front and rear virtual middle wheel steering drive state, thereby converting the four wheels into the first virtual wheel information and the second virtual wheel information. The specific formula is as follows:

[0040]

[0041] Among them, δfl Indicates the left front wheel steering angle, v fl Expressed as left front wheel drive speed, δ fr Expressed as the right front wheel steering angle, v fr Expressed as the right front wheel drive speed, δ rl Expressed as the left rear wheel steering angle, v rl Expressed as the left rear wheel drive speed, δ rr Expressed as the right rear wheel steering angle, v rr Indicates the right rear wheel drive speed.

[0042] Step S204, obtaining the motion constraint information of the chassis.

[0043] When controlling the chassis to move, due to obstacles in the environmental information and the constraints of the chassis itself, it is necessary to combine multiple motion constraint information for motion planning when planning the chassis. Then the robot can obtain the motion constraint information of the chassis. Among them, the motion constraint information of the chassis includes but is not limited to the constraints on the movement of the wheels in the chassis, the constraints of the chassis kinematic model, and the constraints of the trajectory restrictions formed by obstacles. The robot can construct the motion constraints of the chassis by means of cost functions.

[0044] Step S206: Perform motion planning for the chassis according to the kinematic model and the motion constraint information.

[0045] After obtaining the kinematic model and motion constraint information, the robot can perform motion planning for the chassis according to the kinematic model and motion constraint information. When the robot performs motion planning for the chassis, it can perform planning through multiple steps. For example, the robot can first obtain the initial solution of the path to obtain the initial trajectory, and then optimize the trajectory in combination with the initial solution, kinematic model and motion constraint information to obtain a smoother trajectory.

[0046] Among them, the state quantities contained in the constructed kinematic model are all the virtual wheel information of each virtual wheel. When solving the planned trajectory, in order to ensure the smoothness of the trajectory and the smooth execution of commands, the state described by the first point of the trajectory is the current state of the robot. Therefore, it is necessary to convert the state of the virtual wheel information of each virtual wheel according to the real-time state of the robot and assign it to the first trajectory point to determine the trajectory point.

[0047] In the above motion planning method, a kinematic model corresponding to the chassis is generated based on the chassis state information of the chassis and the virtual wheel information corresponding to each wheel group, and the speed and angle of each virtual wheel are not related; the motion constraint information of the chassis is obtained, and the chassis is motion planned according to the kinematic model and the motion constraint information. Compared with the traditional motion planning by switching between the double Ackerman steering mode and the oblique shift mode, this solution removes the restriction that each wheel in the chassis must rotate at the same angle and in opposite directions, constructs multiple virtual wheel information, and generates a kinematic model in combination with the chassis state information and the virtual wheel information, and performs motion planning on the chassis in combination with the kinematic model and motion constraint information of the chassis, thereby improving the operating efficiency of the chassis.

[0048] In one embodiment, obtaining the motion constraint information of the chassis includes: generating first motion constraint information according to speed and steering restriction information corresponding to the first wheel group and the second wheel group of the chassis; and / or generating second motion constraint information according to the kinematic model restriction corresponding to the chassis; and / or generating third motion constraint information according to the trajectory restriction information; obtaining the motion constraint information according to the first motion constraint information, the second motion constraint information and / or the third motion constraint information.

[0049] In this embodiment, in the process of motion planning for the chassis, the robot can optimize the motion trajectory of the chassis, and the robot can combine multiple motion constraint information to perform motion planning for the chassis. Among them, the above-mentioned motion constraint information can include multiple types, such as first motion constraint information, second motion constraint information and third motion constraint information; the robot can generate first motion constraint information according to the speed and steering restriction information corresponding to the first wheel group and the second wheel group of the chassis, that is, the first motion constraint information indicates that there are corresponding restrictions on the speed and steering of the first wheel group and the second wheel group of the chassis; the robot can also generate second motion constraint information according to the kinematic model restriction corresponding to the chassis, that is, the second motion constraint information indicates that due to the existence of a certain structural volume of the chassis, the limitations of the chassis itself need to be considered during motion planning; the robot can also generate third motion constraint information according to the trajectory restriction information, that is, when the robot performs trajectory planning, the planned trajectory should be sufficiently smooth and continuous, and have a starting point and an end point, so the robot needs to consider the trajectory restriction information to form corresponding motion constraint information.

[0050] Therefore, the robot can obtain motion constraint information according to the first motion constraint information, the second motion constraint information and / or the third motion constraint information, and optimize the trajectory when performing motion planning in combination with the above-mentioned motion constraint information.

[0051] Specifically, when the robot performs path planning, it can first perform initial solution planning to obtain initial path information. Each trajectory point in the initial path information contains position information in two-dimensional space, and the robot constructs the above chassis trajectory optimization expression as follows: f(B)=∑ k (γ k f k (B)), Among them, f k (B) is the kth cost function, B * is the optimal solution after optimization, that is, the optimal path or state in motion planning; that is, the robot can define the optimization problem as a least squares problem. In order to better express the motion constraint information contained in the least squares problem, a cost function corresponding to the motion constraint information can be constructed. The cost function is specifically expressed as: τ (x,x r ,ε,S,n) is asymptotically equal to {((x-(x r -ε)) / S) n , if x>x r -ε; 0, otherwise}. Among them, x represents the parameter to be optimized, x r Represents the threshold value that the parameter needs to refer to, ε represents the tolerance parameter, S represents the scaling factor, which is used to adjust the amplitude of the error, and n represents the exponential parameter, which is used to control the shape of the error function. The above-mentioned first motion constraint information includes one or more of the restrictions on the driving speed of the four wheels, the restrictions on the driving acceleration of the four wheels, the physical limit of the steering of the four wheels, the maximum steering speed limit of the four wheels, and the matching degree of the steering and speed of the four wheels; the second motion constraint restriction includes the restriction on the kinematic model constraint; the third motion constraint restriction includes one or more of the trajectory points cannot collide with obstacles and are as close to the global path as possible, the time and distance between two adjacent trajectory points are as small as possible, and the starting point and end point of the trajectory. The above-mentioned various motion constraint information can be represented by a cost function.

[0052] Among them, the maximum limit of the driving speed of the four wheels is considered during optimization, and a cost function is added for each trajectory point. The cost function corresponding to each wheel can be specifically expressed as: f v(i,fl) =e τ (v i,fl ,v max ,ε,S,n)+e τ (-v i,fl ,-v min ,ε,S,n);f v(i,fr) =e τ (v i,fr ,v max ,ε,S,n)+e τ (-v i,fr,-v min ,ε,S,n);f v(i,rl) =e τ (v i,rl ,v max ,ε,S,n)+e τ (-v i,rl ,-v min ,ε,S,n);f v(i,rr) =e τ (v i,rr ,v max ,ε,S,n)+e τ (-v i,rr ,-v min ,ε,S,n). Among them, v i,fl represents the speed of the left front wheel at point i, v i,fr represents the speed of the right front wheel at point i, v i,rl represents the speed of the left rear wheel at point i, v i,rr represents the speed of the right rear wheel at point i, v max Indicates the maximum speed, v min Indicates the minimum speed.

[0053] There is a maximum limit on the driving acceleration of the four wheels. For each trajectory point, the robot increases the cost function as follows: a(i,fl) =e τ (a i,fl ,a max ,ε,S,n)+e τ (-a i,fl ,-a min ,ε,S,n);f a(i,fr) =e τ (a i,fr ,a max ,ε,S,n)+e τ (-a i,fr ,-a min ,ε,S,n);f a(i,rl) =e τ (a i,rl ,a max ,ε,S,n)+e τ (-a i,rl ,-a min ,ε,S,n);f a(i,rr) =e τ (a i,rr ,a max ,ε,S,n)+e τ (-a i,rr ,-a min ,ε,S,n). Among them, a i,fl represents the acceleration of the left front wheel at point i, ai,fr represents the acceleration of the right front wheel at point i, a i,rl represents the speed of the left rear wheel at point i, a i,rr represents the acceleration of the right rear wheel at point i, a max Indicates the maximum acceleration, a min Indicates the minimum acceleration.

[0054] In the process of trajectory optimization, there is a maximum limit on the steering speed of the four wheels. For each trajectory point, the following cost function can be added: δ'(i,fl) =e τ (δ ' i,fl ,δ ' max ,ε,S,n)+e τ (-δ ' i,fl ,-δ ' min ,ε,S,n);f δ'(i,fr) =e τ (δ ' i,fr ,δ ' max ,ε,S,n)+e τ (-δ ' i,fr ,-δ ' min ,ε,S,n);f δ'(i,rl) =e τ (δ ' i,rl ,δ ' max ,ε,S,n)+e τ (-δ ' i,rl ,-δ ' min ,ε,S,n);f δ'(i,rr) =e τ (δ ' i,rr ,δ ' max ,ε,S,n)+e τ (-δ ' i,rr ,-δ ' min ,ε,S,n). Among them,δ ' i,fl represents the turning speed of the left front wheel at point i, δ ' i,fr represents the turning speed of the right front wheel at point i, δ ' i,rlrepresents the speed of the left rear wheel at point i, δ ' i,rr represents the turning speed of the right rear wheel at point i, δ ' max Indicates the maximum steering speed, δ ' min Indicates the minimum steering speed.

[0055] In the optimization process, the mismatch between four-wheel steering and speed will affect the stability of chassis driving. Therefore, the robot can add the following cost function for each trajectory point: f stab,i =(v f,i cos(δ f,i )-v r,i cos(δ r,i )) 2 Among them, f stab,i represents a stable function, v f,i represents the speed of the virtual front wheel at point i, v r,i represents the speed of the virtual rear wheel at point i, δ f,i represents the rotation angle of the virtual front wheel at point i, δ r,i Indicates the rotation angle of the virtual rear wheel at point i.

[0056] The kinematic model constraints between two adjacent trajectory points are also included in the optimization process. The robot adds the following cost function to the two adjacent trajectory points:

[0057]

[0058] Among them, X i Represents the kinematic model of point i.

[0059] For trajectory constraints, during optimization, it is considered that the trajectory point cannot collide with obstacles and should be as close to the global path as possible. The robot adds the following cost function to each trajectory point: f path,ij =e τ (d min,ij ,r pmax ,ε,S,n);f obs,ij =e τ (-d min,ij ,-r pmax ,ε,S,n). Among them, f path,ij represents the trajectory stabilization function, f obs,ij represents the obstacle function, d min,ij represents the minimum distance of the trajectory from point i to point j, r pmax Indicates the maximum influence radius of the obstacle.

[0060] When optimizing, the robot also needs to consider the time and distance between two adjacent trajectory points as small as possible, so the following cost function is added to each two adjacent trajectory points: t,i =Δt i 2 ;f s,i =Δs i 2 Among them, f t,i represents the time function of moving to point i, f s,i Represents the distance function to point i, Δt i represents the time to move to point i, Δs i Indicates the distance moved to point i.

[0061] In addition, the starting point and end point of the trajectory must also be considered in path planning. The starting point and end point of the trajectory are constants and are not optimization variables. Instead, they are motion constraint information that restricts the trajectory. Specifically, they can be expressed as: X0=[x init ,y init ,θ init ,v f,init ,v r,init ,v r,init ,δ f,init ,δ r,init ], X n =[x end ,y end ,θ end ,v f,end ,v r,end ,v r,end ,δ f,end ,δ r,end ]. Among them, x init ,y init represents the initial position of the chassis represented by the kinematic model, θ init Indicates the initial orientation of the chassis, v f,init represents the initial speed of the virtual front wheel, v r,init represents the initial speed of the virtual rear wheel, δ f,init represents the initial steering angle of the virtual front wheel, δ r,init Indicates the initial steering angle of the virtual front wheel; x end ,y end represents the position of the chassis represented by the kinematic model when it reaches the end point, θ end Indicates the direction of the chassis when it reaches the end point, v f,end Indicates the speed of the virtual front wheel when it reaches the end point, v r,end represents the speed of the virtual rear wheel when it reaches the end point, δ f,end represents the steering angle of the virtual front wheel when it reaches the end point, δ r,end Indicates the steering angle of the virtual front wheel when reaching the end point.

[0062] Through this embodiment, the robot constructs speed steering restrictions, kinematic model restrictions and trajectory restrictions for the chassis, and respectively constructs cost functions corresponding to the corresponding motion constraint information, and uses the motion constraint information to plan the motion of the chassis, thereby improving the rationality of motion planning and the effectiveness of the planned trajectory.

[0063] In one embodiment, motion planning is performed on the chassis based on the kinematic model and motion constraint information, including: obtaining initial trajectory point information; optimizing the trajectory of the chassis based on the motion constraint information, the initial trajectory point information and the kinematic model to obtain target trajectory point information; and obtaining the motion planning result corresponding to the chassis based on the target trajectory point information.

[0064] In this embodiment, the robot can perform motion planning for the chassis in combination with the kinematic model and motion constraint information. Wherein, planning includes initial planning and trajectory optimization. The robot can first obtain the initial trajectory point information, such as generating the initial trajectory point information for the starting point, the end point and the obstacle information in between. The robot can also optimize the initial trajectory point information. For example, the robot optimizes the trajectory of the chassis according to the motion constraint information, the initial trajectory point information and the kinematic model, obtains the target trajectory point information, and obtains the motion planning result corresponding to the chassis according to the target trajectory point information. Wherein, the above-mentioned motion constraint information includes the first motion constraint information, the second motion constraint information and the third motion constraint information; and the first motion constraint information represents the speed and steering restrictions on the first wheel group and the second wheel group; the second motion constraint information represents the kinematic model restrictions corresponding to the chassis; the third motion constraint information represents the restrictions on the trajectory. The robot can perform the trajectory optimization process in combination with the above-mentioned various motion constraint information.

[0065] In one embodiment, the chassis is trajectory optimized according to motion constraint information, initial trajectory point information and a kinematic model to obtain target trajectory point information, including: determining first wheel motion information and second wheel motion information of the first wheel group and the second wheel group of the chassis corresponding to the initial trajectory point information according to first virtual wheel information and second virtual wheel information corresponding to the kinematic model, respectively; determining optimized reference wheel motion information according to first motion constraint information, first wheel motion information and second wheel motion information; determining optimized reference chassis state information according to second motion constraint information and chassis state information corresponding to the kinematic model; determining optimized reference trajectory point information according to third motion constraint information and initial trajectory point information; adjusting the initial trajectory point information according to reference wheel motion information, reference chassis state information and reference trajectory point information until the weighted sum of reference wheel motion information, reference chassis state information and reference trajectory point information is minimized to obtain the target trajectory point information.

[0066] In this embodiment, the robot can realize the motion optimization of different angles of the chassis based on different motion constraint information. Among them, the robot can determine the first wheel motion information and the second wheel motion information corresponding to the initial trajectory point information of the first wheel group and the second wheel group of the chassis respectively according to the first virtual wheel information and the second virtual wheel information corresponding to the kinematic model; that is, the robot can realize the conversion from the virtual wheel information to the motion information of the wheels of the corresponding wheel group. Therefore, the robot can determine the optimized reference wheel motion information according to the first motion constraint information, the first wheel motion information and the second wheel motion information. Among them, the reference wheel motion information represents the motion information such as the speed and angle of the wheel obtained after optimization according to the motion constraint information.

[0067] The robot can also determine optimized reference chassis state information according to the second motion constraint information and chassis state information corresponding to the kinematic model, wherein the reference chassis state information represents information such as the position and orientation of the chassis obtained after optimization according to the motion constraint information.

[0068] The robot can also determine the optimized reference trajectory point information according to the third motion constraint information and the initial trajectory point information. The reference trajectory point information represents the information of the trajectory point obtained after optimization according to the motion constraint information. Thus, the robot can adjust the initial trajectory point information according to the reference wheel motion information, the reference chassis state information and the reference trajectory point information until the weighted sum of the reference wheel motion information, the reference chassis state information and the reference trajectory point information is minimized, and the target trajectory point information is obtained. That is, the robot can obtain the target trajectory point information by combining each cost function f k (B), so that f(B) is minimized, the final target trajectory point information is obtained, and the optimal path B is obtained based on the target trajectory point information * .

[0069] Specifically, when performing motion planning, the obstacle information around the chassis is represented in the form of a grid map, and the initial trajectory point information is determined, that is, the robot can use the A* algorithm to perform a global search in the created grid map to obtain the initial solution information consisting of a point sequence from the starting point to the end point as the initial trajectory point information. Among them, each trajectory point in the initial trajectory point information contains position information in two-dimensional space. Therefore, the robot can solve the optimization problem based on the above prerequisites and the various cost functions corresponding to the above motion constraint information, thereby obtaining the smooth planning trajectory information of the four-wheel four-turn chassis from the starting point to the end point as the target trajectory point information.

[0070] Through the above embodiments, the robot performs initial trajectory planning for the chassis, constructs speed steering restrictions, kinematic model restrictions and trajectory restrictions for the chassis, respectively constructs cost functions corresponding to the corresponding motion constraint information, and uses the motion constraint information to optimize the initial trajectory of the chassis, thereby improving the rationality and smoothness of the planned trajectory point information, thereby improving the robot's travel efficiency.

[0071] In one embodiment, first wheel motion information and second wheel motion information corresponding to the initial trajectory point information of the first wheel group and the second wheel group of the chassis are respectively determined according to the first virtual wheel information and the second virtual wheel information corresponding to the kinematic model, including: determining the first wheel rotation angle corresponding to the first wheel group and the second wheel rotation angle corresponding to the second wheel group according to the first virtual wheel rotation angle corresponding to the initial trajectory point information and the second virtual wheel rotation angle corresponding to the second virtual wheel information; determining the first wheel speed corresponding to the first wheel group according to the first virtual wheel rotation angle and the first wheel rotation angle; determining the second wheel speed corresponding to the second wheel group according to the second virtual wheel rotation angle and the second wheel rotation angle; determining the first wheel motion information corresponding to the initial trajectory point information of the first wheel group according to the first wheel rotation angle and the first wheel speed; determining the second wheel motion information of the second wheel group corresponding to the initial trajectory point information according to the second wheel rotation angle and the second wheel speed.

[0072] In this embodiment, when planning, it is necessary to construct the first virtual wheel information and the second virtual wheel information corresponding to the chassis, and after the optimization is completed, it is necessary to determine the control instructions for each wheel, so the robot needs to convert the virtual wheel information into the motion information of the corresponding wheel. Among them, the robot can determine the first wheel rotation angle corresponding to the first wheel group and the second wheel rotation angle corresponding to the second wheel group according to the first virtual wheel rotation angle corresponding to the initial track point information of the first virtual wheel information and the second virtual wheel rotation angle corresponding to the second virtual wheel information; that is, the robot determines the first wheel rotation angle corresponding to the first wheel group based on the first virtual wheel rotation angle corresponding to the initial track point information of the first virtual wheel information, wherein the first wheel group may be the front wheel group of the chassis; the robot determines the second wheel rotation angle corresponding to the second wheel group based on the second virtual wheel rotation angle corresponding to the initial track point information of the second virtual wheel information, wherein the second wheel group may be the rear wheel group of the chassis.

[0073] The robot can also determine a first wheel speed corresponding to the first wheel set according to the first virtual wheel rotation angle and the first wheel rotation angle; and determine a second wheel speed corresponding to the second wheel set according to the second virtual wheel rotation angle and the second wheel rotation angle.

[0074] Therefore, the robot can determine the first wheel motion information corresponding to the initial track point information of the first wheel group according to the first wheel rotation angle and the first wheel speed. And determine the second wheel motion information corresponding to the initial track point information of the second wheel group according to the second wheel rotation angle and the second wheel speed. Among them, at each track point, the robot can use the above method to realize the conversion of virtual wheel information to four-wheel motion information.

[0075] Specifically, after the robot solves the optimization problem, it selects a trajectory point close to the current position of the chassis from the planned trajectory, and calculates the four-wheel four-turn chassis control command according to the kinematic model state at the trajectory point. Therefore, the robot needs to switch from the front and rear virtual wheel steering drive state to the four-wheel steering drive state, that is, to realize the conversion of virtual wheel information to four-wheel motion information, which can be specifically expressed as:

[0076]

[0077] Among them, v f represents the virtual front wheel speed, v r represents the virtual rear wheel speed, δ f represents the virtual front wheel turning angle, δ r represents the virtual rear wheel turning angle, δ fl Indicates the left front wheel steering angle, v fl Expressed as left front wheel drive speed, δ fr Expressed as the right front wheel steering angle, v fr Expressed as the right front wheel drive speed, δ rl Expressed as the left rear wheel steering angle, v rl Expressed as the left rear wheel drive speed, δ rr Expressed as the right rear wheel steering angle, v rr Indicates the right rear wheel drive speed.

[0078] Through this embodiment, the robot can realize the conversion of motion information of the wheels of the corresponding wheel group based on the relevant information of the virtual wheels, thereby using the control instructions for the movement of the actual wheels to control the movement of the chassis, thereby improving the operating efficiency of the chassis during motion planning.

[0079] In the related art, the chassis realizes movement by switching between two operating modes, but at the moment of switching between the two modes, the chassis speed will inevitably be zero. And because the above method does not completely simplify the model, most of the chassis movement characteristics are eliminated, and the full movement capacity of the chassis cannot be exerted, thereby increasing the energy loss of the planned trajectory.

[0080] In an exemplary embodiment, another motion planning method is provided. In this embodiment, in order to simplify the kinematic model of the four-wheel four-turn chassis, the robot simplifies the chassis into a bicycle model that can steer both front and rear, such as Figure 2 As shown in the figure. The two front wheels and the two rear wheels obey Ackerman steering respectively, but there is no restriction that the rotation angles of the front and rear virtual middle wheels must be the same in magnitude and opposite in direction. Compared with the double Ackerman steering mode, this simplified method not only includes the oblique movement mode, but also greatly increases the movement characteristics of the chassis, which can achieve more complex movements, but at the same time, the complexity of the kinematic model is also greatly improved.

[0081] Assume that the four-wheel four-turn chassis is rigidly connected and the wheel deformation is ignored. The chassis mainly travels on the horizontal ground. The chassis control point is the four-wheel geometric center point M. In motion planning, point M represents the chassis, and the state quantity is (x, y, θ, v f ,v r ,δ f ,δ r ), mainly including chassis position information (x, y), chassis orientation information θ and other chassis status information, and also including first virtual wheel information and second virtual wheel information, wherein the first virtual wheel information may be information of a virtual front wheel, and the second virtual wheel information may be information of a virtual rear wheel, specifically including virtual front wheel speed v f , virtual rear wheel speed v r , virtual front wheel steering angle δ f 、Virtual rear wheel turning angle δ r ; The control amount of the chassis is (a f ,a r ,δ f ',δ r '), mainly including the virtual front wheel acceleration a f , virtual front wheel steering speed δ f 'The first virtual wheel information, and the virtual rear wheel acceleration a r , virtual rear wheel steering speed δ r 'Wait for the second virtual wheel information.

[0082] In addition, the robot can also convert the four-wheel steering drive state to the front and rear virtual wheel information drive state. When constructing the kinematic model of the chassis, the four-wheel four-turn chassis is simplified to a bicycle model that can turn both front and rear. When performing trajectory optimization, in order to consider the smoothness of the planned trajectory, the current state of the chassis is considered, and it is necessary to derive the four-wheel steering drive state to the front and rear virtual middle wheel steering drive state, thereby converting the four wheels into the first virtual wheel information and the second virtual wheel information. In addition, after the optimization problem is completed, the robot needs to actually control the movement of each wheel. The robot selects a trajectory point close to the current position of the chassis from the planned trajectory, and calculates the four-wheel four-turn chassis control command according to the kinematic model state at the trajectory point, so that the robot needs to convert the front and rear virtual wheel steering drive state to the four-wheel steering drive state, that is, to realize the conversion of virtual wheel information to the motion information of the four wheels.

[0083] Among them, in the optimization process, it is necessary to construct various cost functions corresponding to the motion constraint information. The obstacle information around the chassis is represented in the form of a grid map, and the initial trajectory point information is determined, that is, the robot can use the A* algorithm to perform a global search in the created grid map to obtain the initial solution information consisting of a point sequence from the starting point to the end point as the initial trajectory point information. Among them, each trajectory point in the initial trajectory point information contains position information in two-dimensional space. Therefore, the robot can solve the optimization problem based on the above prerequisites and the various cost functions corresponding to the above motion constraint information, thereby obtaining the smooth planning trajectory information of the four-wheel four-turn chassis from the starting point to the end point as the target trajectory point information.

[0084] Among them, the optimization of the chassis trajectory of the four-wheel four-turn can be expressed by the least squares problem. In order to better express the motion constraint information contained in the least squares problem, a cost function corresponding to the motion constraint information can be constructed. The cost function is specifically expressed as: τ (x,x r ,ε,S,n).

[0085] Among them, during the optimization, the maximum limit of the four-wheel driving speed is considered, and a cost function is added for each trajectory point; there is a maximum limit on the four-wheel driving acceleration, and the robot adds a cost function for each trajectory point; there is a maximum limit on the steering speed of the four wheels during the trajectory optimization process, so a cost function can be added for each trajectory point; during the optimization process, it is considered that the mismatch between the four-wheel steering and the speed will affect the stability of the chassis driving, so the robot can add a cost function for each trajectory point; the optimization process also includes the kinematic model constraints between two adjacent trajectory points, and the robot adds a cost function to the two adjacent trajectory points.

[0086] For trajectory restriction, during optimization, the robot should consider that the trajectory point cannot collide with obstacles and should be as close to the global path as possible, so the robot adds a cost function to each trajectory point; during optimization, the robot also needs to consider that the time and distance between two adjacent trajectory points should be as small as possible, so the cost function is added to each two adjacent trajectory points; in addition, the starting point and end point of the trajectory should also be considered in path planning, where the starting point and end point of the trajectory are constants and are not optimization variables, but are used as motion constraint information for trajectory restriction. Thus, the robot solves the above-constructed optimization problem and obtains the smooth planning trajectory information of the four-wheel four-turn chassis from the starting point to the end point as the target trajectory point information.

[0087] Through the above embodiments, the robot removes the restriction that each wheel in the chassis must rotate at the same angle and in opposite directions, constructs multiple virtual wheel information, and generates a kinematic model by combining the chassis state information and the virtual wheel information. The kinematic model and motion constraint information of the chassis are combined to perform motion planning for the chassis, thereby improving the operating efficiency of the chassis.

[0088] Moreover, the complete kinematic model can more comprehensively and accurately describe the robot's motion, including the steering angle of each wheel, the driving speed, and the overall linear and angular velocity of the chassis, thus improving the accuracy and stability of motion planning. Secondly, it has stronger smoothness and adaptability, and can dynamically adjust the motion strategy according to different working environments and task requirements without relying on preset mode switching, reducing the sudden changes and discontinuities that may occur during mode switching and the energy loss of the planned trajectory, so that the chassis can better cope with complex and changing environments and tasks. The motion planning method based on the complete kinematic model has obvious advantages in accuracy, smoothness, and adaptability, and is particularly suitable for application scenarios with high requirements for motion accuracy and stability.

[0089] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0090] Based on the same inventive concept, the embodiment of the present application also provides a motion planning device for implementing the motion planning method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more motion planning device embodiments provided below can refer to the limitations on the motion planning method above, and will not be repeated here.

[0091] In an exemplary embodiment, Figure 3 As shown, a motion planning device is provided, comprising: a first acquisition module 500, a second acquisition module 502 and a planning module 504, wherein:

[0092] The first acquisition module 500 is used to acquire the kinematic model corresponding to the chassis; the kinematic model is generated based on the chassis state information, the first virtual wheel information and the second virtual wheel information of the chassis; the speed and angle of the first virtual wheel information and the second virtual wheel information are not related; the first virtual wheel information corresponds to the first wheel group of the chassis, and the second virtual wheel information corresponds to the second wheel group of the chassis; the vertical line corresponding to the wheel of the first wheel group points to the first center of the circle when turning; the vertical line corresponding to the wheel of the second wheel group points to the second center of the circle when turning.

[0093] The second acquisition module 502 is used to acquire the motion constraint information of the chassis.

[0094] The planning module 504 is used to perform motion planning for the chassis according to the kinematic model and motion constraint information.

[0095] In one embodiment, the above-mentioned first acquisition module 500 is used to generate corresponding first virtual wheel information and second virtual wheel information according to the motion information of each wheel corresponding to the first wheel group and the second wheel group of the chassis respectively; obtain chassis status information of the chassis according to the corresponding position information and orientation information of the chassis; determine the corresponding kinematic model according to the chassis status information including the position information and orientation information, the first virtual wheel information and the second virtual wheel information.

[0096] In one embodiment, the second acquisition module 502 is used to generate first motion constraint information based on speed and steering restriction information corresponding to the first wheel group and the second wheel group of the chassis; and / or to generate second motion constraint information based on the kinematic model restriction corresponding to the chassis; and / or to generate third motion constraint information based on trajectory restriction information; and to obtain motion constraint information based on the first motion constraint information, the second motion constraint information and / or the third motion constraint information.

[0097] In one embodiment, the second acquisition module 502 is used to obtain initial trajectory point information; optimize the trajectory of the chassis according to the motion constraint information, the initial trajectory point information and the kinematic model to obtain target trajectory point information; and obtain the motion planning result corresponding to the chassis according to the target trajectory point information.

[0098] In one embodiment, the second acquisition module 502 is used to determine the first wheel motion information and the second wheel motion information corresponding to the initial trajectory point information of the first wheel group and the second wheel group of the chassis respectively according to the first virtual wheel information and the second virtual wheel information corresponding to the kinematic model; determine the optimized reference wheel motion information according to the first motion constraint information, the first wheel motion information and the second wheel motion information; determine the optimized reference chassis state information according to the second motion constraint information and the chassis state information corresponding to the kinematic model; determine the optimized reference trajectory point information according to the third motion constraint information and the initial trajectory point information; adjust the initial trajectory point information according to the reference wheel motion information, the reference chassis state information and the reference trajectory point information until the weighted sum of the reference wheel motion information, the reference chassis state information and the reference trajectory point information is minimized, thereby obtaining the target trajectory point information.

[0099] In one embodiment, the above-mentioned second acquisition module 502 is used to determine the first wheel rotation angle corresponding to the first wheel group and the second wheel rotation angle corresponding to the second wheel group according to the first virtual wheel rotation angle corresponding to the initial trajectory point information of the first virtual wheel information and the second virtual wheel rotation angle corresponding to the second virtual wheel information; determine the first wheel speed corresponding to the first wheel group according to the first virtual wheel rotation angle and the first wheel rotation angle; determine the second wheel speed corresponding to the second wheel group according to the second virtual wheel rotation angle and the second wheel rotation angle; determine the first wheel motion information of the first wheel group corresponding to the initial trajectory point information according to the first wheel rotation angle and the first wheel speed; determine the second wheel motion information of the second wheel group corresponding to the initial trajectory point information according to the second wheel rotation angle and the second wheel speed.

[0100] Each module in the above motion planning device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in the robot in the form of hardware, or can be stored in the memory in the robot in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0101] In an exemplary embodiment, a robot is provided. The robot may be an autonomous mobile robot, and its internal structure diagram may be as shown in FIG. Figure 4As shown. The robot includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the robot is used to provide computing and control capabilities. The memory of the robot includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the robot is used to exchange information between the processor and an external device. The communication interface of the robot is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a motion planning method is implemented. The display unit of the robot is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the robot can be a touch layer covering the display screen, or a button, trackball or touchpad set on the robot shell, or an external keyboard, touchpad or mouse.

[0102] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the robot to which the scheme of the present application is applied. The specific robot may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0103] In an exemplary embodiment, a robot is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above-mentioned motion planning method when executing the computer program.

[0104] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the motion planning method described above is implemented.

[0105] In one embodiment, a computer program product is provided, comprising a computer program, which implements the above-mentioned motion planning method when executed by a processor.

[0106] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0107] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0108] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0109] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A motion planning method, characterized in that: The method comprises: Acquire a kinematic model corresponding to a chassis; the kinematic model is generated based on chassis state information, first virtual wheel information, and second virtual wheel information of the chassis; the speeds and angles of the first virtual wheel information and the second virtual wheel information are not associated; the first virtual wheel information corresponds to a first wheel set of the chassis, and the second virtual wheel information corresponds to a second wheel set of the chassis; a vertical line corresponding to a wheel of the first wheel set points to a first center of a circle when turning; and a vertical line corresponding to a wheel of the second wheel set points to a second center of a circle when turning; Acquiring motion constraint information of the chassis; Performing motion planning for the chassis according to the kinematic model and the motion constraint information.

2. The method according to claim 1, characterized in that: The obtaining of the kinematic model corresponding to the chassis includes: Generate corresponding first virtual wheel information and second virtual wheel information respectively according to the motion information of each wheel corresponding to the first wheel group and the second wheel group of the chassis; Obtaining chassis state information of the chassis according to the position information and orientation information corresponding to the chassis; A corresponding kinematic model is determined according to the chassis state information including the position information and the orientation information, the first virtual wheel information, and the second virtual wheel information.

3. The method according to claim 1, characterized in that The obtaining of the motion constraint information of the chassis includes: generating first motion constraint information according to speed and steering restriction information corresponding to the first wheel group and the second wheel group of the chassis; and / or generating second motion constraint information according to kinematic model restrictions corresponding to the chassis; and / or generating third motion constraint information according to trajectory restriction information; The motion constraint information is obtained according to the first motion constraint information, the second motion constraint information and / or the third motion constraint information.

4. The method according to claim 1, characterized in that: The performing motion planning for the chassis according to the kinematic model and the motion constraint information comprises: Get the initial trajectory point information; According to the motion constraint information, the initial trajectory point information and the kinematic model, the trajectory of the chassis is optimized to obtain target trajectory point information; According to the target trajectory point information, a motion planning result corresponding to the chassis is obtained.

5. The method according to claim 4, characterized in that The motion constraint information includes first motion constraint information, second motion constraint information and third motion constraint information; the first motion constraint information represents speed and steering restrictions on the first wheel group and the second wheel group; the second motion constraint information represents kinematic model restrictions corresponding to the chassis; the third motion constraint information represents restrictions on the trajectory; The step of performing trajectory optimization on the chassis according to the motion constraint information, the initial trajectory point information and the kinematic model to obtain target trajectory point information includes: According to the first virtual wheel information and the second virtual wheel information corresponding to the kinematic model, respectively determine the first wheel motion information and the second wheel motion information of the first wheel group and the second wheel group of the chassis corresponding to the initial trajectory point information; determining optimized reference wheel motion information according to the first motion constraint information, the first wheel motion information, and the second wheel motion information; Determining optimized reference chassis state information according to the second motion constraint information and the chassis state information corresponding to the kinematic model; Determining optimized reference trajectory point information according to the third motion constraint information and the initial trajectory point information; The initial trajectory point information is adjusted according to the reference wheel motion information, the reference chassis state information and the reference trajectory point information until the weighted sum of the reference wheel motion information, the reference chassis state information and the reference trajectory point information is minimized, thereby obtaining the target trajectory point information.

6. The method according to claim 5, characterized in that The determining, according to the first virtual wheel information and the second virtual wheel information corresponding to the kinematic model, first wheel motion information and second wheel motion information of the first wheel group and the second wheel group of the chassis corresponding to the initial trajectory point information, respectively, comprises: Determine a first wheel rotation angle corresponding to the first wheel set and a second wheel rotation angle corresponding to the second wheel set according to a first virtual wheel rotation angle corresponding to the initial track point information of the first virtual wheel information and a second virtual wheel rotation angle corresponding to the second virtual wheel information; determining a first wheel speed corresponding to the first wheel set according to the first virtual wheel rotation angle and the first wheel rotation angle; determining a second wheel speed corresponding to the second wheel set according to the second virtual wheel rotation angle and the second wheel rotation angle; Determine first wheel motion information of the first wheel group corresponding to the initial track point information according to the first wheel rotation angle and the first wheel speed; Second wheel motion information of the second wheel group corresponding to the initial trajectory point information is determined according to the second wheel rotation angle and the second wheel speed.

7. A motion planning device, characterized in that: The device comprises: A first acquisition module is used to acquire a kinematic model corresponding to a chassis; the kinematic model is generated based on chassis state information, first virtual wheel information, and second virtual wheel information of the chassis; the speeds and angles of the first virtual wheel information and the second virtual wheel information are not associated; the first virtual wheel information corresponds to a first wheel set of the chassis, and the second virtual wheel information corresponds to a second wheel set of the chassis; a vertical line corresponding to the wheel of the first wheel set points to a first center of a circle when turning; and a vertical line corresponding to the wheel of the second wheel set points to a second center of a circle when turning; A second acquisition module, used to acquire motion constraint information of the chassis; A planning module is used to perform motion planning for the chassis according to the kinematic model and the motion constraint information.

8. A robot comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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