Robot adaptive path planning algorithm in complex environment

Abstract

The invention relates to the technical field of robot path planning, and discloses a robot adaptive path planning algorithm in a complex environment, and the technical scheme is characterized in that a motion model is constructed; constructing a constraint model; constructing a performance index function to obtain an optimal or quasi-optimal path and avoid obstacles; constructing a penalty function to optimize the constraint condition; the maximum function is replaced with a smooth approximation function. By combining path optimization and kinematics constraint of the mobile robot, an optimal path can be obtained for the mobile robot model in an unknown environment with many irregular obstacles.

Description

Technical Field

[0001] The present invention relates to the technical field of robot path planning, and more specifically, to an adaptive path planning algorithm for a robot in a complex environment. Background Art

[0002] The planning process in existing technologies relies on complete environmental information. Data collection is space-consuming and expensive. Mobile robots need to build a global map model before planning. When unknown obstacles or moving obstacles appear in the motion space, the mobile robot cannot guarantee that it can safely track the planned path to the target.

[0003] Therefore, the present invention provides a robot adaptive path planning algorithm in a complex environment, which improves the above-mentioned technical problems. Summary of the invention

[0004] The disclosed embodiments aim to address the deficiencies of the prior art and provide a robot adaptive path planning algorithm in a complex environment. The present invention combines path optimization with the kinematic constraints of the mobile robot to obtain an optimal path for the mobile robot model in an unknown environment with many irregular obstacles.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions: A robot adaptive path planning algorithm in a complex environment, comprising the following steps:

[0006] S1, build motion model;

[0007] S2, build constraint model;

[0008] S3, constructing a performance indicator function to obtain an optimal or quasi-optimal path and avoid obstacles;

[0009] S4, construct a penalty function to optimize the constraints;

[0010] S5. Use a smooth approximate function instead of the maximum function.

[0011] As a preferred technical solution of the present invention, the process of constructing the motion model is: x(k+1)=F(x(k),u(k)), k=0, I, 2, ..., ∞;

[0012] Among them, k = {0, 1, 2, ..., ∞} is defined as the sampling point in this work, It is a mapping of system functions.

[0013] As a preferred technical solution of the present invention, the process of constructing the constraint model is as follows: for the path planning problem, the mobile robot will obtain the corresponding sampled state value x(k) at each time step k, and the initial state x0 and terminal state x0 of the mobile robot should be satisfied. f ; Due to the actual physical characteristics of the mobile robot, both the state x(k) and the control u(k) need to be constrained:

[0014] x min ≤x(k)≤ max ;

[0015] u min ≤u(k)≤u max ;

[0016] Among them, x min and u min is the minimum value of state and control, x max and u max are the maximum values ​​of state and control respectively.

[0017] As a preferred technical solution of the present invention, the distance between the mobile robot and the obstacle should be kept at a safe distance to prevent collision; in the dynamic path planning problem, the mobile robot moves in an unknown environment without prior knowledge and only calculates the distance to the obstacle within its perception range; therefore, the obstacle constraint is expressed as:

[0018] d m (k)≥β

[0019] Where m = 1, 2, ..., M k Indicates that there is M within the perception range k Obstacles ; β is the mobile robot and O m The predefined safety distance between m (k) is the distance between the mobile robot and O m Euclidean distance between exterior surfaces.

[0020] As a preferred technical solution of the present invention, a performance index function J(x(k)) is constructed to obtain an optimal or quasi-optimal path and avoid obstacles;

[0021]

[0022] in, is the set of allowed controls;

[0023] Optimal control u * (k) is:

[0024]

[0025] As a preferred technical solution of the present invention, the process of constructing a penalty function to optimize the constraint conditions is:

[0026]

[0027] Where J(x(k)) represents the minimization function under the constraint condition; γ represents the penalty term of the penalty function.

[0028] As a preferred technical solution of the present invention, a smooth approximate function is used instead of the maximum value function:

[0029]

[0030] Among them, the operator ρ ∈，γ，G is a first-order differentiable operator of the constraint set G(x(k), u(k), k), the symbol G=G(x(k), u(k), k) represents the constraint set, the parameter ε represents the smoothing coefficient, and the restriction is given by lim ε→0 +p ε，γ，G =max{0,G(x(k),u(k),k)}.

[0031] In summary, the present invention has the following beneficial effects: by combining path optimization with the kinematic constraints of the mobile robot, an optimal path can be obtained for the mobile robot model in an unknown environment with many irregular obstacles. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of a robot adaptive path planning algorithm in a complex environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present application is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements can also be made without departing from the concept of the present application. These all belong to the protection scope of the present application.

[0034] 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.

[0035] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. In addition, although the functional module division is performed in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a sequence different from the module division in the device or the flow chart. In addition, the words "first", "second", "third", etc. used herein do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0036] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used in this specification and in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0037] In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0038] Please refer to Figure 1 , Figure 1 The flowchart of a robot adaptive path planning algorithm in a complex environment described in an embodiment of the present disclosure is shown. The overall process mainly includes the following 5 steps:

[0039] Step 1: Build a motion model.

[0040] x(k+1)=F(x(k),u(k)),k=0,1,2,...,∞;

[0041] Among them, k = {0, 1, 2, ..., ∞} is defined as the sampling point in this work, It is a mapping of system functions.

[0042] Step 2: Build the constraint model.

[0043] For the path planning problem, the mobile robot will obtain the corresponding sampled state value x(k) at each time step k, and the initial state x0 and terminal state x0 of the mobile robot should be satisfied. f Due to the actual physical characteristics of mobile robots, both the state x(k) and the control u(k) need to be constrained:

[0044] x min ≤x(k)≤x max ;

[0045] u min ≤u(k)≤umax ;

[0046] Among them, x min and u min is the minimum value of state and control, x max and u max are the maximum values ​​of state and control respectively.

[0047] The distance between the mobile robot and obstacles should be kept at a safe distance to prevent collision. In the dynamic path planning problem, the mobile robot moves in an unknown environment without prior knowledge and only calculates the distance to obstacles within its perception range. Therefore, the obstacle constraint is expressed as:

[0048] d m (k)≥β

[0049] Where m = 1, 2, ..., M k Indicates that there is M within the perception range k Obstacles β is the mobile robot and O m The predefined safety distance between m (k) is the distance between the mobile robot and O m Euclidean distance between exterior surfaces.

[0050] Step 3: Construct a performance indicator function J(x(k)) to obtain the optimal or quasi-optimal path and avoid obstacles.

[0051]

[0052] in, is the set of allowed controls.

[0053] Optimal control u * (k) is:

[0054]

[0055] Step 4: Construct a penalty function to optimize the constraints:

[0056]

[0057] Where J(x(k)) represents the minimization function under the constraint condition; γ represents the penalty term of the penalty function.

[0058] Step 5: To maximize the non-smooth operator, use a smooth approximation function instead of the maximum function:

[0059]

[0060] Among them, the operator p ∈，γ，Gis a first-order differentiable operator of the constraint set G(x(k), u(k), k), the symbol G=G(x(k), u(k), k) represents the constraint set, the parameter ε represents the smoothing coefficient, and the restriction is given by lim ε→0 +p ε，γ，G =max{0,G(x(k),u(k),k)}.

[0061] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A robot adaptive path planning algorithm in complex environments, characterized in that: The method comprises the following steps: S1, build motion model; S2, build constraint model; S3, constructing a performance indicator function to obtain an optimal or quasi-optimal path and avoid obstacles; S4, construct a penalty function to optimize the constraints; S5. Use a smooth approximate function instead of the maximum function.

2. According to the robot adaptive path planning algorithm in complex environment as described in claim 1, it is characterized in that: The process of constructing the motion model is: x(k+1)=F(x(k),u(k)), k=0,1,2,...,∞; Among them, k = {0, 1, 2, ..., ∞} is defined as the sampling point in this work, It is a mapping of system functions.

3. The robot adaptive path planning algorithm in a complex environment according to claim 1, characterized in that: The process of building the constraint model is as follows: For the path planning problem, the mobile robot will obtain the corresponding sampled state value x(k) at each time step k, and the initial state x0 and terminal state x0 of the mobile robot should be satisfied. f ; Due to the actual physical characteristics of the mobile robot, both the state x(k) and the control u(k) need to be constrained: x min ≤x(k)≤x max ; in min ≤u(k)≤u max ; Among them, x min and u min is the minimum value of state and control, x max and u max are the maximum values ​​of state and control respectively.

4. The robot adaptive path planning algorithm in a complex environment according to claim 3, characterized in that: The distance between the mobile robot and obstacles should be kept at a safe distance to prevent collision; in the dynamic path planning problem, the mobile robot moves in an unknown environment without prior knowledge and only calculates the distance to obstacles within its perception range; therefore, the obstacle constraint is expressed as: d m (k)≥β Where m = 1, 2, ..., M k Indicates that there is M within the perception range k Obstacles β is the mobile robot and O m The predefined safety distance between m (k) is the distance between the mobile robot and O m Euclidean distance between exterior surfaces.

5. The robot adaptive path planning algorithm in a complex environment according to claim 1, characterized in that: Construct a performance indicator function J(x(k)) to obtain the optimal or quasi-optimal path and avoid obstacles; in, is the set of allowed controls; Optimal control u * (k) is:

6. The robot adaptive path planning algorithm in a complex environment according to claim 1, characterized in that: The process of constructing a penalty function to optimize the constraints is: Where J(x(k)) represents the minimization function under the constraint condition; γ represents the penalty term of the penalty function.

7. The robot adaptive path planning algorithm in a complex environment according to claim 1, characterized in that: Use a smooth approximation function instead of the maximum function: Among them, the operator p ∈，γ，G is a first-order differentiable operator of the constraint set G(x(k), u(k), k), the symbol G=G(x(k), u(k), k) represents the constraint set, the parameter ε represents the smoothing coefficient, and the restriction is given by lim ε→0 +p ε，γ，G =max{0,G(x(k),u(k),k)}.

Images