An autonomous navigation path planning method based on graph structure

Abstract

The application relates to the technical field of autonomous navigation path planning, and particularly discloses an autonomous navigation path planning method based on a graph structure, which comprises a preset method: S11, special point positions are pre-collected, and the angles required to reach the special point positions and the obstacle avoidance information in the rotating state are set; S12, the special point positions are connected to set the obstacle avoidance information and speed information of each edge; S13, the running state of the edge is tested, and the generated graph structure is adjusted accordingly; and a running method: S21, points in a point list are searched in the graph structure, and it is determined that all the points in the graph structure are contained and have connectivity; S22, a shortest path algorithm is performed on each two points, and a supplementary point is added, so that a complete path is obtained; and S23, the corresponding edge is searched by using the point list after the supplement, and the obstacle avoidance information and speed information in the edge are read to plan and generate a complete queue. The method can provide more suitable and safe planning, the path planning is performed by using the graph structure, and different obstacle avoidance information on different paths can be provided for robots.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of autonomous navigation path planning. BACKGROUND

[0002] The line-patrolling navigation technology of a robot is a technology for making the robot automatically patrol along a predetermined route and perform a task. A conventional line-patrolling robot can construct an accurate environment map by analyzing and processing sensing data, describe the geometric and semantic information of the surrounding environment, including the positions of obstacles, the height of the terrain, the structure of a room, etc., and after obtaining the accurate environment map, the robot needs to plan a safe and efficient path from the starting point to the target point. After receiving the line-patrolling node queue (only with pose information) issued by other ROS nodes, the autonomous navigation system of the robot generates a path using the specified acceleration and maximum speed for line-patrolling path planning, and uses the obstacle avoidance node for obstacle avoidance, so as to realize line-patrolling navigation.

[0003] The conventional line-patrolling navigation planning described above cannot switch the obstacle avoidance under a specific path, cannot plan the acceleration and maximum speed under different paths, cannot perform complex curve planning when the robot needs to pass a curve, and when the input point position is an error point position, the path planning is also performed. SUMMARY

[0004] The present application aims to provide a graph structure-based autonomous navigation path planning method capable of providing more appropriate and safe planning, which plans a path based on a graph structure and can provide different obstacle avoidance information on different paths for a robot.

[0005] To achieve the above-mentioned purpose, the technical solution of the present application is: a graph structure-based autonomous navigation path planning method, characterized in that the path planning method comprises a pre-setting method step and a running method step,

[0006] The pre-setting method step comprises the following:

[0007] S11, special point positions on the robot line-patrolling path are pre-collected, and the angle required to reach the special point positions and the obstacle avoidance information at the time of rotation are set in the system;

[0008] S12, the special point positions are connected in the system to obtain edges, and the obstacle avoidance information and speed information of each edge are set;

[0009] S13, the running conditions of each edge in step S12 are tested, and corresponding adjustments are made to generate a complete graph structure;

[0010] The running method step comprises the following:

[0011] S21, search all points in the received point list in the graph structure, determine that all points in the point list are contained in the graph structure, and all points in the point list have connectivity, and the point list is denoted as A{A1, A2, … A n};

[0012] S22, perform a shortest path algorithm on each two points in the received point list, and supplement points for the two points which are not adjacent, to ensure that all points in the point list are adjacent in the graph structure, obtain n-1 paths, and then superimpose the n-1 paths into a complete path denoted as B{B1, B2, … B m};

[0013] S23, find corresponding edges using the supplemented point list, read obstacle avoidance information and speed information in the edges, and plan and generate a complete queue.

[0014] The specific method steps of step S23 are as follows:

[0015] S31, first traverse the entire edge set of path B, the entire edge set of path B is denoted as En, and obtain a required edge set denoted as E{E1, E2, … E m-1},

[0016] S32, read the edges E i of B i and B i+1 , calculate an acceleration required length denoted as L1 and a deceleration required length denoted as L3, wherein i represents the i-th edge, and obtain a uniform speed distance denoted as L2 according to a weight of the edge minus L1 and L3, and the expression is as follows:

[0017]

[0018] S33, generate a point list according to the obtained acceleration and deceleration lengths and the uniform speed movement length, and the expression is as follows:

[0019]

[0020] Wherein a represents the acceleration of the edge, d represents the distance between each two planning points, Vmax represents the maximum speed, V1 and V2 respectively represent the planning speeds at two points, W represents the length of the edge, dx and dy respectively represent the numerical differences of the poses x and y between the two vertices corresponding to the edge;

[0021] According to the above method, the planning of all points in the corresponding edge is generated, the obstacle avoidance parameters are read, the complete queue is generated by splicing the queues generated by each different edge, and then sent to the control node to control the robot to move.

[0022] In the specific method steps of step S23, when the edge is a curve, the point list contains control points generated by a Bezier curve, and the formula for generating the queue is as follows:

[0023]

[0024] x c and y c represent the coordinates of the control points, and t is the scale parameter of each generated point, when the robot performs curve motion, the robot is always kept uniform motion, and the stability of the robot motion is ensured.

[0025] The special point positions in the step S1 include robot work points and turning points, and / or the speed information includes maximum speed and acceleration.

[0026] In the step S2, if the edge is a curve, the Bezier curve setting is used, the control points are adjusted, and the curvature and offset of the curve are changed.

[0027] By adopting the above technical scheme, the method of the present application has the following advantages: the method of the present application stores some special point positions in the form of vertices in the graph structure, connects the adjacent points, and stores the attributes of the points in the edges, wherein the attributes of the points include the pose, the speed, and the obstacle avoidance information, and the attributes of the edges include the acceleration, the maximum speed, the weight, and the obstacle avoidance information. The method can set different obstacle avoidance for any path, can set the obstacle avoidance state according to different points, and can set different speed and acceleration in some special path. In actual application scenarios, the robot often encounters various complex working environments. The method provides different obstacle avoidance information on different paths for the robot, thereby providing suitable and safe planning, and achieves the purpose of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a principle block diagram of a graph structure-based autonomous navigation path planning method involved in the embodiment. DETAILED DESCRIPTION

[0029] In order to further explain the technical scheme of the present application, the present application will be described in detail below through specific embodiments.

[0030] The graph structure-based autonomous navigation path planning method disclosed in the embodiment includes a pre-setting method step and a running method step.

[0031] The pre-setting method step includes the following steps:

[0032] S11, in order to make the robot move according to the planning, it is necessary to pre-collect special point positions on the robot patrol path, the special point positions include robot work points and turning points, etc., which can be manually collected and set, and the angle required to reach the special point position and the obstacle avoidance information when rotating are set in the system,

[0033] S12, connecting the special points in the system to obtain edges, and setting the obstacle avoidance information and speed information (including maximum speed, acceleration, etc.) of each edge. In this step, if the edge is a curve, a Bezier curve is set by adjusting the control points to change the curvature and offset of the curve.

[0034] S13, testing the running condition of each edge in step S12 and making corresponding adjustments to generate a complete graph structure.

[0035] The running method steps include the following:

[0036] S21, searching for all points in the point list received by the system in the graph structure, determining that all points in the point list are included in the graph structure, and all points in the point list have connectivity, and the point list is denoted as A{A1, A2, … A n};

[0037] S22, performing a shortest path algorithm on each two points in the received point list, and supplementing points for the two points that are not adjacent to ensure that all points in the point list are adjacent in the graph structure, obtaining n-1 paths and superimposing them into a complete path denoted as B{B1, B2, … B m};

[0038] S23, using the supplemented point list to find the corresponding edges, reading the obstacle avoidance information and speed information in the edges to plan and generate a complete queue.

[0039] The specific method steps of step S23 are as follows:

[0040] S31, first traversing the entire edge set of path B, denoted as En, obtaining the required edge set denoted as E{E1, E2, … E m-1},

[0041] S32, reading the edges E i of B i and B i+1 , calculating the required length of acceleration length denoted as L1 and the required length of deceleration denoted as L3, where i represents the i-th, and according to the weight of the edge, subtracting L1 and L3 to obtain the uniform speed distance denoted as L2, the expression is as follows:

[0042]

[0043] S33, generating the point list according to the obtained acceleration and deceleration length and uniform speed length, the expression is as follows:

[0044]

[0045] Wherein a represents the acceleration of the edge, d represents the distance between each two planning points, Vmax represents the maximum speed, V1 and V2 respectively represent the planning speed at two points, W represents the length of the edge, dx and dy respectively represent the numerical difference of the pose x and y between the two vertices corresponding to the edge;

[0046] According to the above method, the planning of all points in the corresponding edge is generated, the obstacle avoidance parameters are read, the complete queue is generated by splicing the queues generated by each different edge, and then the complete queue is sent to the control node to control the robot to move.

[0047] In the specific method steps of the step S23, when the edge is a curve, the point list contains the control points generated by the Bezier curve, and the formula for generating the queue is as follows:

[0048]

[0049] x c and y c represent the coordinates of the control points, t is the proportional parameter of each generated point, and other letter symbols represent the same letter symbols as in the above expression, when the robot performs curve motion, the robot is always kept at a constant speed, and the stability of the robot motion is ensured.

[0050] The method of the application performs path planning of the robot in a graph structure, sets straight-line obstacle avoidance through the edge attribute in the graph, sets rotary obstacle avoidance through the attribute of the point, and sets the acceleration and maximum speed of the robot motion on the edge. The method can set different obstacle avoidance for any path, can set the obstacle avoidance state according to different points, and can set different speeds and accelerations on some special sections. In actual application scenarios, the robot often encounters various complex working environments. The method can provide different obstacle avoidance information on different paths for the robot, thereby providing suitable and safe planning,

[0051] The method of the application proposes the concept of a road network, abstracts the existing points and paths, values the abstracted points, and enables the robot to move by reading the data in the graph structure, so as to achieve planned and stable operation. The method solves the problems that the traditional line navigation planning cannot switch obstacle avoidance under a specific path, cannot plan the acceleration and maximum speed under different paths, and cannot perform complex curve planning when the robot needs to pass a curve, and solves the problem that path planning is performed when the input point is an error point.

[0052] The above embodiments and drawings do not limit the product form and style of the application, and any appropriate changes or modifications made by those skilled in the art to the above embodiments and drawings should be considered as not departing from the patent scope of the application.

Claims

1. A graph-based autonomous navigation path planning method, characterized in that, Path planning methods include pre-set method steps and execution method steps. The pre-setting method includes the following steps: S11. Pre-collect special points on the robot's line-following path, and set the angle required to reach the special points and obstacle avoidance information during rotation in the system. S12. Connect the special points in the system to obtain the edges, and set the obstacle avoidance information and speed information for each edge; S13. Test the operation of each edge in step S12 and make corresponding adjustments to generate a complete graph structure. The operation method includes the following steps: S21. Search the graph structure for all points in the point list received by the system, and determine that the graph structure contains all points in the point list, and that all points in the point list are connected. The point list is denoted as A{A1, A2, ... A}. n }; S22. Perform a shortest path algorithm on every two points in the received point list, and add points to the non-adjacent pairs to ensure that all points in the point list are adjacent in the graph structure. After obtaining n-1 paths, combine them into a complete path denoted as B{B1, B2, ... B}. m }; S23. Use the supplemented point list to find the corresponding edge, read the obstacle avoidance information and speed information in the edge to plan and generate a complete queue; The specific steps of step S23 are as follows: S31. First, traverse the entire edge set of path B, denoted as En. Then, obtain the required edge set denoted as E{E1, E2, ... E}. m-1 }, S32, Read B i and B i+1 The edge E i The length required for acceleration is calculated and denoted as L1, and the length required for deceleration is denoted as L3, where i represents the i-th edge. The constant speed distance is obtained by subtracting L1 and L3 from the edge weights and denoted as L2, as shown in the following expression: ， S33. Generate a point sequence based on the obtained acceleration / deceleration length and uniform motion length, as shown in the following expression: ， Where a represents the acceleration of the edge, d represents the distance between any two planning points, Vmax represents the maximum velocity, V1 and V2 represent the planning velocities at the two points respectively, W represents the length of the edge, and dx and dy represent the differences in pose x and y between the two vertices corresponding to the edge respectively. The plan for all points on the corresponding edge is generated according to the above method. Then, the obstacle avoidance parameters are read. The queues generated by each different edge are spliced ​​together to form a complete queue, which is then sent to the control node to control the robot to move.

2. The autonomous navigation path planning method based on graph structure as described in claim 1, characterized in that, In the specific method steps of step S23, when the edge is a curve, the point list includes control points for generating the Bézier curve, and the formula for generating the queue is as follows: ， x c and y c The coordinates of the control point are represented by t, which is a scaling parameter for each generated point. When the robot performs curvilinear motion, it is kept at a constant speed to ensure the stability of the robot's motion.

3. The autonomous navigation path planning method based on graph structure as described in claim 1 or 2, characterized in that, The special points in step S1 include the robot's working point and turning point, and / or the speed information includes the maximum speed and acceleration.

4. The autonomous navigation path planning method based on graph structure as described in claim 1 or 2, characterized in that, In step S2, if the edge is a curve, then a Bézier curve is used to set the curve, and the curvature and offset of the curve are changed by adjusting the control points.

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