WO2021179409A1 - 一种非规则形状移动机器人的路径规划方法 - Google Patents
一种非规则形状移动机器人的路径规划方法 Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G—PHYSICS
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- G05D1/0088—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
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- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0217—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with energy consumption, time reduction or distance reduction criteria
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Definitions
- the invention belongs to the technical field of mobile robot path planning, and specifically relates to a path planning method for an irregular shape mobile robot.
- Path planning is a core issue in the research field of mobile robots. Its goal is to find a route that connects the current state and target state of the robot in the work environment, and avoid collisions with obstacles in the environment.
- the research methods of path planning include cell decomposition method, artificial potential field method and various intelligent algorithms, but these methods are mainly for single-point robots, that is, the robot is regarded as a single element with a regular shape, and the size of the robot is used to affect the working space.
- the network is divided, and path planning is performed on this basis. In the path planning algorithm, only the robot's 2-degree-of-freedom movement is considered. In reality, the shape of most robots is irregular.
- the path planning of an irregular shape robot not only needs to consider the movement of 2 degrees of freedom, but also its rotation.
- the robot is affected by obstacles. It can only move or rotate within a certain angle range. These are important constraints in path planning.
- the obstacles in the workspace may be smaller than the size of the robot.
- the current solution is to expand the shape of the obstacles, that is, as long as there are obstacles in the divided workspace grid, the entire grid is regarded as Obstacles, which causes the possible exercise path to be ignored, which is the so-called "path blocked" problem.
- the safety of the robot must also be considered to avoid “too close” or "too far” problems.
- the present invention proposes a path planning method for an irregularly shaped mobile robot.
- the present invention is a planning method that integrates biological neural networks and convolutional neural networks to control path safety to solve complex environments. , The path planning problem of irregular shape mobile robot.
- This method evenly divides the robot’s workspace into a topological network structure, and performs iterative calculations of bio-neural dynamics, and uses the stable network node function values after iterative calculation to describe the environment information of the workspace; forms a core matrix according to the shape characteristics of the mobile robot, The core matrix and the biological neural network in the workspace are convolved to obtain the movement state matrix and the rotation state matrix of the robot at the network node; using the initial node position and direction of the robot in the workspace as the initial conditions, iteratively search step by step, and build from the initial The node tree from the state to the target state, and finally the node tree is searched backward to obtain the planned path of the robot.
- both the robot and the target include x, y coordinates and three-dimensional state parameters of the direction ⁇ , taking into account the robot’s 2-degree-of-freedom movement and 1-degree-of-freedom rotational motion, and can control the planning by adjusting the safety factor in the convolution calculation
- the distance between the path and the obstacle adjusts the safety of the robot's path.
- the technical solution of the present invention is: a path planning method for an irregularly shaped mobile robot, including the following steps:
- Construct a workspace network divide the two-dimensional workspace of the robot evenly into a topological workspace network T, and perform biological neural dynamics iterative calculations on the workspace network T to form the biological neural network S of the workspace, and the description of the biological neural network S The environmental information of the entire workspace;
- Construct the shape core matrix of the robot construct the core matrix according to the shape characteristics of the mobile robot;
- Construct the movement state matrix and rotation state matrix of the robot perform convolution calculation on the core matrix and the biological neural network in the work space to obtain the movement state matrix and rotation state matrix of the robot at the network node;
- Build a tree of nodes Take the initial position and direction of the robot in the workspace as initial conditions, and iteratively search step by step to build a tree of nodes from the initial state to the target state;
- Generate planned path Search the node tree in reverse to obtain the planned path of the robot.
- the two-dimensional workspace of the robot is evenly divided into a topological workspace network T.
- Each node of the workspace network T includes free space and obstacles occupying two states.
- the node function value of the free space is set to 0, and obstacles occupy the node function value is set to 1, the working space of the network node T T I will be adjacent to the eight neighboring nodes, the network workspace T biological neural dynamics iteration, biological neural network constituting the working space S, the biological
- the function value of the node on the neural network S describes the environmental information of the location of the node, and the biological neural network S describes the environmental information of the entire workspace.
- a function value X i of the i-th neuron nodes of S i at time t, A, B, D is a non-negative constant parameter set, and there is A>B> D
- the step of constructing the shape core matrix of the robot is specifically as follows:
- Each element of the matrix includes two states: free space and robot occupancy.
- the element value of free space is set to 0, and the robot
- Extract h 2, ⁇ (x, y), ⁇ 1, 2, ..., 8, to form a 1 ⁇ 8 movement state matrix TSM (x, y), which describes the movement output state of the robot at the current node, that is, whether Able to enter the state of 8 adjacent nodes from the node (x, y);
- Extract h 1, ⁇ (x, y), ⁇ 1, 2, ..., 8, to form a 1 ⁇ 8 rotation state matrix RSM (x, y), which is composed of '0'or'-1', describing The rotation state of the robot at the node (x, y).
- the first layer convolution calculation method is:
- s is the set of subspaces occupied by the biological neural network S where the kernel matrix W ⁇ is located,'*' represents the convolution calculation, and b represents the safety factor, using the symbolic activation function f( ⁇ ), which is defined as
- the step of constructing the node tree is specifically:
- search step step step+1, determine whether the target information belongs to the information set P step , if the target information belongs to information Set P step , then stop searching. If the target information does not belong to the information set P step , update the node tree Tree that has been searched, and set the node information set P step to the current state, and perform the next search until the target information is searched until;
- step 1, 2, ... p, a tree of nodes from the initial state to the target state is formed.
- the nodes searched by p constitute the path node Path connecting the target position and the initial position, that is, the planned path.
- the determination of the determined path node is specifically: if there is a horizontally or vertically connected parent node in the parent node set, use it as the walking path node of the robot; The parent node, and use it as the robot walking path node.
- the actual space described by the nodes of the biological neural network S is equal to the actual space described by the nodes of the core matrix W ⁇ reflecting the shape of the robot.
- the robot placed in the biological neural network S occupies a core
- the core matrix W ⁇ reflecting the shape of the robot is placed in the biological neural network S, which can ensure that the node positions of W ⁇ and S correspond one-to-one.
- the beneficial effect of the present invention is: the present invention integrates biological neural network and convolutional neural network into path planning, and proposes a path planning method for an irregularly shaped mobile robot, which describes the robot's path by constructing a kernel matrix.
- the shape feature is used for the safe path planning of any irregularly shaped mobile robot in a complex workspace; because the size of the workspace division network is much smaller than the size of the robot, it can describe the characteristics of small obstacles well and solve the dense scattered obstacles The problem of "path blocked" in the working environment; only need to change the safety factor in the convolution calculation, the distance between the planned path and the obstacle can be adjusted, and the safety of the planned path can be easily controlled.
- FIG. 1 is a schematic diagram of the work space division of a robot according to an embodiment of the present invention.
- Fig. 2 is a schematic diagram of the connection of neuron nodes in a workspace according to an embodiment of the present invention.
- Fig. 3 is a schematic diagram of the construction of a biological neural network in a workspace according to an embodiment of the present invention.
- Fig. 4 is a schematic diagram of the construction of a shape core matrix of a robot according to an embodiment of the present invention.
- FIG. 5 is a schematic diagram of calculation of a convolutional neural network according to an embodiment of the present invention.
- FIG. 6 is a schematic diagram of a secondary convolution calculation kernel matrix according to an embodiment of the present invention.
- Fig. 7 is a flow chart of constructing a node tree according to an embodiment of the present invention.
- Fig. 8 is a flowchart of generating a planned route according to an embodiment of the present invention.
- Fig. 9 is a plan path diagram in a U-shaped obstacle working environment according to an embodiment of the present invention.
- Fig. 10 is a plan path diagram in a densely scattered obstacle work environment according to an embodiment of the present invention.
- a path planning method for an irregularly shaped mobile robot includes the following steps:
- Construct a workspace network divide the two-dimensional workspace of the robot evenly into a topological workspace network T, and perform biological neural dynamics iterative calculations on the workspace network T to form the biological neural network S of the workspace, and the description of the biological neural network S The environmental information of the entire workspace;
- Construct the shape core matrix of the robot construct the core matrix according to the shape characteristics of the mobile robot;
- Construct the movement state matrix and rotation state matrix of the robot perform convolution calculation on the core matrix and the biological neural network in the work space to obtain the movement state matrix and rotation state matrix of the robot at the network node;
- Build a tree of nodes Take the initial position and direction of the robot in the workspace as initial conditions, and iteratively search step by step to build a tree of nodes from the initial state to the target state;
- the steps of constructing a workspace network are specifically as follows:
- the two-dimensional workspace of the mobile robot is evenly divided into a topological workspace network T along the x and y directions.
- it is divided into a 100 ⁇ 100 network structure.
- Each node T of the workspace network T (x, y) includes two states: free space and obstacle occupancy.
- the free space is shown in the white area in Figure 1.
- the obstacle occupancy is shown in the figure
- the work space network T is subjected to the iterative calculation of biological neural dynamics to form the biological neural network S of the work space.
- the calculation formula is
- a function value X i of the i-th neuron nodes of S i at time t, A, B, D is a non-negative constant parameter set, and there is A>B> D
- I i is a biological neural the network node S i external stimulus
- I i E
- I i -E
- I j is the j-th adjacent neuron
- j 1, 2 ...8
- the connection weight of adjacent neurons w ij 1/d
- d is the distance between the i-th and j-th neuron nodes
- the function value of the node on the biological neural network S describes the environmental information of the location of the node, and the biological neural network S describes the environmental information of the entire workspace.
- the steps of constructing the shape core matrix of the robot are specifically as follows:
- the shape of the mobile robot can be arbitrary;
- the element W(m,n) includes two states: free space and robot occupation.
- the actual space described by the nodes of the constructed biological neural network S should be equal to the actual space described by the nodes of the robot shape core matrix W ⁇ .
- the robot placed in the biological neural network S occupies a sub-network equal in size to the core matrix W.
- the calculation structure of the convolutional neural network is shown in Figure 5.
- the 8 groups of core matrices W ⁇ reflecting the shape of the robot are convolved with the biological neural network S to obtain 8 one-layer convolutional networks H 1, ⁇ , convolution calculation
- the method is
- s is the set of subspaces occupied by the biological neural network S where the kernel matrix W ⁇ is located, S p and q are the subspaces of s,'*' represents the convolution calculation, b represents the safety factor, and the symbolic activation function f is used.
- Use M ⁇ to perform the secondary convolution calculation on the one-layer convolutional network H 1, ⁇ respectively, and obtain 8 two-layer convolutional networks H 2, ⁇ .
- the safety factor b is set to 0 in the convolution calculation.
- Extract h 2, ⁇ (x, y), ⁇ 1, 2, ..., 8, to form a 1 ⁇ 8 movement state matrix TSM (x, y), which is composed of '0'or'-1', describing The robot's mobile output state at the current node, that is, whether it can go from node (x, y) to the state of 8 adjacent nodes.
- TSM(x,y) [0,-1,-1,0,0,0,-1,0], which means that the robot can travel from the node (x,y) to the adjacent 5 nodes (x+ 1, y), (x+1, y-1), (x, y-1), (x-1, y-1) and (x-1, y+1).
- Extract h 1, ⁇ (x, y), ⁇ 1, 2, ..., 8, to form a 1 ⁇ 8 rotation state matrix RSM (x, y), which is composed of '0'or'-1', describing The rotation state of the robot at the node (x, y).
- the steps of constructing a node tree are specifically:
- the node information set P step includes the position and direction of the robot. Only when they are equal to the target position (x p , y p ) and the direction ⁇ p , the robot reaches the target.
- step 1, 2, ... p, a tree of nodes from the initial state to the target state is formed.
- the program After the search is completed, the program returns to the constructed search node tree Tree.
- search for the parent node connected to it to form a parent node set if there is a parent node connected horizontally or vertically in the parent node set, use it as the robot walking path node, if not, look for the parent node
- the parent node with a smaller angle of rotation in the current direction of the robot is used as the node of the robot's walking path.
- the program returns the path node Path that connects the target location and the initial location, that is, the planned path.
- the distance between the path and the obstacle can be controlled, and as b increases, the distance between the path and the obstacle The distance increases to ensure the safety of the robot.
- the planned path is the shortest path under a certain safety factor b.
- the arrow in Figure 9 is the forward direction.
- the path planning of the irregularly shaped mobile robot takes into account the robot’s 3-degree-of-freedom motion in the workspace, that is, 2-degree-of-freedom translation and 1-degree-of-freedom rotation.
- the robot needs to meet the requirements at the same time. Only when the target position (x p , y p ) and direction ⁇ p are reached can the path be generated.
- the planned path can be changed by setting the safety factor b.
- b3>b2>b1 it can be seen that as the safety factor increases, the planned path will also change accordingly.
- the planned path and obstacles The distance increases accordingly to ensure the safety of robot movement.
- the actual space described by the nodes of the constructed biological neural network S is equal to the actual space described by the nodes of the core matrix W ⁇ of the robot shape.
- the robot occupies a sub-network in S.
- W ⁇ has a one-to-one correspondence with the node positions of S.
- Figure 10 shows the results of the planned path in the working environment of densely scattered obstacles. Set the safety factor b, b2>b1, and you can also adjust the distance between the path and the obstacle.
- the present invention is used for safe path planning of any irregularly shaped mobile robot in a complex workspace.
- the workspace of the robot is evenly divided into a topological network structure, and the iterative calculation of biological neural dynamics is carried out, and iterative calculation is used to stabilize the
- the network node function value describes the environment information of the working space;
- the core matrix is formed according to the shape characteristics of the mobile robot, and the core matrix is convolved with the biological neural network of the working space to obtain the movement state matrix and the rotation state matrix of the robot at the network node;
- iteratively search step by step construct a node tree from the initial state to the target state, and finally search the node tree backwards to obtain the robot's planned path.
- the robot can be of any irregular shape, taking into account the robot's 3-degree-of-freedom motion in the plane space, and adding a safety factor factor in the convolution calculation, controlling the distance between the planned path and the obstacle, and solving the dense dispersion.
Abstract
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Claims (10)
- 一种非规则形状移动机器人的路径规划方法,其特征在于,包括以下步骤:构建工作空间网络:将机器人的二维工作空间均匀划分成拓扑的工作空间网络T,并对工作空间网络T进行生物神经动力学迭代计算,构成工作空间的生物神经网络S,生物神经网络S描述了整个工作空间的环境信息;构建机器人形状核矩阵:根据移动机器人的形状特征构成核矩阵;构建机器人的移动状态矩阵和转动状态矩阵:将核矩阵与工作空间的生物神经网络进行卷积计算,获得机器人在网络节点的移动状态矩阵和转动状态矩阵;构建节点树Tree:以机器人在工作空间初始位置和方向为初始条件,逐步迭代搜索,构建从初始状态到目标状态的节点树Tree;生成规划路径:反向搜索节点树Tree,获得机器人的规划路径。
- 根据权利要求1所述的非规则形状移动机器人的路径规划方法,其特征在于,所述构建工作空间网络的步骤具体为:将机器人的二维工作空间均匀划分成拓扑的工作空间网络T,工作空间网络T的每个节点包括自由空间和障碍物占据两个状态,自由空间的节点函数值设定为0,障碍物占据的节点函数值设定为1,工作空间网络T中的节点T i会与周边8个节点相邻,将工作空间网络T进行生物神经动力学迭代计算,构成工作空间的生物神经网络S,生物神经网络S上节点的函数值描述了该节点所在位置的环境信息,生物神经网络S则描述了整个工作空间的环境信息。
- 根据权利要求1所述的非规则形状移动机器人的路径规划方法,其特征在于,所述构建机器人形状核矩阵步骤具体为:将移动机器人所占据区域扩展成规则矩形或正方形区域,并进行均匀划分,构成核矩阵 W,矩阵的每个元素包括自由空间和机器人占据两个状态,自由空间的元素值设定为0,机器人占据的元素值设定为1,将机器人在工作空间内,间隔45°进行旋转,构建反映机器人8个方向的核矩阵W θ,θ=1,2,…,8。
- 根据权利要求4所述的非规则形状移动机器人的路径规划方法,其特征在于,所述构建机器人的移动状态矩阵和转动状态矩阵的步骤具体为:将8组反映机器人形状的核矩阵W θ分别与生物神经网络S进行卷积计算,得到8个一层卷积网络H 1,θ,H 1,θ网络中每个节点的输出h 1,θ(x,y)表示机器人是否能够以θ方向放置于工作空间节点(x,y),且h 1,θ(x,y)=0表示能够放置,h 1,θ(x,y)=-1表示不能够放置;构建8个3×3的核矩阵M θ,用M θ分别对一层卷积网络H 1,θ进行二次卷积计算,得到8个二层卷积网络H 2,θ,H 2,θ网络中每个节点的输出h 2,θ(x,y)表示机器人是否能够以θ方向从当前节点(x,y)行使进入相邻的节点,且h 2,θ(x,y)=0表示能够行驶进入,h 2,θ(x,y)=-1表示不能够行驶进入;提取h 2,θ(x,y),θ=1,2,…,8,构成一个1×8的移动状态矩阵TSM(x,y),描述了机器人在当前节点的移动输出状态,即是否能够从节点(x,y)行使进入相邻的8个节点的状态;提取h 1,θ(x,y),θ=1,2,…,8,构成一个1×8的转动状态矩阵RSM(x,y),它由‘0’或‘-1’组成,描述机器人在节点(x,y)的转动状态。
- 根据权利要求1所述的非规则形状移动机器人的路径规划方法,其特征在于,所述构建节点树的步骤具体为:以机器人的初始状态,搜索机器人能够进入的相邻神经元信息,构成一组节点信息集合P step,搜索步长step=step+1,判断目标信息是否属于信息集合P step,如果目标信息属于信息集合P step,则停止搜索,如果目标信息不属于信息集合P step,则更新已经搜索的节点树Tree,并将节点信息集合P step设定为当前状态,进行下一步搜索,直到搜索到目标信息为止;由每一步长的节点集合P step,step=1,2,…p,构成一个从初始状态到目标状态的节点树Tree。
- 根据权利要求1所述的非规则形状移动机器人的路径规划方法,其特征在于,所述生成规划路径具体步骤为:根据所述节点树Tree,输入机器人的初始位置(x 0,y 0)和方向θ 0,输入目标位置(x p,y p)和方向θ p;从目标信息节点出发,搜索与之相连的父节点,构成父节点集合,确定路径节点,更新路径节点Path,然后进行循环搜索,直到步长step=1,由每一步长step=1,2,…p搜索到的节点构成连接目标位置和初始位置的路径节点Path,即规划路径。
- 根据权利要求8所述的非规则形状移动机器人的路径规划方法,其特征在于,所述确定路径节点的确定具体为:如果父节点集合中有水平或垂直相连的父节点,则将其作为机器人行走路径节点,如果没有,则寻找与机器人当前方向转动角度较小的父节点,并将其作为机器人行走路径节点。
- 根据权利要求1所述的非规则形状移动机器人的路径规划方法,其特征在于,所述生物神经网络S的节点所描述的实际空间与反映机器人形状的核矩阵W θ的节点所描述的实际空间相等,机器人放置于生物神经网络S中所占据的是一个与核矩阵W大小相等的子网络,将反映机器人形状的核矩阵W θ放置于生物神经网络S中,能够保证W θ与S的节点位置一一对应。
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