Planetary vehicle long-distance path planning method and system in time-varying environment

Abstract

The invention discloses a planetary vehicle long-distance path planning method and system in a time-varying environment, and the method comprises the steps: constructing a time-varying three-dimensional space-time cost map based on a digital elevation map and sun position information; in the three-dimensional space-time cost graph, a starting point and an ending point are taken as targets, nodes are expanded in the space dimension and the time dimension at the same time, node expansion is carried out in a one-way progressive increase mode of the time dimension, and path search is carried out by calculating the moving cost and the time cost between the nodes based on the three-dimensional space-time cost graph and following the principle of the minimum cost. An optimal path is obtained; key path points are extracted from the optimal path, a smooth movement track meeting the kinematics constraint of the planet vehicle is generated based on the key path points, and a navigation point sequence used for controlling movement of the planet vehicle is generated according to the smooth movement track. According to the invention, the problem that long-distance path planning is difficult to adapt to the dramatic change of illumination in the time-varying environment is solved, and the driving safety and global path planning capability of the planet vehicle in the complex time-varying environment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of star planet car path planning, and in particular to a star planet car long-distance path planning method and system in a time-varying environment. BACKGROUND

[0002] The star planet car long-distance path planning method refers to a space-time movement path planning of a star planet car in a time-varying factor change such as a terrain and an extraterrestrial celestial body surface illumination in a long-time long-distance movement process. For example, at the north and south poles of the moon, the solar elevation angle is low, and the terrain ups and downs cause the illumination to change dramatically. In the past, short-time short-distance movement planning was considered, and the presence or absence of sunlight was almost unchanged in several days, and only the terrain obstacles of the star planet car movement needed to be considered. However, in the long-distance planning in the time-varying illumination environment, the time dimension is more than ten days, the mileage is more than one hundred meters, and the illumination condition changes dramatically. The time-varying illumination of the path planning becomes a factor that must be considered, that is, a space-time path planning method considering the change of geographical position and time.

[0003] The long-distance path planning is a key link for the star planet car to perform a large-scale scientific exploration task. The unique time-varying environment of the extraterrestrial celestial body surface (such as the moon poles, the low solar elevation angle, the dynamic change of the solar azimuth angle with time, the superposition of the terrain ups and downs leading to the dramatic change of the shadow area position and range, and the mutation of the state of part of the passable area) makes the star planet car long-distance travel face more complex safety challenges and path feasibility risks. At present, the mainstream star planet car path planning algorithm mainly constructs a path search model based on the assumption of a static environment, is applied to short-distance and low-latitude areas, and the illumination condition does not change obviously. The influence of the time-varying environment information on the path cost is not considered in the planning process. In addition, although part of the algorithm considers the dynamic nature of the environment, it only focuses on the real-time adjustment of the short-distance local path to the dynamic obstacle, and cannot complete the long-distance global top path planning. It is difficult to meet the demand of the star planet car large-scale scientific exploration task for path planning.

[0004] With the expansion of the moon exploration task to more complex terrain and a larger area, the extraterrestrial celestial body exploration vehicle needs to complete more target scientific exploration tasks in a larger exploration area. Higher requirements are put forward for the time-varying environment adaptability, global optimization and task adaptability of the long-distance path planning. The traditional path planning method only considering the terrain factor is difficult to adapt to the time-varying environment and long-distance travel scene, and is difficult to meet the engineering actual demand.

[0005] Therefore, it is urgent to provide a technical solution to solve the above problems. SUMMARY

[0006] To solve the above technical problems, the present application provides a star planet car long-distance path planning method and system in a time-varying environment.

[0007] In a first aspect, the present application provides a long-distance path planning method for a star ball vehicle in a time-varying environment, and the technical scheme of the method is as follows: Based on the digital elevation map and the sun position information, the illumination shadow area distribution of a plurality of time slices in a planning period is generated. Based on the digital elevation map, a static movement cost representing the terrain passability is generated. The illumination shadow area distribution of each time slice is fused with the static movement cost to form a three-dimensional space-time cost map that dynamically changes over time. In the three-dimensional space-time cost map, the start point and the end point containing spatial coordinates and time information are taken as the start and end targets, the nodes are expanded in the spatial dimension and the time dimension at the same time, and the time dimension is unidirectionally increased, the movement cost between the nodes is calculated based on the three-dimensional space-time cost map, and the path search is performed in accordance with the principle of minimum cost, so as to obtain an optimal path composed of a series of nodes containing spatial coordinates and time information. Key path points are extracted from the optimal path, a smooth movement trajectory satisfying the kinematic constraint of the star ball vehicle is generated based on the key path points, and a navigation point sequence for controlling the movement of the star ball vehicle is generated according to the smooth movement trajectory.

[0008] The long-distance path planning method for a star ball vehicle in a time-varying environment provided by the present application has the following beneficial effects: The method of the present application solves the problem that long-distance path planning is difficult to adapt to the dramatic changes of the time-varying environment light by constructing a three-dimensional space-time cost map that fuses the terrain and the dynamic light, expanding the search nodes in the spatial and time dimensions at the same time for global path optimization, and improves the driving safety and global path planning ability of the star ball vehicle in a complex time-varying environment.

[0009] In a second aspect, the present application provides a long-distance path planning system for a star ball vehicle in a time-varying environment, and the technical scheme of the system is as follows: The first generation module is used for generating the illumination shadow area distribution of a plurality of time slices in a planning period based on the digital elevation map and the sun position information. The second generation module is used for generating a static movement cost representing the terrain passability based on the digital elevation map. The fusion generation module is used for fusing the illumination shadow area distribution of each time slice with the static movement cost to form a three-dimensional space-time cost map that dynamically changes over time. The path searching module is configured to perform node expansion in the three-dimensional space-time cost map, with the start point and the end point containing space coordinates and time information as start and end targets, expand nodes in the space dimension and the time dimension simultaneously, and increase the time dimension unidirectionally, perform path searching based on the three-dimensional space-time cost map, and obtain an optimal path composed of a series of nodes containing space coordinates and time information, in which the principle of minimum cost is followed. The path planning module is configured to extract key path points from the optimal path, generate a smooth movement trajectory satisfying the kinematic constraint of the star globe vehicle based on the key path points, and generate a navigation point sequence for controlling the movement of the star globe vehicle according to the smooth movement trajectory.

[0010] The star globe vehicle long-distance path planning system in a time-varying environment has the following beneficial effects: The system of the present application solves the problem that long-distance path planning is difficult to adapt to the dramatic changes of the time-varying environment, and improves the driving safety and global path planning capability of the star globe vehicle in a complex time-varying environment.

[0011] In a third aspect, the technical scheme of an electronic device of the present application is as follows: The processor executes the program to realize the steps of the long-distance path planning method for a star globe vehicle in a time-varying environment.

[0012] In a fourth aspect, the technical scheme of a computer readable storage medium provided by the present application is as follows: The computer readable storage medium stores instructions, and when the computer readable storage medium reads the instructions, the computer readable storage medium executes the steps of the long-distance path planning method for a star globe vehicle in a time-varying environment.

[0013] The above description is only a summary of the technical scheme of the present application, in order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings are only used to illustrate the embodiments and are not considered as limiting the present application. Moreover, the same reference signs are used to represent the same parts throughout the drawings. In the drawings: Figure 1 The flowchart of an embodiment of the long-distance path planning method for a star globe vehicle in a time-varying environment of the present application is shown in the figure. Figure 2 A construction diagram of an occlusion angle database; Figure 3 A light shadow diagram; Figure 4 A slope diagram; Figure 5 A slope cost diagram; Figure 6 A roughness diagram; Figure 7 A roughness cost diagram; Figure 8 A three-dimensional space-time cost diagram; Figure 9 A structural diagram of an embodiment of a star car long-distance path planning system in a time-varying environment according to the present application; Figure 10 A structural diagram of an embodiment of an electronic device according to the present application. DETAILED DESCRIPTION

[0015] Exemplary embodiments of the present application will be described in detail below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein.

[0016] Figure 1 A flow diagram of an embodiment of a star car long-distance path planning method in a time-varying environment according to the present application is shown, which can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the star car long-distance path planning method in a time-varying environment by calling computer-readable instructions stored in the memory through the processor. As shown in Figure 1 The steps include: S1, based on a digital elevation map and solar position information, generating a light shadow area distribution of a plurality of time slices in a planning period.

[0017] The digital elevation map refers to a digital map that records the elevation of each position on the surface of a celestial body in a regular grid form. For example, a digital elevation map covering the S region of the lunar south pole with a resolution of 1 m / pixel, in which each grid cell stores the elevation value of the terrain at that location. The solar position information refers to celestial motion data describing the azimuth and altitude angle of the sun seen from an observation point on the surface of a celestial body at a specific time. For example, by calling the orbital ephemeris calculation library B, the azimuth and altitude angle data of the sun at the subnight time at a predetermined landing point in the lunar south pole within a planning period of 10 consecutive days from the start time of the mission are obtained. The planning period refers to a continuous time period covered by path planning. For example, the lunar rover plans to perform a moving task with a total duration of 10 days from the start time T0, and the continuous time period of 10 days is the planning period. The time slice refers to an independent time segment obtained by discretizing a continuous planning period at a fixed interval. For example, the 10-day planning period is discretized at an interval of 1 day to obtain 10 time slices, corresponding to the 1st day, the 2nd day, and so on to the 10th day. The distribution of light and shadow regions refers to the spatial distribution of positions in a target region that are determined to be illuminated by sunlight or shaded by terrain at a specific time slice. For example, for the time slice "Day 3", a binary distribution map is generated based on the solar position and digital elevation map at that time, in which each cell is labeled as "light area" or "shadow area".

[0018] S2, generating a static movement cost representing terrain passability based on the digital elevation map.

[0019] The terrain passability refers to the inherent property of terrain features on the difficulty of a lunar rover moving through. For example, an area with a gentle slope and a flat surface has high passability, while an area with large rocks on a steep slope has low passability or is not passable. The static movement cost refers to a value determined only by the location terrain features and not changing over time within the planning period, which is used to quantify the difficulty or energy consumption of the lunar rover moving at that location. For example, by calculating the digital elevation map, a cost matrix with the same size as the map is obtained, in which each passable cell stores a movement cost value between 0 and 1.

[0020] S3, fusing the light and shadow region distribution of each time slice with the static movement cost to form a three-dimensional spatiotemporal cost map that changes dynamically over time.

[0021] The three-dimensional space-time cost map refers to a three-dimensional data model formed by adding a time dimension to a two-dimensional space grid, wherein each (x, y, t) unit stores a comprehensive movement cost or a traffic state at a position (x, y) at time t. For example, a data cube with dimensions of 1000 (rows) * 1000 (columns) * 10 (time layers).

[0022] S4. In the three-dimensional space-time cost map, the node expansion is performed in a manner that the start point and the end point contain space coordinates and time information, the nodes are expanded in the spatial dimension and the time dimension at the same time, the time dimension is unidirectionally increased, the movement cost between nodes is calculated based on the three-dimensional space-time cost map, and the path search is performed in a manner that the principle of minimum cost is followed. An optimal path composed of a series of nodes containing space coordinates and time information is obtained.

[0023] The space coordinates refer to a pair of numerical values used to uniquely identify a position on a two-dimensional plane. For example, in a lunar map using UTM projection, the space coordinates of a point can be represented as (east distance 500123.4 m, north distance 3456789.0 m). The time information refers to a specific time or time point associated with a path point. For example, an intermediate point obtained by path search can contain time information “Day 3, 14:30”. The start point refers to the spatial position and time when the path planning starts. For example, the start point can be defined as the space coordinates (east distance 500000 m, north distance 3456000 m) and the time “Day 1, 08:00”. The end point refers to the spatial position and time when the path planning ends. For example, the end point can be defined as the space coordinates (east distance 502000 m, north distance 3458000 m) and the time “Day 10, 18:00”. The start and end targets refer to the start state and the end state that need to be connected in a path search problem. For example, the path search algorithm needs to find a feasible trajectory from the “start point (position P1, Day 1 08:00)” to the “end point (position P2, Day 10 18:00)”.

[0024] wherein, spatial dimension refers to: in the path search state space, two directions (usually X-axis and Y-axis) used to describe the change of position; for example, in a three-dimensional space-time cost map, from a certain node (x, y, t), it can be expanded to adjacent spatial nodes such as (x+1, y, t), (x, y+1, t), etc. Time dimension refers to: in the path search state space, a single direction used to describe the change of time; for example, in a three-dimensional space-time cost map, from a certain node (x, y, t), it can be expanded to a node in the next time dimension (x, y, t+Δt). Single-increasing refers to: in path search, the expansion of the time dimension is only allowed to move forward in the future direction, and is not allowed to backtrack to the past time; for example, from the node representing "day 2", it can only be expanded to the node representing "day 3" and later. Node expansion refers to: in the path search algorithm, starting from the current state (node) under consideration, all possible next states are explored; for example, starting from node (x, y, t), the algorithm simultaneously explores the same time nodes of its spatial 8-neighborhood, as well as the next time slice nodes of the same position. Movement cost refers to: the comprehensive cost value quantified by the rover moving from one state (node) to the next state (node); for example, the movement cost from node A to node B may include the terrain traversal cost and the time waiting cost. The minimum cost principle refers to: the optimization criterion followed by the path search algorithm, that is, in all feasible paths, the path with the minimum total movement cost cumulative sum from the starting point to the end point is selected; for example, when using A* search algorithm, the path with the minimum cumulative cost g(n) to the end point is aimed to be found. Path search refers to: in a given state space and constraint conditions, a feasible path from the starting point to the end point is found by using a specific algorithm; for example, in a three-dimensional space-time cost map state space, an improved A* algorithm is used to find the optimal path with the minimum cost from the starting point to the end point. Optimal path refers to: according to the preset cost model and optimization principle, the space-time path with the minimum total movement cost is found by path search; for example, a path composed of a sequence of space-time nodes is searched, and the total cost is the minimum value among all feasible paths.

[0025] S5, extracting key path points from the optimal path, generating a smooth movement trajectory satisfying the rover kinematic constraint based on the key path points, and generating a navigation point sequence for controlling the rover movement according to the smooth movement trajectory.

[0026] The key path point refers to a few important points that have a decisive effect on the path geometry and are filtered out from the discrete optimal path node sequence. For example, after the path containing multiple points is processed using the Douglas-Peucker algorithm, the connection of the remaining key points can approximately represent the main direction of the original path. The planetary vehicle kinematics constraint refers to a restriction condition that must be considered when planning a smooth moving trajectory and is determined by the physical structure and motion characteristics of the planetary vehicle. For example, the curvature radius of the planned trajectory cannot be smaller than the minimum turning radius of the planetary vehicle. The smooth moving trajectory refers to a spatial curve described by a mathematical function, which is continuous and has a gentle change in curvature and meets the kinematics constraint of the planetary vehicle. For example, a smooth moving trajectory is generated by fitting key path points with a cubic spline curve, which is infinitely differentiable in a two-dimensional plane. The navigation point sequence refers to a series of discrete position points sampled from the smooth moving trajectory according to a certain rule and used to directly control the movement of the planetary vehicle. For example, a target point is sampled from the smooth moving trajectory every 10 s according to the control period of the planetary vehicle, and these target points are arranged in time sequence to form a navigation point sequence.

[0027] The technical scheme of the embodiment solves the problem that a long-distance path planning is difficult to adapt to a dramatic change of time-varying environment light, and improves the driving safety and global path planning capability of the planetary vehicle in a complex time-varying environment.

[0028] In an optional manner, S1 specifically comprises: Based on the digital elevation map, a terrain occlusion angle database is constructed, wherein the terrain occlusion angle database is used to record the terrain occlusion angle of each position in different directions in the digital elevation map.

[0029] The terrain occlusion angle database refers to a data set of the maximum terrain occlusion elevation angle of each position in each direction in the digital elevation map, which is calculated and stored in advance. For example, for a DEM of a target area of 1000x1000, the maximum elevation angle of the line of sight direction of each unit in each direction is calculated at an azimuth angle interval of 0.5°, and a multi-layer data is formed to constitute the database. The terrain occlusion angle refers to the maximum elevation angle formed by the terrain contour line when observing from a specific azimuth angle direction. For example, at a point, the highest point of a distant ridge is 5° from the horizon when looking in the east direction (azimuth angle 90°), and the terrain occlusion angle of the east direction of the point is 5°.

[0030] According to the sun position information, the sun azimuth angle and the sun elevation angle corresponding to each time slice in the planning period are calculated.

[0031] The solar azimuth angle refers to the angle of the horizontal direction in which the sun is located as viewed from an observation point, and is usually measured clockwise from the north direction. For example, if the sun is located at a direction 150° clockwise from the north direction at a certain observation point at a certain time on a certain day, the solar azimuth angle at that time is 150°. The solar elevation angle refers to the angle between the sun and the horizon as viewed from an observation point. For example, the solar elevation angle at a certain time at the south pole of the moon can be only 1.5°.

[0032] According to the solar azimuth angle corresponding to each time slice, the corresponding terrain occlusion angle is queried from the terrain occlusion angle database.

[0033] By comparing the solar elevation angle corresponding to any time slice with the terrain occlusion angle, it is determined whether each position in the digital elevation map is in the light shadow at the time slice, and each position is marked as a light area or a shadow area, to generate the light and shadow area distribution of the time slice, until the light and shadow area distribution of each time slice is obtained.

[0034] The light area refers to an area in which the solar elevation angle is greater than the terrain occlusion angle in the direction of the solar azimuth angle at a specific time slice. For example, at a certain time slice, the solar elevation angle of a point is 1.8°, and the terrain occlusion angle of the point in the direction of the solar azimuth angle is 1.0°. Since 1.8>1.0, the point is determined to be a light area. The shadow area refers to an area in which the solar elevation angle is less than or equal to the terrain occlusion angle in the direction of the solar azimuth angle at a specific time slice. For example, at the same time slice, the solar elevation angle of another point is also 1.8°, but the terrain occlusion angle of the point in the direction of the solar azimuth angle is 2.5°. Since 1.8<2.5, the point is determined to be a shadow area.

[0035] In the above optional manner, the terrain occlusion angle database is further constructed to store the occlusion information of each position in different directions, the occlusion angle corresponding to the solar azimuth angle is queried, and the solar elevation angle is compared, to quickly determine the shadow state of each position at different time slices, and to improve the calculation efficiency and accuracy of the light and shadow area distribution.

[0036] In an optional manner, S2 specifically includes: The terrain slope value and the terrain roughness value of each position in the digital elevation map are calculated.

[0037] The terrain slope value refers to the inclination of the surface of the earth at a certain point, usually expressed in degrees. For example, by calculating the elevations of a point and its surrounding pixels in a digital elevation map, the slope value of the local area where the point is located is obtained as 8°. The terrain roughness value refers to a measure that describes the degree of local surface fluctuation, usually characterized by the maximum height difference of elevations within a certain range. For example, the difference between the maximum and minimum values of the 9 elevation values within a 3*3 window around a point is calculated to obtain the roughness value of the point as 0.15 m.

[0038] The positions with terrain slope values greater than the threshold value of the climbing ability of the rover or terrain roughness values greater than the threshold value of the obstacle crossing ability are determined as obstacle regions.

[0039] The threshold value of the climbing ability refers to the maximum slope limit value that the rover can safely climb. For example, if the design climbing ability of a certain model of rover is 15°, then 15° is the threshold value of the climbing ability. The threshold value of the obstacle crossing ability refers to the maximum height difference of local obstacles on the ground that the rover can safely cross. For example, if the design of the obstacle wheel of a certain model of rover can cross a rock with a height of 0.2 m, then 0.2 m is the threshold value of the obstacle crossing ability. The obstacle region refers to a region that is determined to be impassable due to terrain conditions exceeding the passing ability of the rover. For example, regions with slope values greater than 15° or roughness values greater than 0.2 m in the map are marked as obstacle regions in the static movement cost map.

[0040] The terrain slope value and terrain roughness value of each position in the digital elevation map that does not belong to the obstacle region are calculated to generate the static movement cost.

[0041] The normalized cost calculation refers to the process of mapping original terrain feature values with different dimensions and ranges to a unified standard numerical interval (such as 0 to 1) according to certain rules. For example, a region with a slope value between 0° and 15° is mapped to a movement cost value between 0 and 0.8 through a linear function.

[0042] In the above optional manner, the terrain passability is further evaluated by combining slope and roughness, the ability threshold value is set to clearly divide the obstacle region, the movement cost is normalized calculated for the passable region, and a fine static cost map is generated to provide a scientific terrain difficulty quantification basis for path planning.

[0043] In an optional manner, S3 specifically includes: For any time slice in the planning period, according to the light shadow area distribution of the time slice, set the shadow area as impassable, according to the static movement cost, set the obstacle area as impassable, and for each position in the digital elevation map, assign a corresponding movement cost value according to the static movement cost, to generate a two-dimensional cost map layer of the time slice, until the two-dimensional cost map layer of each time slice is obtained; wherein, the two-dimensional cost map layer of each time slice is composed of the passable state or movement cost value of each position corresponding to the corresponding time slice.

[0044] Wherein, the movement cost value refers to: in the two-dimensional cost map layer or the three-dimensional space-time cost map, a specific cost value assigned to each passable position, used to quantify the relative difficulty of moving at that position; for example, in the two-dimensional cost map layer of a certain time slice, the movement cost value stored at position (x, y) is 0.35. The two-dimensional cost map layer refers to: for a specific time slice, the passable state after fusing the light shadow information and the static terrain cost, expressed in the form of a two-dimensional grid data layer; for example, for the time slice "day 3", a 1000*1000 grid is generated, and each grid stores a movement cost value or a "impassable" identifier. The passable state refers to: in the two-dimensional cost map layer, the most basic classification of each grid cell: passable or impassable; for example, in the finally generated two-dimensional cost map layer, the content of each cell is either a specific movement cost value (indicating passable) or a special mark (indicating impassable).

[0045] According to the time sequence, combine the two-dimensional cost map layer corresponding to each time slice in the planning period to form the three-dimensional space-time cost map.

[0046] In the above optional manner, further fuse the light shadow distribution of each time slice with the static cost, set the shadow area and the obstacle area as impassable, and generate the two-dimensional cost map layer by assigning a corresponding movement cost value to the remaining positions, and combine and construct the three-dimensional space-time cost map in time sequence to realize unified modeling of dynamic light and static terrain.

[0047] In an optional manner, S4 specifically includes: From the starting point, expand the node in the three-dimensional space-time cost map; wherein, the expansion of the node is carried out simultaneously in the spatial dimension and the time dimension, the expansion in the spatial dimension is based on the predefined geometric neighborhood, and the expansion in the time dimension is one-way increasing to the next time slice along the time axis.

[0048] In this context, the predefined geometric neighborhood refers to the set of nodes spatially adjacent to the current node, defined beforehand during the spatial dimensional expansion of the path search. For example, when using a raster map, a commonly used predefined geometric neighborhood is the 8-neighborhood, which consists of eight adjacent raster cells: the top, bottom, left, right, and four diagonal directions of the central node. Unidirectional time axis increment means that during the time dimensional expansion of the path search, a node can only move a fixed time interval in the positive direction (future) of the time axis. For example, from a node representing "day 3," the node can only expand to a node representing "day 4." The next time slice refers to the time segment immediately following the current time slice in the discretized time series. For example, if the current time slice is "day 5" and the slice interval is 1 day, then the "next time slice" refers to "day 6."

[0049] The cumulative cost during node expansion is calculated based on the movement cost of the corresponding position of the node in the three-dimensional spatiotemporal cost map.

[0050] The node expansion process refers to the iterative steps in a path search algorithm that sequentially explore all possible subsequent nodes from the current node and calculate the corresponding costs. For example, in each step of the A* algorithm, the node with the smallest evaluation function is selected from the open list for expansion, and the costs of all its neighboring nodes are calculated. The cumulative cost refers to the sum of the movement costs of all road segments traversed from the starting point of the path search to the currently examined node. For example, during the search process, the cumulative cost g(n) to reach each node n is recorded.

[0051] Based on the accumulated cost, and in accordance with the principle of minimizing cost, a path is searched from the starting point to the ending point, and the path with the minimum accumulated cost is determined as the optimal path.

[0052] It should be noted that the movement cost used in the path search process of this embodiment has a specific meaning. Unlike the movement cost that generally only considers terrain suitability, the movement cost constructed in this embodiment is a combination of spatial terrain access cost and temporal waiting cost.

[0053] To achieve spatiotemporal joint optimization, this embodiment proposes a spatiotemporal search method that integrates time and spatial movement costs. The core of this method lies in designing a unified cost function that quantifies and integrates the spatial movement cost δ(x,y,t) and the time cost ζ(t). The spatial cost is calculated by weighting the slope cost S(x,y) and roughness cost R(x,y), i.e., δ(x,y,t) = α*S(x,y) + β*R(x,y). The time cost ζ(t) = λT(t) represents the time cost incurred in moving from one time slice to the next. To enable comparison and accumulation of spatial and time costs at the same scale, both need to be normalized and weighted by coefficients α, β, and λ.

[0054] When expanding nodes in the 3D spatiotemporal cost map, the search algorithm employs a 9-neighborhood search strategy, including 8 spatial directions (adjacent grids within the same time slice) and 1 temporal direction (the next time slice at the same spatial location). This design allows the algorithm to weigh the options of "immediately moving to the surrounding space" versus "staying in place and waiting until the next moment" at each decision point. The key to comprehensive optimization lies in setting weight coefficients to make the maximum spatial cost of moving within a time slice comparable to the temporal cost of waiting for a complete time slice. Specifically, by combining the rover's maximum movement speed and the time slice length, the theoretically maximum distance that can be moved within a time slice can be calculated, and then the upper limit of the spatial cost generated by this maximum distance under the worst terrain conditions can be estimated. By correlating this upper limit value with the cost of a time slice (λT(t)), the algorithm can proactively choose to "wait" until the next time slice when the travel cost in the surrounding area is high at the current moment, hoping for a better travelable path to emerge after the lighting conditions change. This mechanism ensures the global adaptability of path planning in time-varying environments and achieves the joint optimal allocation of time and space resources.

[0055] In the above-mentioned optional methods, the spatial and temporal dimension nodes are further expanded simultaneously in the three-dimensional spatiotemporal cost graph. The spatial expansion is based on the geometric neighborhood, and the temporal expansion is unidirectionally increasing. The movement cost is accumulated and the optimal path is searched according to the minimum principle to achieve long-distance spatiotemporal joint global optimization.

[0056] In one alternative approach, the step of extracting key path points from the optimal path and generating a smooth trajectory that satisfies the kinematic constraints of the planetary rover based on the key path points includes: The Douglas-Peucker algorithm is used to extract critical path points from a series of location points contained in the optimal path.

[0057] The Douglas-Peucker algorithm is a geometric algorithm for curve simplification that reduces the number of points that make up a curve by retaining the points that have the greatest impact on the overall shape. For example, when processing a polyline composed of multiple points, the algorithm outputs a set of key points to approximate the original curve after setting a distance tolerance ε.

[0058] Based on the key path points, a smooth movement trajectory that satisfies the kinematic constraints of the planetary rover is generated using a cubic spline curve fitting method.

[0059] The cubic spline curve fitting method refers to a mathematical method that uses piecewise cubic polynomial functions to make a curve smoothly pass through a series of given key points. For example, given multiple key path points, a smooth curve that is continuously differentiable of the second order can be constructed by calculating the cubic polynomial coefficients between every two adjacent points.

[0060] Among the above-mentioned optional methods, the Douglas-Peucker algorithm is further used to extract key path points to reduce data redundancy, and cubic spline curve fitting is used to generate a continuous and smooth trajectory to meet the kinematic constraints of the planetary rover, thereby improving trajectory smoothness and practical control feasibility.

[0061] In one alternative approach, the step of generating a sequence of navigation points for controlling the movement of the planetary rover based on the smoothed trajectory includes: The sampling interval on the smooth movement trajectory is determined based on the distance of the planetary rover's single-step movement control.

[0062] In this context, "planetary rover" refers to a robotic vehicle designed for mobile exploration on the surface of extraterrestrial bodies. For example, a certain type of planetary rover, powered by solar energy, possesses autonomous or teleoperated movement capabilities. The distance of a single-step movement control refers to the expected length of a linear movement that the planetary rover will execute and complete in a single operation after its navigation and control system issues a movement command. For example, based on the design of a certain type of planetary rover's movement control system, its positioning update and control command cycle is 10 seconds, and it moves at a preset safe speed of 0.5 m / s within this cycle; therefore, the distance of a single-step movement control is set to 5 meters. The sampling interval refers to the distance or time difference between two adjacent sampling points when performing discrete sampling on a continuous curve. For example, to generate navigation points, based on the planetary rover's movement speed of 200 m / h, if it is desired to issue a navigation point every 10 seconds, the sampling interval is approximately 0.56 meters.

[0063] Multiple location points are extracted from the smooth movement trajectory according to the sampling interval.

[0064] Here, a location point refers to a point on a two-dimensional plane represented by a coordinate pair (x, y); for example, a point in a navigation point sequence with coordinates (501235.6 m east and 3457123.4 m north).

[0065] The plurality of location points are arranged in order along the smooth movement trajectory to form the navigation point sequence.

[0066] In the above-mentioned optional methods, the smooth trajectory sampling interval is further determined according to the rover's movement speed, the corresponding position points are extracted and arranged in time sequence to generate a navigation point sequence, thereby realizing the conversion from continuous trajectory to discrete control commands and enhancing the adaptability and practicality of navigation control.

[0067] To achieve long-distance movement path planning in time-varying environments, this embodiment establishes a spatiotemporal integrated environmental cost model for the planetary rover, constructs a spatiotemporal movement path search method, and generates navigation points that satisfy the rover's movement constraints to address path planning requirements under scenarios with drastic changes in illumination. Long-distance movement path planning is based on a high-precision digital elevation map constructed from remote sensing imagery. The planning process mainly includes the following five steps: Step 1: Modeling the time-varying lighting and shadow environment.

[0068] To accurately calculate the illumination and shadow conditions over a large area, the key is to accurately calculate the terrain's shading of the sun, that is, the maximum terrain shading angle at each location in all directions. For example... Figure 2 As shown, the terrain occlusion angle is calculated point-by-point across a 360° azimuth. The azimuth angle is discretized at specified intervals (e.g., 0.5°) to generate a series of terrain occlusion angle map data. These maps together constitute a terrain occlusion angle database, which records the terrain occlusion angle of each location in the digital elevation map at different azimuths.

[0069] Step 2: Modeling the movement costs of time-varying and time-invariant factors.

[0070] The spatiotemporal integrated modeling of movement costs comprises two parts. The first part is the construction of a time-varying illumination and shadow map. Planetary rovers are constrained by illumination conditions and can typically only move within illuminated areas, which change rapidly over time. Based on the terrain occlusion angle database constructed in step one, combined with solar position information obtained from a celestial orbit kinematics database, the solar altitude angle and azimuth angle corresponding to each time slice within the planning period can be calculated. By comparing the terrain occlusion angle and solar altitude angle of each location in the digital elevation map along the solar azimuth direction of that time slice, it is determined whether the location is in illumination or shadow, and each location is marked as either an illuminated or shadowed area, thus generating the illumination and shadow region distribution for that time slice. For example... Figure 3 The image shown is an example of a light and shadow map of a slice at a certain moment.

[0071] The second part is the static geometric movement cost modeling, which is determined by the terrain and does not change over time. The movement cost is mainly analyzed from two terrain factors: slope and roughness. First, the terrain slope and roughness values ​​are calculated for each location in the digital elevation map. Slope is determined by analyzing the elevation values ​​of the center cell and its eight neighboring cells. Roughness can be calculated using the difference between the maximum and minimum terrain elevation values ​​in the neighborhood. Figure 4 A slope diagram is shown. Figure 5 A schematic diagram of slope cost is shown. Figure 6 A roughness diagram is shown. Figure 7 A roughness cost diagram is shown. Next, the terrain slope value is compared with the rover's climbing ability threshold, and the terrain roughness value is compared with the rover's obstacle-crossing ability threshold. Locations where the terrain slope value is greater than the climbing ability threshold or the terrain roughness value is greater than the obstacle-crossing ability threshold are identified as obstacle areas. Finally, for each location in the digital elevation map that does not belong to an obstacle area, the terrain slope value and terrain roughness value are normalized and their costs are calculated to generate a static movement cost map.

[0072] Step 3: Integrated modeling of spatiotemporal mobility costs.

[0073] The time-varying illumination and shadow information obtained in step two is fused with the static terrain cost information to form a three-dimensional spatiotemporal cost map that dynamically changes over time. For any time slice within the planning period, the following operations are performed: shadowed areas are set as impassable based on the illumination and shadow distribution of that time slice; obstacle areas are set as impassable based on the static movement cost; and each other location on the digital elevation map is assigned a corresponding movement cost based on the static movement cost. This generates a two-dimensional cost layer corresponding to that time slice, which consists of the accessibility status or movement cost of each location corresponding to that time slice. The two-dimensional cost layers corresponding to each time slice within the planning period are combined in chronological order to form the three-dimensional spatiotemporal cost map. Figure 8 The concept of a three-dimensional spatiotemporal cost volume is illustrated, where the time dimension extends from the planning start time to the end time, and the cost map of each Ti section consists of a comprehensive terrain cost map and a shadow obstacle map at time Ti.

[0074] Step 4: Search for long-distance movement paths in a dynamic spatiotemporal environment.

[0075] Path search is performed in a 3D spatiotemporal cost graph to obtain the optimal path consisting of a series of nodes containing spatial coordinates and temporal information. The search uses a starting point and an ending point containing spatial coordinates and temporal information as the starting and ending targets. The search process starts from the starting point and expands nodes in the 3D spatiotemporal cost graph; node expansion occurs simultaneously in both spatial and temporal dimensions. Spatial expansion is based on predefined geometric neighborhoods (e.g., eight neighborhoods), while temporal expansion is unidirectionally increasing along the time axis to the next time slice. During node expansion, the movement cost needs to be calculated. The movement cost consists of spatial and temporal costs. The spatial cost δ(x,y,t) is calculated by weighting the slope cost S(x,y) and roughness cost R(x,y) at position (x,y), i.e., δ(x,y,t)=α*S(x,y)+β*R(x,y). The temporal cost ζ(t) is related to the time increment from the previous time slice to the current time slice, i.e., ζ(t)=λT(t). Here, α, β, and λ are normalized weighting coefficients. The total cost g(n) of node (x,y,t) is the cumulative sum of the movement costs of all segments on the path from the starting point to that node, i.e., g(n) = ∑[α*S(x,y) + β*R(x,y) + λT(t)]. Based on the movement cost of the corresponding position of the node in the 3D spatiotemporal cost map, the cumulative cost during the node expansion process is calculated. Based on the cumulative cost, and according to the principle of minimizing cost, a path from the starting point to the ending point is searched, and the path with the minimum cumulative cost is determined as the optimal path.

[0076] Step 5: Fitting long-distance movement paths and generating navigation points.

[0077] The optimal path obtained in step four is fitted to satisfy the kinematic constraints of the planetary rover, generating a smooth trajectory. The Douglas-Peucker algorithm is used to extract critical path points from a series of locations within the optimal path. Based on these extracted critical path points, a cubic spline curve fitting method is employed to generate a smooth trajectory that satisfies the kinematic constraints of the planetary rover.

[0078] A sequence of navigation points for controlling the rover's movement is generated based on the smooth trajectory. The sampling interval on the smooth trajectory is determined based on the rover's speed. Multiple location points are extracted from the smooth trajectory according to this sampling interval. These extracted location points are then arranged in the order they appear on the smooth trajectory to form the navigation point sequence.

[0079] This embodiment solves the problem of long-distance "light-chasing" movement path planning for a planetary rover in a time-varying environment. This embodiment achieves integrated spatiotemporal cost environment modeling under conditions of drastic lighting changes. By constructing a three-dimensional spatiotemporal cost map that integrates time-varying lighting shadows and static terrain accessibility, a unified dynamic environment representation is provided for path planning. Based on this, a global spatiotemporal path search method calculated using this three-dimensional spatiotemporal cost map is established, capable of jointly optimizing in both spatial and temporal dimensions to generate the spatiotemporal path with the optimal cost. Simultaneously, a method is provided to extract key path points from discrete spatiotemporal path points and further generate smooth navigation trajectories, achieving comprehensive optimization and adaptive adjustment of the movement path in both temporal and spatial dimensions.

[0080] This embodiment effectively supports robust path planning under the influence of spatiotemporal variations in long-distance driving tasks. By iteratively calculating the geometric features of path points and optimizing distance thresholds, it achieves comprehensive optimization and extraction of key path points. This embodiment constructs an integrated solution for mobile path search suitable for complex time-varying environments. Through iterative optimization of the key point extraction and navigation point generation process, it significantly improves the applicability of the planned mobile path over a long period and its adaptability to time-varying environmental factors, demonstrating high engineering application value.

[0081] Figure 9 This diagram illustrates a structural schematic of an embodiment of a long-distance path planning system 200 for a planetary rover under time-varying conditions provided by the present invention. Figure 9 As shown, the long-distance path planning system 200 for planetary rovers in this time-varying environment includes: The first generation module 201 is used to generate the distribution of light and shadow areas of multiple time slices within the planned time period based on digital elevation maps and solar position information. The second generation module 202 is used to generate a static movement cost representing terrain accessibility based on the digital elevation map. The fusion generation module 203 is used to fuse the illumination and shadow region distribution of each time slice with the static movement cost to form a three-dimensional spatiotemporal cost map that changes dynamically over time. The path search module 204 is used to perform path search in the three-dimensional spatiotemporal cost map, taking the starting point and ending point containing spatial coordinates and time information as the starting and ending targets, expanding the nodes in both spatial and time dimensions and increasing the time dimension in one direction, calculating the movement cost between nodes based on the three-dimensional spatiotemporal cost map and following the principle of minimizing the cost, to obtain an optimal path consisting of a series of nodes containing spatial coordinates and time information. The path planning module 205 is used to extract key path points from the optimal path, generate a smooth movement trajectory that satisfies the kinematic constraints of the planetary rover based on the key path points, and generate a sequence of navigation points for controlling the movement of the planetary rover based on the smooth movement trajectory.

[0082] In one alternative embodiment, the first generation module 201 is specifically used for: Based on the digital elevation map, a terrain occlusion angle database is constructed; wherein, the terrain occlusion angle database is used to record the terrain occlusion angle of each location in the digital elevation map in different directions; Based on the solar position information, calculate the solar azimuth and solar altitude angle corresponding to each time slice within the planned time period; Based on the solar azimuth angle corresponding to each time slice, the corresponding terrain occlusion angle is queried from the terrain occlusion angle database respectively; By comparing the solar altitude angle and terrain occlusion angle corresponding to any time slice, it is determined whether each location in the digital elevation map is in the light or shadow of that time slice, and each location is marked as a lit area or a shadow area, generating the light and shadow area distribution of that time slice, until the light and shadow area distribution of each time slice is obtained.

[0083] In one alternative embodiment, the second generation module 202 is specifically used for: Calculate the terrain slope and terrain roughness values ​​for each location in the digital elevation map; Locations where the terrain slope value is greater than the climbing ability threshold of the rover or the terrain roughness value is greater than the obstacle crossing ability threshold are identified as obstacle areas. The static movement cost is generated by normalizing the terrain slope and terrain roughness values ​​of each location in the digital elevation map that does not belong to the obstacle area.

[0084] In an alternative embodiment, the fusion generation module 203 is specifically used for: For any time slice within the planned time period, the shadowed area is set as impassable based on the distribution of light and shadow areas in that time slice, and the obstacle area is set as impassable based on the static movement cost. Each other location in the digital elevation map is then assigned a corresponding movement cost based on the static movement cost, generating a two-dimensional cost layer for that time slice. This process is repeated until a two-dimensional cost layer for each time slice is obtained. Each two-dimensional cost layer for each time slice consists of the accessibility status or movement cost of each location corresponding to that time slice. According to the time sequence, the two-dimensional cost layers corresponding to each time slice within the planned time period are combined to form the three-dimensional spatiotemporal cost map.

[0085] In an alternative embodiment, the path search module 204 is specifically used for: Starting from the aforementioned starting point, nodes are expanded in the three-dimensional spatiotemporal cost graph; wherein, the expansion of nodes is carried out simultaneously in the spatial and temporal dimensions, the expansion in the spatial dimension is based on a predefined geometric neighborhood, and the expansion in the temporal dimension is unidirectionally increasing along the time axis to the next time slice. Based on the movement cost of the corresponding position of the node in the three-dimensional spatiotemporal cost map, calculate the cumulative cost during the node expansion process; Based on the accumulated cost, and in accordance with the principle of minimizing cost, a path is searched from the starting point to the ending point, and the path with the minimum accumulated cost is determined as the optimal path.

[0086] In an alternative embodiment, the path planning module 205 is specifically used for: The Douglas-Peucker algorithm is used to extract key path points from a series of location points contained in the optimal path; Based on the key path points, a smooth movement trajectory that satisfies the kinematic constraints of the planetary rover is generated using a cubic spline curve fitting method.

[0087] In an alternative embodiment, the path planning module 205 is specifically used for: Based on the distance of the planetary rover's single-step movement control, the sampling interval on the smooth movement trajectory is determined; According to the sampling interval, extract multiple location points from the smooth movement trajectory; The plurality of location points are arranged in order along the smooth movement trajectory to form the navigation point sequence.

[0088] It should be noted that the beneficial effects of the long-distance path planning system 200 for planetary rovers in time-varying environments provided in the above embodiments are the same as those of the long-distance path planning method for planetary rovers in time-varying environments described above, and will not be repeated here. Furthermore, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation processes are detailed in the method embodiments, and will not be repeated here.

[0089] The long-distance path planning system 200 for a planetary rover in a time-varying environment of the present invention can be a computer program (including program code) running on a computer device. For example, the long-distance path planning system 200 for a planetary rover in a time-varying environment of the present invention is an application software that can be used to execute the corresponding steps in the long-distance path planning method for a planetary rover in a time-varying environment of the present invention.

[0090] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned long-distance path planning methods for planetary rovers in time-varying environments. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the long-distance path planning method for planetary rovers in time-varying environments shown in any embodiment of the present invention by calling the computer program.

[0091] In one alternative embodiment, an electronic device is provided, such as Figure 10 As shown, Figure 10 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0092] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0093] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0094] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0095] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0096] Among them, electronic devices can also be terminal devices. A terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0097] It should be noted that, Figure 10 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0098] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned long-distance path planning methods for planetary rovers under time-varying environments.

[0099] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0100] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0101] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A star wheel long distance path planning method in a time-varying environment, characterized in that, The method comprises the following steps: Based on the digital elevation map and the sun position information, the light and shadow area distribution of multiple time slices within the planning period is generated; Based on the digital elevation map, a static movement cost representing the terrain passability is generated; The light and shadow area distribution of each time slice is fused with the static movement cost to form a three-dimensional space-time cost map that changes dynamically over time; In the three-dimensional space-time cost map, the start point and the end point are taken as the starting and ending targets, the nodes are expanded in the spatial dimension and the time dimension, the time dimension is unidirectionally increased, the movement cost between nodes is calculated based on the three-dimensional space-time cost map, and the path search is performed in accordance with the principle of minimum cost, so as to obtain an optimal path composed of a series of nodes containing spatial coordinates and time information; Key path points are extracted from the optimal path, a smooth movement trajectory satisfying the kinematic constraints of the planetary vehicle is generated based on the key path points, and a navigation point sequence for controlling the movement of the planetary vehicle is generated according to the smooth movement trajectory.

2. The star-planet vehicle long-distance path planning method in a time-varying environment according to claim 1, characterized in that, The step of generating the light and shadow area distribution of multiple time slices within the planning period based on the digital elevation map and the sun position information comprises the following steps: Based on the digital elevation map, a terrain occlusion angle database is constructed; wherein the terrain occlusion angle database is used to record the terrain occlusion angle of each position in different directions in the digital elevation map; According to the sun position information, the sun azimuth angle and the sun elevation angle corresponding to each time slice within the planning period are calculated; According to the sun azimuth angle corresponding to each time slice, the corresponding terrain occlusion angle is queried from the terrain occlusion angle database; By comparing the sun elevation angle and the terrain occlusion angle corresponding to any time slice, it is determined whether each position in the digital elevation map is in the light and shadow at this time slice, and each position is marked as a light area or a shadow area to generate the light and shadow area distribution of this time slice, until the light and shadow area distribution of each time slice is obtained.

3. The star-vehicle long-distance path planning method in a time-varying environment according to claim 2, characterized in that, The step of generating a static movement cost representing the terrain passability based on the digital elevation map comprises the following steps: The terrain slope value and the terrain roughness value of each position in the digital elevation map are calculated; The positions with terrain slope values greater than the climbing ability threshold of the planetary vehicle or the positions with terrain roughness values greater than the obstacle crossing ability threshold are determined as obstacle areas; The terrain slope value and the terrain roughness value of each position in the digital elevation map that does not belong to the obstacle area are subjected to normalized cost calculation to generate the static movement cost.

4. The star vehicle long distance path planning method in time-varying environment according to claim 3, characterized in that, The step of fusing the light and shadow area distribution of each time slice with the static movement cost to form a three-dimensional space-time cost map that changes dynamically over time comprises the following steps: For any time slice in the planning period, according to the light shadow area distribution of the time slice, set the shadow area as impassable, according to the static movement cost, set the obstacle area as impassable, and assign each position in the digital elevation map with a corresponding movement cost according to the static movement cost, generate a two-dimensional cost map layer of the time slice, until a two-dimensional cost map layer of each time slice is obtained; wherein, the two-dimensional cost map layer of each time slice is composed of the passable state or movement cost of each position corresponding to the corresponding time slice; In time sequence, combine the two-dimensional cost map layer corresponding to each time slice in the planning period to form the three-dimensional space-time cost map.

5. The star-vehicle long-distance path planning method in a time-varying environment according to any one of claims 1 to 4, characterized in that, The step of obtaining the optimal path composed of a series of nodes containing spatial coordinates and time information in the three-dimensional space-time cost map by starting and ending targets containing spatial coordinates and time information, expanding nodes in the spatial dimension and the time dimension, and searching for a path according to the principle of minimum cost, includes: Starting from the starting point, expand nodes in the three-dimensional space-time cost map; wherein, the expansion of nodes is carried out simultaneously in the spatial dimension and the time dimension, the expansion in the spatial dimension is based on a predefined geometric neighborhood, and the expansion in the time dimension is unidirectional along the time axis to the next time slice; According to the movement cost of the position corresponding to the node in the three-dimensional space-time cost map, calculate the cumulative cost in the node expansion process; Based on the cumulative cost, search for a path from the starting point to the ending point according to the principle of minimum cost, and determine the path with the minimum cumulative cost obtained by searching as the optimal path.

6. The star-vehicle long-distance path planning method in a time-varying environment according to claim 5, characterized in that, The step of extracting key path points from the optimal path and generating a smooth movement trajectory satisfying the kinematic constraints of the star planet vehicle based on the key path points, includes: Using the Douglas-Peucker algorithm, extract key path points from a series of position points contained in the optimal path; Based on the key path points, generate the smooth movement trajectory satisfying the kinematic constraints of the star planet vehicle by using a cubic spline curve fitting method.

7. The star-vehicle long-distance path planning method in a time-varying environment according to claim 6, characterized in that, The step of generating a navigation point sequence for controlling the movement of the star planet vehicle according to the smooth movement trajectory, includes: Based on the distance of single-step movement control of the star planet vehicle, determine the sampling interval on the smooth movement trajectory; According to the sampling interval, extract a plurality of position points from the smooth movement trajectory; Arrange the plurality of position points in sequence on the smooth movement trajectory to form the navigation point sequence.

8. A star vehicle long distance path planning system under time-varying environment, characterized in that, It includes: A first generation module for generating the light shadow area distribution of a plurality of time slices in a planning period based on a digital elevation map and sun position information; A second generation module for generating a static movement cost representing the terrain passability based on the digital elevation map; A fusion generation module for fusing the light shadow area distribution of each time slice with the static movement cost to form a three-dimensional space-time cost map that changes dynamically over time; The path searching module is configured to perform node expansion in the three-dimensional space-time cost map, with the start point and the end point containing space coordinates and time information as start and end targets, with nodes expanded in the space dimension and the time dimension simultaneously and the time dimension increasing unidirectionally, with movement cost between nodes calculated based on the three-dimensional space-time cost map, and with path searching performed following the principle of minimum cost, to obtain an optimal path composed of a series of nodes containing space coordinates and time information. The path planning module is configured to extract key path points from the optimal path, generate a smooth movement trajectory satisfying the kinematic constraint of the star globe vehicle based on the key path points, and generate a navigation point sequence for controlling movement of the star globe vehicle according to the smooth movement trajectory.

9. An electronic device, comprising: The electronic device includes a processor coupled with a memory, and the memory stores at least one computer program, which is loaded and executed by the processor, so that the electronic device implements the star globe vehicle long-distance path planning method in a time-varying environment according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one computer program, which is executed by the processor to implement the star globe vehicle long-distance path planning method in a time-varying environment according to any one of claims 1 to 7.

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