Flying robot motion planning method and system in microgravity environment and storage medium

Abstract

The invention discloses a flying robot motion planning method and system in a microgravity environment and a storage medium, relates to the field of robot automation, and aims to solve the problem that an efficient, accurate and lightweight flying robot motion planning method is lacked for a dynamically changing microgravity environment. Comprising the following steps: step S100, planning an initial optimal path from a starting point to an end point in an ideal environment assuming that no obstacle exists, and recording key positions interacting with the obstacle in the path to form a crossing point sequence; step S200, performing ray tracing along a vertical direction of a crossing point connecting line, searching a barrier-free area in a path, marking the barrier-free area as a candidate node, dynamically adjusting the node by introducing a feedback mechanism, and constructing a topological graph through iterative optimization; and S300, performing path optimization in combination with the search A and kinematics constraint, and introducing a heuristic function to improve the path search efficiency, thereby obtaining the optimal path of the motion of the flying robot in the microgravity environment.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a method and system for motion planning of a flying robot in a microgravity environment and a storage medium. Background Art

[0002] As my country's microgravity environment construction and operation enter a new phase, the increasing variety and complexity of missions place greater demands on astronauts' physical strength and energy. As a new type of autonomous, intelligent, and unmanned equipment developed for microgravity environments, space intelligent flying robots are capable of independently monitoring, operating, and performing routine maintenance on scientific experimental payloads when operators are absent or unable to intervene directly. This effectively reduces the burden on personnel and improves operational efficiency in microgravity work scenarios. However, due to the narrow, complex, and dynamic interference-prone indoor work environments in microgravity environments, developing lightweight motion planning algorithms to ensure the robots can perform their tasks efficiently and safely has become a major challenge.

[0003] Although existing motion planning algorithms have achieved remarkable results in many scenarios, they still face many challenges in microgravity environments. Traditional path planning methods, such as those based on A*, perform well in static environments, but have high computational overhead and poor real-time performance when faced with dynamic obstacles. While RRT-based algorithms can avoid obstacles, their path search process is relatively rigid and susceptible to spatial constraints and environmental changes, resulting in insufficient path planning accuracy. In addition, while path optimization algorithms can generate high-quality motion trajectories, their high computational complexity limits their application in resource-limited microgravity environments. In highly dynamic and closed microgravity environments, path planning not only needs to deal with static obstacles, but also dynamic factors such as robot motion and equipment changes. When dealing with such complex environments, traditional algorithms often face problems with rough path planning or excessive computational effort, which affects the accuracy and safety of the robot when performing tasks.

[0004] Therefore, in a dynamic and complex environment like microgravity, designing an efficient and accurate lightweight motion planning method has become a technical challenge that needs to be solved urgently. Summary of the Invention

[0005] The technical problems to be solved by the present invention are:

[0006] In view of the dynamically changing microgravity environment, the existing technology lacks efficient, accurate and lightweight flying robot motion planning methods.

[0007] The present invention is to solve the above technical problems using the following technical solutions:

[0008] The present invention provides a method for motion planning of a flying robot in a microgravity environment, comprising the following steps:

[0009] Step S100: Under an ideal environment assuming no obstacles, plan an initial optimal path from the starting point to the end point, and record key locations where obstacles interact with the path to form a crossing point sequence;

[0010] Step S200: tracing rays in the vertical direction of the line connecting the crossing points, searching for obstacle-free areas in the path, marking them as candidate nodes, dynamically adjusting the nodes by introducing a feedback mechanism, and constructing a topology map through iterative optimization;

[0011] Step S300: Path optimization is performed by combining A-search and kinematic constraints, and a heuristic function is introduced to improve the efficiency of path search, so as to obtain the optimal path for the flying robot in the microgravity environment.

[0012] Furthermore, step S100 includes the following steps:

[0013] Assume the robot's position is ,in is the coordinate of the robot in three-dimensional space, starting from the starting point To the end Find a shortest path between:

[0014]

[0015] in, It is a path composed of several nodes;

[0016] Minimize the total cost of the path, the cost function J is:

[0017]

[0018] The key locations of interaction with obstacles in the path are dynamically recorded to form a sequence of "crossing points".

[0019] Furthermore, step S200 includes the following process:

[0020] The environment with static obstacles is modeled, and ray tracing is performed in the vertical direction of the line connecting the crossing points. The obstacle-free area closest to the crossing point and without colliding with obstacles is searched and marked as a candidate node. The nodes are dynamically adjusted by introducing a feedback mechanism. During each iteration, collision detection is performed on the edges of the graph. If a collision is detected, the edge will be marked as impassable, a new path will be constructed, and the topology structure will be updated to capture environmental changes.

[0021] Furthermore, step S300 includes the following steps:

[0022] Step S310: Set From the starting point to the current node The actual cost, is the heuristic estimated cost from the current node to the target node, then the total cost in the Hybrid A* algorithm is for:

[0023]

[0024] Step S320: construct the state equation and motion equation of the robot in the microgravity environment to determine the state of the node;

[0025] Step S330: Each time a node is updated, a new node is generated through control input, where the control input includes the propulsion force and rotation torque of the robot in space;

[0026] Step S340: Constructing a heuristic function for:

[0027]

[0028] in, They are the adjustment weight parameters, is the proximity of the current node to the obstacle in the topology graph, is the obstacle point, It is the distance from the current node to the target point along the topological route.

[0029] Furthermore, in step S320, the state equation of the robot in the microgravity environment is constructed as follows:

[0030]

[0031] is the position coordinate of the robot, is the Euler angle, which represents the angle around The rotation angle of the axis, is the linear velocity of the robot in the X, Y, and Z axis directions, It's around Angular velocity of the axis;

[0032] The equation of motion is:

[0033]

[0034] in: is the mass of the robot, It's the robot Thrust in the axial direction, It's a robot around The rotational torque of the shaft, It's a robot around moment of inertia of the axis;

[0035] Determine the status of the node:

[0036]

[0037] Each time a node is updated, a new node is generated through control input, and the new node state is:

[0038]

[0039] in is the time step, is the new position coordinate of the robot; is the new Euler angle.

[0040] Furthermore, the path planning model in step S300 also introduces a dynamic update mechanism. When the robot encounters new environmental changes during the execution of the task, the heuristic function is adjusted in real time to reflect the new obstacle position and dynamic environment.

[0041] The present invention provides a flying robot motion planning system in a microgravity environment. The system has a program module corresponding to the steps of the method described in any one of the above technical solutions, and executes the steps of the flying robot motion planning method in a microgravity environment during operation.

[0042] The present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to implement the steps of the method for motion planning of a flying robot in a microgravity environment described in any one of the above technical solutions when called by a processor.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] ① Lightweight Path Planning: This method boasts high computational efficiency, enabling real-time path planning in complex environments using only CPU computation. By optimizing the algorithm structure, it reduces reliance on hardware resources, meeting the requirements for efficient mission execution in resource-constrained microgravity environments for flying robots and ensuring accurate and stable path planning.

[0045] Dynamic Obstacle Avoidance and Flexibility: This method deeply integrates kinematic constraints with heuristic search strategies to perceive and avoid obstacles in real time in confined, dynamic environments, effectively preventing collisions and ensuring the robot's flexible movement. Through a precise path adjustment mechanism, the robot can quickly respond to environmental changes and promptly avoid unexpected obstacles, significantly improving its adaptability in dynamic environments.

[0046] ③ Efficient Mission Execution Support: Based on this method, intelligent flying robots can efficiently complete diverse tasks such as patrolling and transporting in microgravity. Not only can the robots adapt to the complex microgravity environment, but they can also perform multiple operations in confined spaces, significantly improving the efficiency of daily operations and scientific research activities.

[0047] ④ Engineering Application Value: This method achieves high-precision motion planning and real-time response for flying robots under limited computing resources, demonstrating high engineering application value. This technology is not only applicable to microgravity flying robots but also provides technical support for other resource-constrained and dynamically changing robotic application scenarios, demonstrating broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of robot motion planning in an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a topology map construction scheme for iterative optimization in an embodiment of the present invention;

[0050] Figure 3 Schematic diagram of the path-guided Hybrid A* algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the present invention, exemplary embodiments or examples of the present invention will be described below with reference to the accompanying drawings. Obviously, the described embodiments or examples are only some of the embodiments or examples of the present invention, and not all of them. Based on the embodiments or examples of the present invention, all other embodiments or examples obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0053] Specific implementation plan 1: Combined Figures 1 to 3 As shown, the present invention provides a method for flying robot motion planning in a microgravity environment, comprising the following steps:

[0054] Step S100: Under an ideal environment assuming no obstacles, plan an initial optimal path from the starting point to the end point, and record key locations where obstacles interact with the path to form a crossing point sequence;

[0055] Step S200: tracing rays in the vertical direction of the line connecting the crossing points, searching for obstacle-free areas in the path, marking them as candidate nodes, dynamically adjusting the nodes by introducing a feedback mechanism, and constructing a topology map through iterative optimization;

[0056] Step S300: Path optimization is performed by combining A-search and kinematic constraints, and a heuristic function is introduced to improve the efficiency of path search, so as to obtain the optimal path for the flying robot to move in a microgravity environment.

[0057] The main driving mode of the intelligent flying robot is air jet, and each side of the robot is equipped with 2 nozzles, for a total of 12 nozzles. In a microgravity environment, the robot needs to move flexibly in a microgravity environment, so path planning first needs to calculate the optimal path in an obstacle-free environment. Traditional path planning methods often use graph search algorithms such as A* and Dijkstra, but in the case of limited computing resources, these methods often have a large amount of calculation and are difficult to meet real-time requirements. Therefore, the primary task of the present invention is to provide a basis for subsequent obstacle avoidance and dynamic adjustment by calculating the optimal path from the starting point to the end point in an obstacle-free environment.

[0058] Specific implementation scheme 2: Step S100 includes the following steps:

[0059] like Figure 1 As shown in the figure, it is a schematic diagram of robot motion planning. Assume that the position of the robot is ,in is the coordinate of the robot in three-dimensional space. Assume that the environment is simplified into a three-dimensional grid model, where each grid node corresponds to a point in space. The goal of path planning is to To the end Find a shortest path between them, namely:

[0060]

[0061] in, It consists of several nodes The optimization criterion of the path is to minimize the total cost J of the path, using the Euclidean distance as the cost function:

[0062]

[0063] The key locations of the paths that interact with obstacles are recorded to form a crossing point sequence.

[0064] In an obstacle-free environment, the path planning algorithm calculates the optimal path through a graph search method (A* algorithm), where the heuristic function of the A* algorithm is The calculation is for the current node To the target node Assuming that the distribution of obstacles is sparse and static, the algorithm performs path optimization in this environment to ensure the shortest and most accurate path.

[0065] The internal environment is usually composed of a variety of static obstacles, the position and shape of these obstacles are relatively stable during the execution of the task. Defined as key locations along the path at each interaction with an obstacle, these locations represent the boundaries of the robot's potential contact with obstacles, providing a preliminary understanding of the environment. These crossing points are updated as the environment changes and provide important insights for subsequent path planning and obstacle avoidance decisions.

[0066] To improve path accuracy, the calculation of crossing points relies not only on simple obstacle detection but also takes into account the dynamic structure of the environment. This includes the possibility of small spatial fluctuations or structural changes, requiring the algorithm to be able to update obstacle information in real time during the path planning process. This implementation introduces dynamic environment modeling technology, combining building blueprints with real-time sensor data, and embeds an adaptive update mechanism for the environmental model into the algorithm. When a new obstacle is detected, the system promptly adjusts the crossing points in the path to avoid unnecessary collisions.

[0067] Because the environment is full of complex spatial structures, path planning relies on environmental modeling of static obstacles. By recording the crossing points in the path and performing ray tracing, the accuracy and reliability of path planning can be further improved.

[0068] Specific implementation scheme three: Step S200 includes the following process:

[0069] The static obstacle environment is modeled, and ray tracing is performed along the perpendicular direction of the line connecting the crossing points. The obstacle-free area closest to the crossing point that does not collide with the obstacle is searched and marked as a candidate node. The nodes are dynamically adjusted by introducing a feedback mechanism. During each iteration, collision detection is performed on the edges of the graph. If a collision is detected, the edge will be marked as impassable, a new path will be constructed, and the topology structure will be updated to capture environmental changes. Specifically:

[0070] set up To cross a point, the robot performs ray tracing from the point in the vertical direction. The ray tracing method is as follows:

[0071] Assume that the direction of path tracing is , the equation of the ray is:

[0072]

[0073] in The goal of the ray is to find a spatial position that is closest to the crossing point and does not collide with obstacles. This process is repeated until an obstacle-free area is found, and the area becomes a node of the candidate path. .

[0074] Ray tracing goes beyond simply finding an obstacle-free path; it also requires modeling specific structures in the environment. For example, the environment can be highly irregular, requiring the robot to navigate around protruding objects or equipment. Therefore, ray tracing algorithms must consider multiple dimensions of space, determining not only the intersection of the ray and obstacles but also the continuity and stability of the path based on the three-dimensional layout.

[0075] As the environment changes, the connections between crossing points need to be constantly adjusted to ensure the continuity and safety of the robot's path. Therefore, a topological graph construction method based on a feedback mechanism dynamically adjusts the path nodes. During the graph construction process, the connection relationship between crossing points is not completely determined at the beginning, but through iterative collision detection and path adjustment, the graph structure is gradually optimized. Specifically, for each edge in the path From the crossing point arrive The paths need to be checked for collisions:

[0076]

[0077] If a collision is detected, the edge will be marked as impassable and the optimization will continue to find a new path. Figure 2 As shown, after multiple iterations, the final topological structure diagram can accurately reflect the distribution of obstacles in the environment. The construction of the topological diagram is not limited to the description of static obstacles. It relies on setting corresponding risk levels according to different obstacle types. For special equipment in the cabin, the risk level of its nodes is high, and for ordinary doors and bulkheads, the risk level of the nodes is low. In the subsequent Hybrid A* algorithm, it does not tend to expand to high-risk nodes, which is specifically reflected in the different weights of the obstacle heuristic function. Therefore, each node in the topological diagram not only contains basic spatial coordinates, but also carries additional information related to the surrounding environment. With this information, the robot can more accurately consider environmental constraints when planning the path, thereby improving the feasibility and safety of the path. The rest of this implementation plan is the same as the specific implementation plan two.

[0078] The traditional A* algorithm can provide effective path search in a static environment, but it is often unable to effectively cope with the complexity of the environment in a complex spatial structure. To solve this problem, this embodiment adopts the following Figure 3The environment-guided Hybrid A* algorithm shown takes into account the microgravity environment and combines A* search with flying robot kinematics to better cope with the challenges of complex internal environments.

[0079] The basic principle of the Hybrid A* algorithm is to combine the constraints of robot motion (turning radius, maximum speed) with the graph search algorithm to form a path planning method that is more in line with actual conditions.

[0080] Specific implementation scheme 4: Step S300 includes the following steps:

[0081] Step S310: Set From the starting point to the current node The actual cost, is the heuristic estimated cost from the current node to the target node, then the total cost in the Hybrid A* algorithm is for:

[0082]

[0083] in, is the actual cost, calculated as the cost function of the Euclidean distance from the starting point to the current node;

[0084] Step S320: Construct the state equation of the robot in microgravity environment Equations of motion, which determine the states of nodes;

[0085] Step S330: Each time a node is updated, a new node is generated through control input, where the control input includes the propulsion force and rotation torque of the robot in space;

[0086] Step S340: constructing a heuristic function;

[0087] In the actual search process, the heuristic function not only considers the straight-line distance from the current node to the target node, but also needs to consider the distribution of obstacles recorded in the topology map to optimize the search path selection. Therefore, the heuristic function is constructed. for:

[0088]

[0089] in, They are respectively used to adjust the weight parameters to balance the straight-line distance to the target point and the cost distance to the obstacle; is the proximity of the current node to the obstacle in the topology graph, For obstacle points, the projection calculation is performed by calculating the point to the route, and at the same time querying the node to the nearest node in the topological structure diagram, and determining the corresponding weight according to the different risk levels of the nodes ; is the distance from the current node to the target point along the topological route. The rest of this implementation is the same as the specific implementation plan three.

[0090] To make the Hybrid A* algorithm more accurately adaptable to microgravity environments, this implementation combines a heuristic function with a topology graph. The topology graph provides coarse information about static obstacles in the environment, which guides the search process and makes path search more efficient.

[0091] Specific implementation plan 5: In a microgravity environment, the dynamic constraints of the flying robot are not affected by gravity. The robot can not only translate and rotate freely in space, but also needs to deal with tiny inertial forces, propulsion forces, and possible reaction forces. In a microgravity environment, the state equation of the robot's motion equation is:

[0092]

[0093] in: is the position coordinate of the robot, is the Euler angle, which represents the angle around The rotation angle of the axis, It's the robot Linear velocity in the axial direction, It's around Angular velocity of the axis.

[0094] When control inputs of thrust and torque are applied, the equation of motion is:

[0095]

[0096] in: It's a robot It's the robot Thrust in the axial direction, It's a robot around The rotational torque of the shaft, It's a robot around Moment of inertia of the axis.

[0097] In a microgravity environment, the robot's state includes position, rotation angle, speed, and angular velocity. Therefore, the state of the node is:

[0098]

[0099] Each time a node is updated, the control input Generate a new node:

[0100]

[0101] in is the time step, is the new position coordinate of the robot; The rest of this embodiment is the same as the specific embodiment five.

[0102] Specific implementation plan six: In order to further improve the efficiency of path planning, the present invention also introduces a dynamic update mechanism. When the robot encounters new environmental changes during the execution of the task, the heuristic function will be adjusted in real time to reflect the new obstacle position and dynamic environment. This dynamic adjustment mechanism not only improves the accuracy of path planning, but also effectively avoids the risk of collision of the robot in complex environments. By introducing the combination of heuristic functions and topological graphs, the Hybrid A* algorithm can more effectively find an optimal trajectory that meets the robot's kinematic constraints in a microgravity environment. The rest of this implementation plan is the same as the specific implementation plan five.

[0103] The present invention proposes a method (algorithm) for planning the motion of a flying robot in a microgravity environment, which is the underlying technical core of the present invention. Various products can be derived based on the algorithm.

[0104] Based on the method proposed in the present invention, a flying robot motion planning system in a microgravity environment is developed using a programming language. The lightweight three-dimensional space feasible domain expression is suitable for embedded platform deployment and meets real-time requirements. The system has program modules corresponding to the steps of the above-mentioned technical solution, and executes the steps in the above-mentioned flying robot motion planning method in a microgravity environment during operation.

[0105] The developed system (software) is stored on a computer-readable storage medium. This system possesses high robustness and can stably execute motion planning tasks in a microgravity environment. When invoked by a processor, the computer program is configured to implement the steps of the aforementioned method for motion planning for a flying robot in a microgravity environment. This materializes the present invention on a carrier, becoming a computer program product.

[0106] Various implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] The computer programs (also referred to as programs, software, software applications, or code) herein comprise machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., a magnetic disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0108] Although the present invention is disclosed as above, the scope of protection disclosed by the present invention is not limited thereto. Those skilled in the art of the present invention may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for motion planning of a flying robot in a microgravity environment, characterized in that: The steps include: Step S100: Under an ideal environment assuming no obstacles, plan an initial optimal path from the starting point to the end point, and record key locations where obstacles interact with the path to form a crossing point sequence; Step S200: tracing rays in the vertical direction of the line connecting the crossing points, searching for obstacle-free areas in the path, marking them as candidate nodes, dynamically adjusting the nodes by introducing a feedback mechanism, and constructing a topology map through iterative optimization; Step S300: Path optimization is performed by combining A-search and kinematic constraints, and a heuristic function is introduced to improve the efficiency of path search, so as to obtain the optimal path for the flying robot to move in a microgravity environment.

2. The method for flying robot motion planning in a microgravity environment according to claim 1, characterized in that: Step S100 includes the following steps: Assume the robot's position is ,in is the coordinate of the robot in three-dimensional space, starting from the starting point To the end Find a shortest path between: ； in, It is a path composed of several nodes; Minimize the total cost of the path, the cost function J is: ； The key locations where the vehicle interacts with obstacles in the path are dynamically recorded to form a sequence of "crossing points".

3. The method for flying robot motion planning in a microgravity environment according to claim 2, characterized in that: Step S200 includes the following process: The environment with static obstacles is modeled, and ray tracing is performed in the vertical direction of the line connecting the crossing points. The obstacle-free area closest to the crossing point and without colliding with obstacles is searched and marked as a candidate node. The nodes are dynamically adjusted by introducing a feedback mechanism. During each iteration, collision detection is performed on the edges of the graph. If a collision is detected, the edge will be marked as impassable, a new path will be constructed, and the topology structure will be updated to capture environmental changes.

4. The method for flying robot motion planning in a microgravity environment according to claim 3, characterized in that: Step S300 includes the following steps: Step S310: Set From the starting point to the current node The actual cost, is the heuristic estimated cost from the current node to the target node, then the total cost in the Hybrid A* algorithm is for: ； Step S320: construct the state equation and motion equation of the robot in the microgravity environment to determine the state of the node; Step S330: Each time a node is updated, a new node is generated through control input, where the control input includes the propulsion force and rotation torque of the robot in space; Step S340: Constructing a heuristic function for: ； in, They are the adjustment weight parameters, is the proximity of the current node to the obstacle in the topology graph, is the obstacle point, It is the distance from the current node to the target point along the topological route.

5. The method for motion planning of a flying robot in a microgravity environment according to claim 4, characterized in that: In step S320, the state equation of the robot in the microgravity environment is constructed as follows: ； is the position coordinate of the robot, is the Euler angle, which represents the angle around The rotation angle of the axis, is the linear velocity of the robot in the X, Y, and Z axis directions, It's around Angular velocity of the axis; The equation of motion is: ； in: is the mass of the robot, It's the robot Thrust in the axial direction, It's a robot around The rotational torque of the shaft, It's a robot around moment of inertia of the axis; Determine the status of the node: ； Each time a node is updated, a new node is generated through control input, and the new node state is: ； in is the time step, is the new position coordinate of the robot; is the new Euler angle.

6. The method for flying robot motion planning in a microgravity environment according to claim 5, characterized in that: The path planning model in step S300 also introduces a dynamic update mechanism. When the robot encounters new environmental changes during the execution of the task, the heuristic function is adjusted in real time to reflect the new obstacle position and dynamic environment.

7. A flying robot motion planning system in a microgravity environment, characterized by: The system has a program module corresponding to the steps of the method described in any one of claims 1 to 6 above, and executes the steps of the flying robot motion planning method in a microgravity environment when running.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the flying robot motion planning method in a microgravity environment according to any one of claims 1 to 6 when called by a processor.

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