Path generation and task scheduling method for multi-robot cooperative cross-layer additive manufacturing

Abstract

The invention discloses a path generation and task scheduling method for multi-robot cooperative cross-layer additive manufacturing, and the method comprises the steps: generating layer-by-layer staggered additive paths through the size of a welding gun nozzle and the size of a slice layer; the task scheduling method comprises the following steps: merging adjacent paths to generate task blocks, and determining a sequence dependency relationship between the task blocks according to the spatial positions of the task blocks; in each round of scheduling, selecting a task block without dependence or having a satisfied dependence relationship, executing the task block by the robot with the shortest current accumulated working time, and repeating the process until all the task blocks are arranged to be executed; and in the task block execution process, if collision occurs when the current task block is executed, the robot is controlled to wait until the collision task block is completed. According to the method, the multiple robots can effectively conduct additive manufacturing on different layers at the same time, and the high-efficiency additive manufacturing requirement is met.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing technology, and specifically to a path generation and task scheduling method for multi-robot collaborative cross-layer additive manufacturing. Background Technology

[0002] The widespread application of additive manufacturing technology in aerospace, rail transportation, and energy equipment, particularly the directional energy deposition process based on industrial robots, has led to its extensive use in the additive manufacturing of large metal components due to its high deposition efficiency. To further improve additive manufacturing efficiency, the collaborative execution of tasks by multiple robots and the parallel completion of additive manufacturing processes have become an important development direction for additive manufacturing systems.

[0003] Existing research has proposed several scheduling methods for multi-robot systems. These methods typically only consider the allocation and scheduling of paths within a single slice layer, meaning each robot must wait for all additive manufacturing paths in the current layer to be completed before proceeding to the next layer. Limited by the robot's workspace, some areas can only be completed by specific robots, leading to some robots remaining idle for extended periods during single-layer execution due to a lack of tasks. This is particularly problematic for components with asymmetrical structures or significant variations in layer height, significantly extending additive manufacturing time. Furthermore, existing research generally treats additive path generation and task scheduling as independent modules, lacking integrated coordination between the two. This fragmented design is unsuitable for additive manufacturing of complex components. Currently, there is a lack of technical solutions for multi-robot cross-layer additive manufacturing across different layers, and existing literature does not consider the overall planning of simultaneous path generation and task scheduling. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a path generation and task scheduling method for multi-robot collaborative cross-layer additive manufacturing, which solves the problems of high system idle rate and low manufacturing efficiency caused by using a single slice layer as the basic manufacturing unit in existing multi-robot additive manufacturing systems; and solves the problem of nozzle and part collision in cross-layer scenarios.

[0005] Technical Solution: The present invention provides a path generation method for multi-robot collaborative cross-layer additive manufacturing, comprising: generating progressively staggered additive paths by using the welding torch nozzle size and the slice layer size; calculating the offset between the start and end positions of the additive paths of adjacent slice layers, and adjusting the start and end positions of the paths to achieve a staggered effect; the calculation of the interlayer offset distance satisfies:

[0006] In the formula, This indicates rounding down; after determining the offset of the starting position of the path, the parallel grid method is used to generate all additive paths for a given slice layer.

[0007] Furthermore, the interlayer offset d ensures a minimum safe spacing constraint through a floor function, avoiding interference between the nozzle and the formed area.

[0008] The present invention provides a task scheduling method for multi-robot collaborative cross-layer additive manufacturing, comprising the following steps: (1) Task block generation: Merge adjacent parallel paths to form an executable task block; (2) Dependency determination: Determine the sequential dependency based on the spatial location of the task blocks, requiring the lower-level task blocks to be executed before the upper-level task blocks; (3) Task allocation: Select task blocks with no dependencies or whose dependencies are already satisfied; allocate them to the robot with the shortest cumulative working time; include the additive time of the task blocks in the robot's cumulative working time; iterate until all task blocks are allocated.

[0009] Furthermore, it also includes: collision avoidance: projecting the task block path onto the substrate and calculating the minimum bounding rectangle; if the bounding rectangle projections of the two task blocks coincide, a collision risk is determined; when a collision occurs, the robot waits until the conflicting task block is completed.

[0010] Furthermore, in step (1), the task block merging rule is as follows: set an endpoint distance threshold, merge adjacent paths with an endpoint spacing less than the threshold; if a part of the upper-level task block is directly built on the lower-level task block, it needs to be executed after the lower-level task is completed.

[0011] Furthermore, in step (3), tasks are dynamically allocated based on the principle of the shortest cumulative working time to reduce robot idle time.

[0012] This invention discloses a multi-robot collaborative cross-layer additive manufacturing system, comprising an additive path generation unit and an additive task scheduling unit, supporting multiple robots to work in parallel on multiple slice layers simultaneously. The path generation unit generates paths for cross-layer manufacturing, ensuring that the generated additive paths avoid collisions between the formed components and the nozzle in cross-layer manufacturing scenarios. Each additive path is considered an additive task. The additive task scheduling unit includes a task block generation module, a task block selection module, and a collision avoidance execution module. The task block generation module merges adjacent additive paths, packaging multiple additive tasks into a single task block, and determines the sequential dependencies between task blocks based on their spatial positions. The task block selection module selects a task block from all task blocks that has no dependencies or whose dependent task blocks have already been executed, assigning it to the robot with the shortest cumulative task time as its task to be executed. The collision avoidance execution module inserts waiting periods for robots based on whether they will collide with other robots during the execution of the selected task block, thus preventing collisions between robots.

[0013] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: This invention introduces inter-layer offset rules during the path generation stage, achieving path misalignment between adjacent slice layers and avoiding spatial interference between the nozzle and the formed area from the source. This invention can effectively enable multiple robots to operate simultaneously on multiple slice layers, breaking the robot inter-layer waiting time caused by traditional layer-by-layer stacking strategies, improving additive manufacturing efficiency, and shortening the overall manufacturing cycle. Attached Figure Description

[0014] Figure 1 This invention relates to a multi-machine, multi-layer manufacturing process; Figure 2 This is a schematic diagram illustrating the collision avoidance principle of the additive path generation method of the present invention. Figure 3 This is a schematic diagram of the part model of the present invention; Figure 4 This is a schematic diagram of the deposition path generated by the present invention; Figure 5 This is a schematic diagram illustrating the sequential dependencies between task blocks in this invention; Figure 6 This is a schematic diagram of the part model of the present invention; Figure 7 This is a schematic diagram of the robot's task allocation result according to the present invention; Figure 8 This is a schematic diagram of the robot's two-task allocation result according to the present invention; Figure 9 This is a schematic diagram of the robot's three-task allocation result according to the present invention; Figure 10 This is a schematic diagram of the robot's task allocation result according to the present invention; Figure 11 This is a schematic diagram of the robot's two-task allocation result according to the present invention; Figure 12 This is a schematic diagram of the robot's three-task allocation result according to the present invention; The markings in the diagram are as follows: 1. Welding torch; 2. Wire; 3. Substrate; 4. Formed part; 5. Nozzle height; 6. Slice layer height; 7. Offset distance; 8. Safety distance. Detailed Implementation

[0015] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0016] like Figure 1 As shown, this embodiment of the invention provides a path generation and task scheduling method for multi-robot collaborative cross-layer additive manufacturing, specifically including: 1. Additive Path Generation. The 3D model is sliced, and the initial deposition path for each layer is generated using the parallel grid method. Subsequently, based on the welding torch nozzle size and slice layer thickness, the interlayer offset distance that meets the collision avoidance requirements is calculated. The offset distance should satisfy the following relationship:

[0017] in, The vertical height of the nozzle from the building layer. This represents the minimum safe distance between the nozzle and the constructed portion. Based on the above calculations, the start and end points of each layer's path are offset layer by layer, causing adjacent layer paths to be spatially staggered. Figure 2 As shown, a path layout is formed that can avoid collisions between nozzles and components during cross-layer manufacturing.

[0018] 2. Merge adjacent paths to generate task blocks. After the additive path is generated, spatially adjacent paths in the same slice layer are merged to construct locally executable task blocks. Subsequently, based on the positional relationship of the task blocks in 3D space, the sequential dependency structure between task blocks is determined. Specifically, if a task block is located above another task block, and part of its area is directly built on top of the lower-level task block, meaning that the upper-level task can only begin execution after the corresponding lower-level task is completed, then a sequential dependency is determined between the two.

[0019] 3. Task Block Selection and Execution. From all task blocks, select one with no dependencies, or whose dependent task blocks have already been executed. Assign this task block to the robot with the shortest current cumulative task time, and include its additive manufacturing time in that robot's total task duration. Repeat the above process until all task blocks have been assigned and executed.

[0020] 4. Waiting Time Insertion. During task block execution, the system uses a projection method to determine if there is a potential collision risk. Specifically, the system obtains the XY projections of the task block the robot is about to execute and other task blocks being executed by other robots. If their projection areas overlap, the system determines that there is a collision risk in the execution of the current task block. In this case, the system inserts a waiting time for the task, causing the robot to pause execution until the conflicting task block is completed. This judgment process continues until all task blocks have been completed.

[0021] The following specific examples further illustrate the path generation and task scheduling method for multi-robot collaborative cross-layer additive manufacturing of this application and its effectiveness.

[0022] Case 1: like Figure 1 As shown, this invention provides a path generation method for multi-robot collaborative cross-layer additive manufacturing. Figure 2This diagram illustrates the collision avoidance principle of the additive path generation method mentioned in this invention. It demonstrates the spatial offset effect created by this method. By making a certain offset distance between each path layer, sufficient safety space is provided at the height where the nozzle might collide with the already formed part of the component (i.e., the portion greater than the nozzle height), thus preventing collisions between the nozzle and the component. This can be applied to applications such as... Figure 3 The geometric part with equal height shown.

[0023] First, extract the skeleton structure features of the part slice layer, and identify the path skeleton mainly as follows: Type node. Based on slice layer thickness. Nozzle distance Safety distance Calculate the offset Furthermore, offsets are applied at the start and end points of each layer to form staggered depositional path structures. Figure 4 The path generation results of the above method on this example component are shown. It can be seen that the paths of each layer form an effective misalignment in space and have good cross-layer collision avoidance performance.

[0024] Case 2: This invention provides a path generation and task scheduling method for multi-robot collaborative cross-layer additive manufacturing, applicable to, for example... Figure 6 The geometric part shown has a large height difference.

[0025] First, read the generated deposition path data, extract the coordinates of the start and end points of each path, and set an endpoint distance threshold. Traverse path pairs and merge endpoints with a spacing less than 1. The path is routed to the same task block. Then, the sequential dependencies between each task block are determined, and task blocks with no dependencies or whose dependencies are already satisfied are selected. The robot with the shortest cumulative working time is assigned to execute this task. The cumulative working time of each robot is updated, and the selection process is repeated until all task blocks have been selected. Finally, the task blocks on each robot are executed. Before each task block begins execution, it is determined whether the robot will collide during the execution of that task block. If a collision occurs, the robot is required to wait until the collided task block completes.

[0026] The path generation and task scheduling methods in this embodiment yield the task allocation results for Robot 1, Robot 2, and Robot 3 as follows: Figure 7 , Figure 8 , Figure 9 As shown.

[0027] The calculated total manufacturing time is 48760.58 seconds. The calculated average idle time is 813.67 seconds; To verify the effectiveness of the task allocation in this example, a comparative example was set up: simulation calculations were performed on the parts from Case 2 above based on the layer-by-layer manufacturing algorithm. The task allocation results for Robot 1, Robot 2, and Robot 3 are as follows: Figure 10 , Figure 11 , Figure 12 As shown.

[0028] The calculated total manufacturing time is 54731.78 seconds. The calculated average idle time is 14521.10s; As can be seen from Case 2 compared with the comparative example, the task allocation result greatly shortened the robot's idle time, effectively improved the efficiency of additive manufacturing, and shortened the overall manufacturing cycle.

Claims

1. A path generation method for multi-robot collaborative cross-layer additive manufacturing, characterized in that, Comprise: Generate layer-by-layer staggered additive paths by the size of the welding torch nozzle and the size of the slice layer; Calculate the offset of the start and end positions of the adjacent slice layer additive paths, and adjust the path start and end positions to form a staggered effect; The calculation of the interlayer offset distance satisfies: ; In the formula, represents rounding down; after determining the path starting position offset, the parallel grid method is used to generate all the additive paths of the given slice layer.

2. The path generation method for multi-robot collaborative cross-layer additive manufacturing according to claim 1, wherein, The interlayer offset distance d is ensured by the floor function to ensure the minimum safety distance constraint, avoiding the interference between the nozzle and the formed area.

3. A task scheduling method for multi-robot collaborative cross-layer additive manufacturing, characterized in that, Comprise the following steps: (1) Task block generation: merge adjacent parallel paths to form executable task blocks; (2) Dependency determination: determine the sequential dependency relationship according to the spatial position of the task block, and require the bottom task block to be executed before the upper task block; (3) Task allocation: select a task block with no dependency or whose dependency has been satisfied; Assign to the robot with the shortest cumulative working time; Add the additive time of the task block to the cumulative working time of the robot; Iterate until all task blocks are assigned.

4. The task scheduling method for multi-robot collaborative cross-layer additive manufacturing according to claim 3, characterized in that, Also include: Collision avoidance: project the task block path onto the substrate and calculate the minimum bounding rectangle; If the projections of the bounding rectangles of two task blocks overlap, a collision risk is determined; When a collision occurs, the robot waits until the conflicting task block is completed.

5. The task scheduling method for multi-robot collaborative cross-layer additive manufacturing according to claim 3, wherein, In step (1), the task block merging rule: set an endpoint distance threshold, and merge adjacent paths with an endpoint distance less than the threshold; If the upper task block is partially built on the lower task block, it needs to be executed after the lower task block is completed.

6. The task scheduling method for multi-robot collaborative cross-layer additive manufacturing according to claim 3, wherein, In step (3), dynamically allocate tasks by the shortest cumulative working time principle to reduce robot idle time.

7. A multi-robot collaborative cross-layer additive manufacturing system, characterized in that, Comprise an additive path generation unit and an additive task scheduling unit, supporting multiple robots to work in multiple slice layers simultaneously; The path generation unit is used for path generation for cross-layer manufacturing, and the generated additive path can avoid collision between the formed component and the nozzle in the cross-layer manufacturing scenario, and each additive path is an additive task; The additive task scheduling unit includes a task block generation module, a task block selection module, and a collision avoidance execution module; The task block generation module merges adjacent additive paths to package multiple additive tasks into a task block, and determines the sequential dependency relationship between the task blocks according to the spatial position of the generated task block; The task block selection module selects a task block with no dependency or whose dependent task block has been completed from all task blocks, and assigns it to the robot with the shortest cumulative task time as its to-be-executed task; The collision avoidance execution module inserts a waiting state for the robot according to whether the robot will collide with other robots during the execution of the selected to-be-executed task block, to avoid mutual collision between robots.

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