Modular robot reconfiguration energy consumption deterministic optimization method and system
By optimizing the modular robot reconfiguration process through motion substitution mechanism and Hungarian algorithm, the problems of high time consumption and high randomness in solving reconfiguration solutions in existing technologies are solved, achieving efficient calculation of the global optimal reconfiguration solution and simplifying the reconfiguration process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2025-12-30
- Publication Date
- 2026-06-02
AI Technical Summary
In existing modular robot reconfiguration methods, finding the reconfiguration solution with the shortest travel distance is time-consuming, highly random, and difficult to guarantee the optimal solution. In particular, the size of the solution explodes exponentially as the number of modules increases, leading to high complexity of the optimization problem.
By employing a motion substitution mechanism and the Hungarian algorithm, the problem of minimizing the number of moves for all modules during the modular robot reconfiguration process is transformed into a combinatorial optimization problem of task allocation through configuration sliding. The global optimal reconfiguration solution is achieved by combining configuration sliding and the Hungarian algorithm.
It effectively reduces the difficulty of constructing and solving optimization problems, realizes efficient and stable calculation of the global optimal solution with the shortest movement distance during the modular robot reconfiguration process, and simplifies the reconfiguration process.
Smart Images
Figure CN121447646B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of energy consumption optimization technology for reconfigurable robots, and particularly relates to a deterministic optimization method and system for the energy consumption of modular robots. Background Technology
[0002] Modular reconfigurable robots are assemblies formed by connecting multiple modules, each with independent sensing, movement, and operational capabilities, through articulated mechanisms. The topology of a modular reconfigurable robot can be altered according to the characteristics of the environment and the task at hand.
[0003] Mobile modular robots are an important branch of modular reconfigurable robots. Their modules are autonomous ground-based mobile platforms, and multiple mobile modules are connected by active and passive mechanisms to form a mobile assembly. This assembly of multiple mobile modules possesses terrain adaptability and mission execution capabilities that surpass those of a single platform, showing great promise for applications in rugged terrain transportation, disaster relief, and planetary exploration.
[0004] The reconfiguration process of a mobile modular robot refers to the process by which modules disconnect some of their connecting mechanisms, move to a new location, and form new connections, ultimately assembling into a composite robot with a new configuration. Robot reconfiguration is key to achieving adaptability to complex terrains and diverse tasks, and reconfiguration efficiency is one of the important performance indicators of modular robots.
[0005] The transformation movement distance is the sum of the movement distances of all modules during the transformation process. Among the various factors determining transformation efficiency, the transformation movement distance is an important optimization objective that has been extensively studied. As the number of modules increases, the size of the solution to the optimization problem explodes exponentially, making it challenging to find the transformation solution with the shortest movement distance.
[0006] Existing methods for finding the shortest migration distance in a modified structure include graph search, probabilistic graph search, and iterative optimization. In the process of finding the shortest migration distance in a modified structure: graph search algorithms traverse the solution space to search for the optimal solution, which is extremely time-consuming; the randomness of probabilistic graph search methods makes it difficult to guarantee obtaining the optimal solution, and the solution obtained in each run is uncertain; iterative optimization methods also have randomness and are prone to getting trapped in local optima. Summary of the Invention
[0007] To overcome the shortcomings of the prior art, this invention provides a deterministic optimization method and system for the energy consumption of modular robot configuration changes. Combining the characteristics of modular robot configuration changes, a motion substitution mechanism is proposed, and the global optimal solution is calculated quickly and efficiently through configuration sliding and the Hungarian algorithm. This overcomes the limitation of randomness in probabilistic search methods and can guarantee the determinism of the solution.
[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:
[0009] The first aspect of this invention provides a deterministic optimization method for the energy consumption of modular robots with varying configurations.
[0010] A deterministic optimization method for the energy consumption of modular robots includes the following steps:
[0011] Determine the initial configuration of the modular robot before the modification begins and the target configuration after the modification ends;
[0012] During the modular robot reconfiguration process, a motion substitution mechanism is used to enable the module to move through the interior of the current configuration;
[0013] The problem of minimizing the number of moves for all modules during the modular robot reconfiguration process is transformed into a combinatorial optimization problem of task allocation. By solving the problem using configuration sliding and the Hungarian algorithm, the globally optimal reconfiguration solution is obtained, thereby optimizing the energy consumption of the reconfiguration.
[0014] The second aspect of the present invention provides a deterministic optimization system for the energy consumption of modular robot variants.
[0015] A deterministic optimization system for the energy consumption of modular robots includes:
[0016] The configuration determination module is configured to: determine the initial configuration of the modular robot before the modification begins and the target configuration after the modification ends;
[0017] The motion substitution module is configured to use a motion substitution mechanism during modular robot reconfiguration to enable the module to move through the interior of the current configuration;
[0018] The global optimal reconfiguration solution module is configured to transform the minimum number of movement steps of all modules during the modular robot reconfiguration process into a combinatorial optimization problem of task allocation, and obtain the global optimal reconfiguration solution through configuration sliding and Hungarian algorithm, thereby optimizing the energy consumption of reconfiguration.
[0019] The above one or more technical solutions have the following beneficial effects:
[0020] This invention provides a deterministic optimization method and system for the energy consumption of modular robot reconfiguration, proposing a motion substitution mechanism to simplify the solution complexity of the optimization problem. By analyzing the characteristics of the modular robot reconfiguration process, this invention proposes a motion substitution mechanism that replaces a portion of the internal path of the triggering module moving to the target position with a substitution module. This achieves the module's traversal planning within the initial configuration while reducing the movement distance. This mechanism effectively reduces the difficulty of constructing and solving the optimization problem.
[0021] This invention proposes an efficient and deterministic optimization solution method. Based on the motion substitution mechanism, a simplified model of the distance optimization problem is constructed. This model identifies equivalent solutions with the same fitness value within the solution space, avoiding inefficient traversal of the entire solution space. Through configuration sliding and the Hungarian algorithm, a deterministic search is performed on the modified configuration solution, achieving efficient and stable computation of the globally optimal solution with the shortest movement distance.
[0022] This invention has scalability. While this invention focuses on rectangular modular robots with a two-dimensional planar row and column configuration, the proposed technology can be extended to array-type modular robot platforms with regular shapes and defined connection directions, such as two-dimensional hexagonal modular robots and three-dimensional cubic modular robots.
[0023] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0024] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.
[0025] Figure 1 This is a flowchart of the method in Example 1.
[0026] Figure 2 Examples 1 present two variant schemes proposed for the variant task example 1.
[0027] Figure 3 Examples 1 present two variant schemes proposed for the variant task example 2.
[0028] Figure 4 This is a schematic diagram of the motion substitution process of a modular robot under the motion substitution mechanism in Example 1.
[0029] Figure 5 This is a schematic diagram of the sliding configuration in Example 1.
[0030] Figure 6 The flowchart for solving the global optimal modified structure solution using the configuration sliding and Hungarian algorithm in Example 1 is shown.
[0031] Figure 7 Example 1 Figure 6 A flowchart for calculating the alternative motion path using the mid-position backtracking method. Detailed Implementation
[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0033] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.
[0034] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0035] Example 1
[0036] This embodiment discloses a deterministic optimization method for the energy consumption of modular robot variants.
[0037] like Figure 1 As shown, the deterministic optimization method for the energy consumption of modular robot variants includes the following steps:
[0038] Determine the initial configuration of the modular robot before the modification begins and the target configuration after the modification ends;
[0039] During the modular robot reconfiguration process, a motion substitution mechanism is used to enable the module to move through the interior of the current configuration;
[0040] The problem of minimizing the number of moves for all modules during the modular robot reconfiguration process is transformed into a combinatorial optimization problem of task allocation. By solving the problem using configuration sliding and the Hungarian algorithm, the globally optimal reconfiguration solution is obtained, thereby optimizing the energy consumption of the reconfiguration.
[0041] In the process of obtaining the globally optimal modified structure solution:
[0042] First, the shortest total distance allocation solution for all module movements is obtained by using configuration sliding and the Hungarian algorithm;
[0043] Subsequently, based on the motion substitution mechanism, the position backtracking method is adopted to obtain the globally optimal reconfiguration solution based on the shortest total distance of all module movements, thereby optimizing the energy consumption of the reconfiguration.
[0044] To apply the technology proposed in this embodiment, the modular robot platform must meet the following conditions:
[0045] 1. Modules are isomorphic; two combinations of different modules in the same location are considered to be the same configuration.
[0046] The modules are isomorphic, meaning that all modules that make up a modular robot are identical in terms of structure, function, size and shape.
[0047] Two combined configurations with different modules in the same position are considered the same configuration. Specifically, if two configurations have the same shape but different module numbers at corresponding positions, they are considered the same configuration. Changing the order of modules within a configuration does not alter the overall configuration, provided the shape remains unchanged.
[0048] 2. The modules have regular shapes and defined connection directions, such as rectangular modules with four-sided connections and hexagonal modules with six-way connections.
[0049] The technology provided in this embodiment is not applicable to irregularly shaped modules and robot platforms without a fixed connection direction, such as the arbitrary connection method of irregularly shaped modules and spherical modules.
[0050] It can be understood that, in the specific implementation of the steps in this embodiment, it is first necessary to determine the initial configuration of the modular robot before the modification begins and the target configuration after the modification ends. The process of the modular robot transforming from the initial configuration to the target configuration is called modification.
[0051] This embodiment proposes a motion substitution mechanism. For ease of description, the module that causes the motion substitution behavior is called the triggering module, and the module that participates in the motion of the triggering module is called the substitution module. The motion substitution mechanism is defined as follows:
[0052] The movement of a portion of the path from the trigger module to the target position is replaced by the movement of other modules in the current configuration besides the trigger module, achieving an effect equivalent to directly moving the trigger module to the target position. This can be further explained as follows:
[0053] First, in the provisional configuration, the locations of all modules except the trigger module are assumed to be passable; that is, these modules are temporarily set to not obstruct the trigger module. An initial path is then formed based on the shortest path length principle.
[0054] The initial path may be an internal path or a detour path, determined based on the principle of shortest length. Specifically: if the module's movement path passes through the locations of other modules, it is considered to pass through the interior of the configuration, and this path is called an internal path; conversely, an external detour refers to the module moving along the outer edge of the configuration to the target location, without passing through the locations of any other modules, and this path is called a detour path.
[0055] If the initial path is an inward path, due to obstruction from other modules, the triggering module cannot complete the inward path alone; alternative modules along the inward path must move sequentially. Starting with the alternative module closest to the target position, these modules move sequentially up to the triggering module. In terms of motion effect, other modules replace a portion of the path that would otherwise be completed by the triggering module. The effect of motion substitution is equivalent to directly moving the triggering module to the target position for the configuration. Therefore, the motion substitution mechanism can be simply described as: a portion of the inward path that moves the module towards the target position is replaced by alternative modules.
[0056] The motion substitution rule proposed in this embodiment helps the module complete the movement with the fewest steps, enabling the module to move in a way that allows it to pass through the interior of the configuration, and simplifies the calculation of the total movement distance during the entire reconstruction process.
[0057] The goal of the configuration change is to move the trigger module to the target position. However, directly moving the trigger module to the target position along the internal path would be impossible under practical physical conditions due to obstructions from other modules. The motion substitution proposed in this embodiment achieves the effect of filling the target position with the substitution module and filling the original position of the trigger module with the substitution module. As can be seen from condition 1 that a modular robot must satisfy, the result of motion substitution is equivalent to the result of directly moving the trigger module to the target position for the configuration. Therefore, motion substitution equivalently achieves the effect of the module passing through the interior of the configuration to fill the target position.
[0058] like Figure 2 and Figure 3 As shown, this embodiment exemplarily proposes two types of modified tasks, namely modified task example one and modified task example two, and respectively shows the internal path and the bypass path of modified task example one and modified task example two.
[0059] Figure 2 For the two variant schemes proposed in Example 1 of the variant task, Figure 2 The initial and target configurations of Example 1 of the modal task are shown in the left box. Figure 2 The right-hand frame shows two configuration options for transforming the initial configuration into the target configuration. A, B, C, D, E, and F represent different squares. Square A represents the initial position, and the squares with dashed borders represent the target position. The red broken lines represent the specific routes of the inner path and the bypass path. It can be seen that the inner path is: ACDEF - target position, and the bypass path is AB outside - target position.
[0060] Figure 3 For the two variant schemes proposed in Example 2 of the variant task, Figure 3 The initial and target configurations of Example 2 of the modified task are shown in the left box. Figure 3The right side of the box shows two configuration schemes for transforming the initial configuration into the target configuration. Square A represents the initial position, squares with dashed borders represent the target position, and red broken lines represent the specific routes of the inward path and the bypass path.
[0061] The exercise substitution mechanism is described in more detail below:
[0062] Module For the trigger module, the configuration is temporarily set to exclude Other modules for Without obstruction, based on the principle of shortest path, construct from The initial path from the current position to the target position;
[0063] In the configuration, The initial path traversed other Each module , Indicates the first replacement module. This represents the m-th replacement module. for The number of other modules traversed by the initial path; The number of steps required to complete the entire path is ;
[0064] exist On the initial path, The movement can be partially caused by Replace them in sequence;
[0065] During the replacement process Move to the target location. Move to The original position, and so on, until... Move to From its original position, the movement is completed. This represents the (m-1)th replacement module.
[0066] Naturally, The sum of the number of steps moved equals ,Right now:
[0067] (1)
[0068] in, express The number of steps moved. express The initial path traverses other modules.
[0069] For example, such as Figure 4The diagram illustrates the motion substitution process of a modular robot under a motion substitution mechanism. Solid-lined squares represent modules, and yellow squares represent trigger modules to be moved. The dashed border squares represent The target location. The yellow and blue squares together form the initial configuration of the modular robot, while the blue squares and the dashed-bordered squares together form the target configuration. On the initial path, via Finally, the target position is reached by the dashed border squares; after motion substitution, the current configuration is utilized. conduct The replacement, that is, by Move to the target position indicated by the dashed border squares. Exercise to The original position. It can be understood that the two methods described above can achieve the same module movement effect.
[0070] Module The moving parts were Replacement. Move to the target location in 2 steps, then Move in 3 steps The original position. The sum equals The number of steps to move directly to the target location, i.e. The path step size.
[0071] set up and Representing modules respectively And the row and column numbers of the target location. Under the four-neighborhood motion condition, The minimum number of steps required to complete all motions involved in traversing the interior of the configuration to reach the target position is... The sum of the absolute values of the row difference and column difference between the starting point and the target position, i.e.
[0072] (2)
[0073] Indicates the trigger module; express The minimum distance traveled to reach the target position by passing through the interior of the configuration.
[0074] Based on the aforementioned motion substitution mechanism, for those containing Each trigger module and The reconstruction process of a target point, solving the problem of minimizing the number of moves for all modules during the reconstruction process, is a combinatorial optimization problem of task allocation, which is solved in this embodiment by configuration sliding and the Hungarian algorithm.
[0075] Use the initial configuration OK column matrix Indicates that the target configuration is used OK column matrix Indicates. For example... Figure 5 and Figure 6 As shown, in this embodiment, the globally optimal modified structure solution is obtained through configuration sliding and the Hungarian algorithm, specifically including:
[0076] Step 1: Construct a structure with row and column sizes of [size missing]. A zero-matrix container. Placed at the center of the matrix container, so that of Elements located in the matrix container Location;
[0077] Step 2: Slide the initial configuration matrix within the container. ,by of The elements are vertices, from the matrix container. The position begins to slide row by row and column by column;
[0078] Step 3: Find each step by swiping. and The overlapping parts, such as Figure 5 As shown, this part of the module is called the non-triggering module, and the replacement module will be generated from the non-triggering module. In the non-overlapping parts, it belongs to... The module is used as a trigger module, belonging to The portion is taken as the target location to be filled. Assume this sliding step exists. Each trigger module records... The starting position of each trigger module and One target location to be filled;
[0079] Step 4: Calculate the shortest moving distance from each trigger module to each target location to be filled using equation (2) to form a cost matrix;
[0080] Step 5: Calculate the optimal allocation under this cost matrix condition using the Hungarian algorithm, and then... The optimal allocation of each unfilled target location is... Each trigger module calculates the total distance traveled in the current sliding step;
[0081] Step Six: Repeat steps three through six until... Slide to the matrix container Position, complete the slide;
[0082] Step 7: Select the sliding step with the smallest total moving distance from all sliding steps and record it to obtain the shortest total distance allocation solution; and according to the motion substitution mechanism, form the substitution path of all triggering modules in the sliding step with the smallest total moving distance through the position backtracking method to form the globally optimal modified structure solution.
[0083] Figure 5 The initial and target configurations are shown on the left side of the diagram. In the constructed matrix container, orange squares represent triggering modules, green squares represent target positions, and purple squares represent overlapping parts (non-triggering modules).
[0084] like Figure 7 As shown, the calculation method for forming the final modified structure solution through position backtracking in step seven above is as follows:
[0085] Step 1: Based on the Hungarian algorithm allocation result, select a trigger module and obtain the target position assigned to the module by the Hungarian algorithm. Starting from the target position, backtrack along the row direction to the position of the trigger module until backtracking to a non-trigger module or directly backtracking to the trigger module.
[0086] Step 2: If backtracking returns to a non-triggering module, assign the target position to the non-triggering module, which then becomes the replacement module. Use the current position of this replacement module as the new target position and continue backtracking. If backtracking directly returns to the triggering module, assign the target position to that triggering module and skip to Step 4.
[0087] Step 3: If the row backtracking reaches the same column number as the trigger module, it means the row backtracking is complete. Then, start backtracking along the column direction to the trigger module, that is, start column backtracking.
[0088] Similarly, if backtracking reaches a non-triggering module, the target position is assigned to the non-triggering module, which then becomes the substitute module. The backtracking continues with the current position of this substitute module as the target position; if backtracking reaches the triggering module, the target position is assigned to that triggering module, and the process jumps to step four.
[0089] Step 4: Check if all triggering modules have completed motion substitution planning. If so, obtain the final modified solution with the shortest movement distance, and the algorithm ends; otherwise, skip to step 1.
[0090] In the above process, backtracking means:
[0091] Starting from the target position and ending at the trigger module, check the properties of the modules within each cell along the row or column direction. "Cell by cell" means that at each step, the distance to the trigger module is reduced by one cell in the row or column direction.
[0092] Example 2
[0093] This embodiment discloses a deterministic optimization system for the energy consumption of modular robot variants.
[0094] A deterministic optimization system for the energy consumption of modular robots includes:
[0095] The configuration determination module is configured to: determine the initial configuration of the modular robot before the modification begins and the target configuration after the modification ends;
[0096] The motion substitution module is configured to use a motion substitution mechanism during modular robot reconfiguration to enable the module to move through the interior of the current configuration;
[0097] The global optimal reconfiguration solution module is configured to transform the minimum number of movement steps of all modules during the modular robot reconfiguration process into a combinatorial optimization problem of task allocation, and obtain the global optimal reconfiguration solution through configuration sliding and Hungarian algorithm, thereby optimizing the energy consumption of reconfiguration.
[0098] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.
[0099] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.
Claims
1. A deterministic optimization method for the energy consumption of modular robot variants, characterized in that, Includes the following steps: Determine the initial configuration of the modular robot before the modification begins and the target configuration after the modification ends; the modules constituting the modular robot are isomorphic, and two combined configurations of different modules in the same position are considered to be the same configuration; During the modular robot reconfiguration process, a motion substitution mechanism is used to enable the module to move through the interior of the current configuration; The module that causes the motion substitution behavior is called the triggering module, and the module that participates in the substitution of the triggering module's motion is called the substitution module; the motion substitution mechanism is defined as: First, the locations of all modules in the configuration except the trigger module are set to be accessible; An initial path is formed based on the shortest path length principle. The initial path includes an internal path and a detour path. If the initial path is an inward path, due to the obstruction of other modules, the inward path cannot be completed by the triggering module alone. It requires the alternative modules along the inward path to move continuously: starting from the alternative module closest to the target position on the inward path, and moving continuously one by one to the triggering module. In terms of motion effects, other modules replace part of the path that should have been completed by the triggering module. The effect of motion substitution is equivalent to moving the triggering module directly to the target position for the configuration. The problem of minimizing the number of moves for all modules during the modular robot reconfiguration process is transformed into a combinatorial optimization problem of task allocation. The global optimal reconfiguration solution is obtained by using configuration sliding and the Hungarian algorithm, thereby optimizing the energy consumption of the reconfiguration. Set the initial configuration as OK column matrix The target configuration is OK column matrix By using configuration sliding and the Hungarian algorithm, the globally optimal modified configuration solution is obtained, specifically including: Step 1: Construct a structure with row and column sizes of [size missing]. A zero-matrix container, Placed at the center of the matrix container, so that of Elements located in the matrix container Location; Step 2: Slide the target configuration matrix within the container ,by of The elements are vertices, from the matrix container. The position begins to slide row by row and column by column; Step 3: Find each step by swiping. and The overlapping portion is called the non-triggering module, and the replacement module will be generated from the non-triggering module; in the non-overlapping portion, it belongs to... The module is used as a trigger module, belonging to The portion is taken as the target position to be filled; assuming the current sliding step exists Each trigger module records... The starting position of each trigger module and One target location to be filled; Step 4: Calculate the shortest travel distance from each trigger module to each target location to be filled, forming a cost matrix; Step 5: Calculate the optimal allocation under the cost matrix conditions using the Hungarian algorithm, and then... The optimal allocation of each unfilled target location is... Each trigger module calculates the total distance traveled in the current sliding step; Step Six: Repeat steps three through five until... Slide to the matrix container Position, complete the slide; Step 7: Record the sliding step with the minimum total movement distance to obtain the allocation solution with the shortest total movement distance of all modules, and then obtain the globally optimal modified structure solution.
2. The deterministic optimization method for the energy consumption of modular robot variants as described in claim 1, characterized in that, In the process of obtaining the globally optimal modified structure solution: First, the shortest total distance allocation solution for all module movements is obtained by using configuration sliding and the Hungarian algorithm; Subsequently, based on the motion substitution mechanism, the position backtracking method is adopted to obtain the globally optimal reconfiguration solution based on the shortest total distance of all module movements, thereby optimizing the energy consumption of the reconfiguration.
3. The deterministic optimization method for the energy consumption of modular robot variants as described in claim 1, characterized in that, The specific description of the motion substitution mechanism is as follows: Use only the move trigger module In this way, Move from current position The target location was obtained. The initial path; In the configuration, The initial path traversed other Each module ; exist On the initial path, The movement is partly caused by Replace in sequence: During the replacement process Move along the path: Move to the target location. Move to The original position, and so on, until... Move to From the original position, complete the movement; in, for The number of other modules traversed by the initial path; This indicates the set trigger module; Indicates the first replacement module. This represents the (m-1)th replacement module. This represents the m-th replacement module.
4. The deterministic optimization method for the energy consumption of modular robot variants as described in claim 3, characterized in that, Under the condition of four-neighbor motion, the trigger module The minimum number of steps required to reach the target position by following the internal path and traversing the interior of the configuration is The sum of the absolute values of the row difference and column difference between the starting point and the target position.
5. The deterministic optimization method for the energy consumption of modular robot variants as described in claim 2, characterized in that, Based on the motion substitution mechanism, a position backtracking method is used to obtain the globally optimal reconfiguration solution based on the shortest total distance of all module movements. Specifically, this includes: Step 1: Based on the shortest total distance allocation solution for all modules, select a trigger module and obtain the target position assigned to the selected trigger module from the shortest total distance allocation solution for all modules. Starting from the target position of the selected trigger module, backtrack along the row direction to the position of the selected trigger module until backtracking to a non-trigger module or directly backtracking to the selected trigger module. Step 2: If backtracking to a non-triggering module, assign the target position to the non-triggering module, which becomes the replacement module; use the current position of this replacement module as the new target position and continue backtracking; if backtracking directly to the selected triggering module, assign the target position to the selected triggering module and jump to Step 4; Step 3: If the row backtracking reaches the same column number as the selected trigger module, it means that the row backtracking is complete, and the backtracking along the column direction to the selected trigger module begins, that is, the column backtracking begins; Similarly, if backtracking to a non-triggering module, the target position is assigned to the non-triggering module, which becomes the replacement module; the current position of this replacement module is used as the target position, and backtracking continues; if backtracking to a selected triggering module, the target position is assigned to the selected triggering module, and the process jumps to step four. Step 4: Check if all triggering modules have completed motion substitution planning. If so, obtain the globally optimal reconfiguration solution and the algorithm ends; otherwise, skip to step 1.
6. The deterministic optimization method for the energy consumption of modular robot variants as described in claim 1, characterized in that, The modules that make up the modular robot have regular shapes and defined connection directions.
7. A deterministic optimization system for the energy consumption of modular robot configuration variations, characterized in that, include: The configuration determination module is configured to: determine the initial configuration of the modular robot before the modification begins and the target configuration after the modification ends; the modules constituting the modular robot are isomorphic, and two combined configurations of different modules in the same position are considered to be the same configuration; The motion substitution module is configured to use a motion substitution mechanism during modular robot reconfiguration to enable the module to move through the interior of the current configuration; The module that causes the motion substitution behavior is called the triggering module, and the module that participates in the substitution of the triggering module's motion is called the substitution module; the motion substitution mechanism is defined as: First, the locations of all modules in the configuration except the trigger module are set to be accessible; An initial path is formed based on the shortest path length principle. The initial path includes an internal path and a detour path. If the initial path is an inward path, due to the obstruction of other modules, the inward path cannot be completed by the triggering module alone. It requires the alternative modules along the inward path to move continuously: starting from the alternative module closest to the target position on the inward path, and moving continuously one by one to the triggering module. In terms of motion effects, other modules replace part of the path that should have been completed by the triggering module. The effect of motion substitution is equivalent to moving the triggering module directly to the target position for the configuration. The global optimal reconfiguration solution module is configured to: transform the minimum number of movement steps of all modules in the modular robot reconfiguration process into a combinatorial optimization problem of task allocation, and obtain the global optimal reconfiguration solution through configuration sliding and Hungarian algorithm, thereby optimizing the energy consumption of reconfiguration; Set the initial configuration as OK column matrix The target configuration is OK column matrix By using configuration sliding and the Hungarian algorithm, the globally optimal modified configuration solution is obtained, specifically including: Step 1: Construct a structure with row and column sizes of [size missing]. A zero-matrix container, Placed at the center of the matrix container, so that of Elements located in the matrix container Location; Step 2: Slide the target configuration matrix within the container ,by of The elements are vertices, from the matrix container. The position begins to slide row by row and column by column; Step 3: Find each step by swiping. and The overlapping portion is called the non-triggering module, and the replacement module will be generated from the non-triggering module; in the non-overlapping portion, it belongs to... The module is used as a trigger module, belonging to The portion is taken as the target position to be filled; assuming the current sliding step exists Each trigger module records... The starting position of each trigger module and One target location to be filled; Step 4: Calculate the shortest travel distance from each trigger module to each target location to be filled, forming a cost matrix; Step 5: Calculate the optimal allocation under the cost matrix conditions using the Hungarian algorithm, and then... The optimal allocation of each unfilled target location is... Each trigger module calculates the total distance traveled in the current sliding step; Step Six: Repeat steps three through five until... Slide to the matrix container Position, complete the slide; Step 7: Record the sliding step with the minimum total movement distance to obtain the allocation solution with the shortest total movement distance of all modules, and then obtain the globally optimal modified structure solution.