Automatic garbage sorting method, device and equipment for household garbage robot

Abstract

The invention provides an automatic garbage sorting method, device and equipment for a household garbage robot, and relates to the technical field of robots, the method comprises the following steps: combining the position of the robot, the position of each garbage and the throwing position of each garbage to formulate an initial driving route of the robot; obtaining the remaining garbage storage space of the robot and the garbage characteristics of the garbage; according to the remaining garbage storage space of the robot and the garbage characteristics of the garbage, the initial driving route is adjusted, and a target driving route of the robot is generated; and dynamic obstacle information in the target area is obtained, the target driving route is adjusted according to the dynamic obstacle information, and a final driving route of the robot is generated, so that the robot processes the garbage in the target area according to the final driving route. The garbage collection device has the technical effects that garbage scattered everywhere is automatically collected, and comprehensive classification and collection of the garbage are achieved.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a method, device and equipment for automatically sorting domestic waste by a robot. Background Art

[0002] As urbanization continues to accelerate, the amount of urban domestic waste generated is also increasing. The traditional manual waste collection and sorting methods are inefficient and error-prone, and can no longer meet the needs of modern urban domestic waste management.

[0003] At present, some intelligent garbage management equipment has appeared on the market, such as garbage sorting and recycling machines, garbage sorting boxes, etc. These devices have improved the efficiency and accuracy of household garbage sorting to a certain extent. However, most of these devices are fixed and cannot actively find and collect garbage scattered everywhere, and cannot achieve comprehensive garbage sorting and collection.

[0004] Therefore, there is an urgent need for an intelligent and autonomous method for collecting and sorting domestic waste to solve the above problems. Summary of the invention

[0005] The present application provides a method, device and equipment for automatically sorting domestic waste by a robot, which are used to automatically collect waste scattered everywhere and realize comprehensive classification and collection of waste.

[0006] In a first aspect, the present application provides a method for automatically sorting domestic waste by a robot, the method comprising: obtaining the position of the robot in a target area, the positions of multiple pieces of garbage in the target area and the placement locations of each piece of garbage; formulating an initial driving route of the robot in combination with the position of the robot, the positions of each piece of garbage and the placement locations of each piece of garbage; obtaining the remaining garbage storage space of the robot and the garbage characteristics of each piece of garbage; adjusting the initial driving route to generate a target driving route of the robot based on the remaining garbage storage space of the robot and the garbage characteristics of each piece of garbage; obtaining dynamic obstacle information in the target area, adjusting the target driving route based on the dynamic obstacle information, and generating a final driving route of the robot, so that the robot processes each piece of garbage in the target area according to the final driving route.

[0007] By adopting the above technical scheme, the robot can realize the efficient and intelligent automatic sorting of domestic waste by taking into account the robot position, garbage position, placement position, remaining storage space, garbage characteristics and dynamic obstacle information. First, by obtaining the position information of the robot and garbage in the target area and the placement position of the garbage, the initial driving route is formulated to provide a basic working path for the robot. Subsequently, considering the remaining garbage storage space of the robot and the characteristics of each garbage, the initial route is adjusted to generate a more reasonable target driving route, which avoids the robot from frequently going back and forth due to insufficient storage space during the work process and improves the efficiency of garbage collection. Finally, by obtaining dynamic obstacle information and adjusting the route accordingly, the final driving route is generated, so that the robot can flexibly respond to complex and changeable urban environments, effectively avoid collision risks, and ensure operational safety. This multi-level, dynamically optimized route planning method not only improves the efficiency and accuracy of garbage collection, but also enhances the adaptability and working stability of the robot in the actual environment, thereby realizing the automatic collection of garbage scattered everywhere in the target area and realizing the comprehensive classification and collection of garbage.

[0008] Optionally, the initial driving route of the robot is formulated in combination with the position of the robot, the position of each piece of garbage and the placement location of each piece of garbage, including: calculating the distance from the robot to each piece of garbage and the distance between each piece of garbage according to the position of the robot and the position of each piece of garbage; determining the order in which the robot processes each piece of garbage in turn in combination with the distance from the robot to each piece of garbage and the distance between each piece of garbage to obtain an initial processing sequence of the robot; planning the path for the robot to move between each piece of garbage in combination with the initial processing sequence and the placement location of each piece of garbage to generate the initial driving route of the robot.

[0009] By adopting the above technical solution, the distance from the robot to each piece of garbage and the distance between the garbage are calculated, providing basic data support for route planning. Subsequently, based on this distance information, the order in which the robot processes each piece of garbage is determined to form an initial processing sequence, which helps to optimize the efficiency of garbage collection and reduce unnecessary round trips. Finally, combined with the initial processing sequence and the placement location of each piece of garbage, the specific path for the robot to move between the garbage is planned to generate the final initial driving route. This method not only takes into account the optimization of the order of garbage collection, but also incorporates garbage placement into route planning, thereby realizing a complete workflow that takes into account both collection and placement. Through this comprehensive route planning method, the robot can complete the garbage collection and placement tasks with the most optimized path, significantly improving work efficiency, reducing unnecessary energy consumption, and also enhancing the coherence and coordination of the entire garbage management process. The formulation of this initial driving route lays a solid foundation for subsequent route optimization and helps to achieve more intelligent and efficient urban domestic waste management.

[0010] Optionally, the garbage characteristics include garbage type and garbage volume, and the initial driving route is adjusted according to the remaining garbage storage space of the robot and the garbage characteristics of each garbage to generate the target driving route of the robot, including: determining target garbage that needs to be processed first among the multiple garbage according to the garbage type of each garbage; adjusting the initial driving route according to the location of the target garbage and the location where the target garbage is placed to generate the first driving route of the robot; judging whether the remaining garbage storage space of the robot meets the processing requirements when the robot processes each garbage according to the first driving route according to the remaining garbage storage space of the robot and the garbage volume of each garbage; if the remaining garbage storage space of the robot meets the processing requirements, taking the first driving route as the target driving route of the robot; if the remaining garbage storage space of the robot does not meet the processing requirements, adjusting the first driving route to generate the target driving route of the robot.

[0011] By adopting the above technical solution, the target garbage that needs to be processed first is determined according to the type of garbage, which enables the robot to prioritize important or urgent garbage, thereby improving the timeliness and importance management of garbage processing. Secondly, the initial driving route is adjusted based on the location and placement of the target garbage to generate the first driving route, which ensures the efficient processing of priority garbage. Then, by judging whether the remaining garbage storage space of the robot meets the processing requirements, the system can pre-evaluate the feasibility of the route, avoiding the situation where the robot is interrupted due to insufficient storage space during the execution of the task. Finally, based on the judgment result, the first driving route is adopted as the target driving route, or a new target driving route is further adjusted to generate. This dynamic adjustment mechanism ensures that the robot always runs on the most optimized route, which can not only meet the garbage priority requirements, but also make full use of the robot's storage space, thereby improving the overall work efficiency.

[0012] Optionally, if the remaining garbage storage space of the robot does not meet the processing requirements, the first driving route is adjusted to generate a target driving route of the robot, including: obtaining the amount of garbage that the robot can process, and selecting a corresponding amount of garbage from the first driving route as garbage to be processed according to the amount of garbage that the robot can process; adjusting the first driving route according to the location of the garbage to be processed and the location where the garbage to be processed is placed to generate a second driving route of the robot; and using the second driving route as the target driving route of the robot.

[0013] By adopting the above technical solution, by obtaining the amount of garbage that the robot can handle, the system can accurately evaluate the actual processing capacity of the robot, so as to select a suitable amount of garbage as the garbage to be processed in the first driving route. This capacity-based selection method ensures that the robot can make full use of its storage space in each work, avoiding the situation of insufficient storage space or excessive idleness. Secondly, the first driving route is adjusted according to the location and placement of the garbage to be processed to generate the second driving route. This adjustment takes into account the spatial distribution characteristics of the garbage, so that the robot can complete the collection and placement of a limited amount of garbage in the most optimized path. Finally, the second driving route is determined as the target driving route of the robot, so that the entire processing process not only meets the storage capacity limit of the robot, but also ensures the rationality and efficiency of the route. This dynamic adjustment mechanism not only improves the work efficiency of the robot, but also enhances the adaptability and reliability of the system. In this way, the robot can maximize its processing capacity within a limited storage space, while avoiding the problem of frequent round trips due to insufficient storage space, and significantly improves the overall garbage processing efficiency.

[0014] Optionally, the dynamic obstacle information includes position, moving speed and moving direction, and the acquiring of the dynamic obstacle information in the target area, adjusting the target driving route according to the dynamic obstacle information, and generating the final driving route of the robot includes: predicting the position and time point at which the robot encounters the dynamic obstacle while driving along the target driving route according to the position, moving speed and moving direction of the dynamic obstacle; and adjusting the target driving route based on the position and time point to obtain the final driving route of the robot.

[0015] By adopting the above technical solution, the detailed information of dynamic obstacles, including position, moving speed and moving direction, is used to accurately predict the position and time point where the robot may encounter the dynamic obstacle. This prediction mechanism enables the system to identify potential collision risks in advance, providing a key basis for route adjustment. Secondly, based on the predicted encounter position and time point, the system makes targeted adjustments to the target driving route to generate the final driving route. This route optimization based on dynamic information not only improves the safety of the robot in complex environments, but also ensures the continuity and efficiency of the garbage collection process. By adapting to environmental changes in real time, the robot can flexibly avoid moving obstacles, reduce unnecessary pauses and detours, and thus maintain high work efficiency. This dynamic path planning method significantly enhances the robot's adaptability and working stability in urban environments, and effectively solves the problem that fixed routes are easily affected by environmental changes. At the same time, it also provides the robot with a larger operating space, enabling it to maximize the completion of garbage collection tasks while ensuring safety.

[0016] Optionally, predicting the position and time point at which the robot encounters the dynamic obstacle while traveling along the target driving route based on the position, the moving speed and the moving direction of the dynamic obstacle includes: acquiring the moving speed of the robot, and calculating the estimated time for the robot to arrive at each target point on the target driving route based on the moving speed of the robot and the target driving route; calculating the position of the dynamic obstacle at different time points based on the position, the moving speed and the moving direction of the dynamic obstacle; judging whether the position of the robot coincides with the position of the dynamic obstacle at any time point or the distance is less than a preset safety distance based on the estimated time for the robot to arrive at each target point on the target driving route and the position of the dynamic obstacle at different time points; if the position of the robot coincides with the position of the dynamic obstacle at a first time point or the distance is less than the preset safety distance, determining the position and the first time point as the position and time point at which the robot encounters the dynamic obstacle.

[0017] By adopting the above technical solution, by obtaining the robot's moving speed and combining it with the target driving route, the system can accurately calculate the estimated time for the robot to reach each target point on the route, which lays a time foundation for the subsequent collision risk assessment. At the same time, based on the position, moving speed and moving direction of the dynamic obstacle, the system can predict the position of the obstacle at different time points, which provides necessary information for the risk assessment in the spatial dimension. By comparing the positions of the robot and the dynamic obstacle at each time point, the system can determine whether there is a position overlap or the distance is less than the preset safety distance, so as to accurately identify the potential collision risk point. When it is found that there is a risk of collision between the robot and the dynamic obstacle at a specific time point, the system can determine the corresponding position and time point as the encounter parameter. This multi-dimensional and high-precision prediction method not only improves the accuracy of collision risk assessment, but also provides a specific time and space reference for subsequent route adjustments. In this way, the robot can identify and avoid potential dangerous situations in advance, significantly improving the safety and work efficiency in complex dynamic environments. At the same time, by considering the preset safety distance, the system can also leave enough buffer space for the robot's movement, further enhancing the safety and stability of the entire garbage collection process.

[0018] Optionally, after generating the final driving route of the robot, it also includes: monitoring the operating status of the robot, the garbage distribution change information in the target area and the current position of the robot; optimizing the final driving route according to the current position of the robot, the operating status of the robot and the garbage distribution change information, and generating a dynamic driving route for the robot.

[0019] By adopting the above technical solutions and monitoring the operating status of the robot, the system can timely grasp the robot's working ability and efficiency, providing an important reference for route optimization. At the same time, real-time monitoring of garbage distribution changes in the target area enables the system to quickly respond to environmental changes and adjust the collection strategy in a timely manner. In addition, by continuously tracking the current position of the robot, the system can evaluate the execution of the route in real time and provide accurate spatial information for dynamic adjustment. Based on these real-time data, the system can continuously optimize the final driving route and generate a dynamic driving route that is more in line with the current actual situation. This dynamic optimization mechanism enables the robot to flexibly respond to various emergencies, such as new garbage, environmental changes, or changes in its own state, so as to maintain an efficient working state. Through continuous route optimization, the robot can always complete the garbage collection task with the optimal path, significantly improving the overall work efficiency and resource utilization.

[0020] In a second aspect, the present application provides a domestic waste robot automatic garbage sorting device, the device comprising: a first acquisition module, a combination module, a second acquisition module, a first adjustment module and a second adjustment module; wherein the first acquisition module is used to acquire the position of the robot in a target area, the positions of multiple garbage in the target area and the placement positions of each garbage; the combination module is used to formulate an initial driving route of the robot in combination with the position of the robot, the positions of each garbage and the placement positions of each garbage; the second acquisition module is used to acquire the remaining garbage storage space of the robot and the garbage characteristics of each garbage; the first adjustment module is used to adjust the initial driving route according to the remaining garbage storage space of the robot and the garbage characteristics of each garbage, and generate a target driving route of the robot; the second adjustment module is used to acquire dynamic obstacle information in the target area, adjust the target driving route according to the dynamic obstacle information, and generate a final driving route of the robot, so that the robot processes each garbage in the target area according to the final driving route.

[0021] In a third aspect, the present application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned methods for automatic garbage sorting by a domestic garbage robot.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: a computer program that can be loaded by a processor and execute any of the above-mentioned methods for automatic sorting of garbage by a domestic waste robot is stored.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: By comprehensively considering the robot position, garbage position, placement position, remaining storage space, garbage characteristics and dynamic obstacle information, the efficient and intelligent automatic sorting of domestic garbage robots is realized. First, by obtaining the position information of the robot and garbage in the target area and the placement position of the garbage, the initial driving route is formulated to provide a basic working path for the robot. Subsequently, considering the remaining garbage storage space of the robot and the characteristics of each garbage, the initial route is adjusted to generate a more reasonable target driving route, which avoids the robot from frequently going back and forth due to insufficient storage space during work and improves the efficiency of garbage collection. Finally, by obtaining dynamic obstacle information and adjusting the route accordingly, the final driving route is generated, so that the robot can flexibly respond to complex and changing urban environments, effectively avoid collision risks, and ensure operational safety. This multi-level, dynamically optimized route planning method not only improves the efficiency and accuracy of garbage collection, but also enhances the robot's adaptability and working stability in the actual environment, thereby realizing the automatic collection of garbage scattered everywhere in the target area and realizing comprehensive classification and collection of garbage. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of a process of automatically sorting garbage by a domestic garbage robot provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a domestic waste robot automatic garbage sorting device provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0025] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0028] Figure 1 1 is a flow chart of a method for automatically sorting garbage by a household garbage robot provided in an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, obtaining the position of the robot in the target area, the positions of multiple garbage in the target area, and the placement positions of each garbage.

[0029] In this embodiment, the target area can be a variety of different environments, depending on the application scenario of the domestic waste robot. The target area usually refers to a specific area where garbage sorting is required, which may include but is not limited to the following: indoor environment: such as the interior space of public buildings such as office buildings, shopping malls, schools, hospitals, etc.; outdoor environment: such as parks, squares, pedestrian streets and other open public areas; community environment: including public areas of residential areas, such as roads and green spaces within the community; industrial parks: public places in areas such as factories and warehouses; transportation hubs: such as railway stations, airports, subway stations and other crowded areas; tourist attractions: such as scenic spots, theme parks and other areas where tourists gather.

[0030] In one example, the real-time location coordinates of the robot are obtained by installing a GPS positioning module on the robot or combining indoor positioning technology (such as UWB, Bluetooth beacons, etc.). For the location of multiple garbage in the target area, computer vision technology can be used to install a camera on the robot to detect and locate the garbage through image recognition algorithms. At the same time, deep learning models can be used to classify garbage so that the placement location of each garbage can be determined later. The placement location of each garbage is usually a pre-set fixed point, and this information can be stored in the robot's database.

[0031] In the process of obtaining this information, the robot may need to conduct a preliminary inspection in the target area to fully collect data. In order to improve efficiency, SLAM (Simultaneous Localization and Mapping) technology can be used to enable the robot to complete its own positioning and environmental map construction while moving. This not only obtains the required location information, but also generates a detailed environmental map to provide a reference for subsequent path planning.

[0032] The purpose of obtaining this information is to enable the robot to fully understand the working environment and task objectives. The robot position information is used to determine the starting point, the garbage position information is used to determine the target point to be processed, and the placement position information is used to plan the end point of garbage processing. These data together constitute the basic elements of the robot's driving route planning.

[0033] By accurately acquiring this location information, the robot's work efficiency and accuracy can be significantly improved. For example, the robot can calculate the optimal path based on its own position and the location of each garbage to avoid unnecessary repeated driving; according to the classification of garbage and the corresponding placement location, the robot can reasonably arrange the processing order to reduce the number of round trips; at the same time, accurate location information also helps the robot to accurately navigate in complex environments, avoid obstacles, and complete the garbage sorting task safely and efficiently.

[0034] S102, formulating an initial driving route of the robot based on the position of the robot, the position of each garbage and the placement location of each garbage.

[0035] In one example, the process of developing an initial driving route first involves calculating the distance from the robot to each piece of trash and the distance between the pieces of trash. This can be achieved through simple coordinate calculations, and considering the actual situation, it may be necessary to combine map information of the target area to calculate the actual traversable distance.

[0036] Next, based on these distance data, the system needs to determine the order in which the robot processes each piece of garbage to form an initial processing sequence. The simplest method can be used, that is, each time the unprocessed garbage closest to the current position is selected as the next target. Although this method may not be optimal, it can quickly generate a feasible processing sequence and is easy to implement and adjust.

[0037] After determining the initial processing sequence, the system needs to plan the specific path for the robot to move between the garbage according to the location of each garbage. This step needs to consider factors such as the terrain characteristics of the target area and possible static obstacles. The system can use pre-stored map information to plan a path between each two points that avoids known obstacles.

[0038] Based on the above embodiment, as an optional implementation, in S102, combining the position of the robot, the position of each garbage and the placement position of each garbage, formulating the initial driving route of the robot specifically includes S201-S203: S201, calculating the distance from the robot to each piece of garbage and the distance between each piece of garbage according to the position of the robot and the position of each piece of garbage.

[0039] S202, combining the distance from the robot to each piece of garbage and the distance between each piece of garbage, determining the order in which the robot processes each piece of garbage in sequence, and obtaining an initial processing sequence of the robot.

[0040] In one example, the robot's current location and the location information of all the garbage to be processed in the target area are obtained. This information can be obtained through the monitoring system in the area or the robot's own positioning system. Next, the system calculates the straight-line distance from the robot to each garbage point, and also calculates the mutual distance between all garbage points. These distance data form a complete distance matrix, which provides the basis for subsequent path planning.

[0041] With this distance matrix, various algorithms can be applied to determine the optimal processing order. A commonly used method is to use the improved nearest neighbor algorithm. The basic idea of ​​this algorithm is to select the next garbage point closest to the current position as the target each time. However, the simple nearest neighbor algorithm may lead to suboptimal solutions, so some optimization strategies can be introduced. For example, when selecting the next target, not only the nearest point to the current position is considered, but also the impact of selecting this point on the overall path length. The system can further optimize the initial sequence through methods such as local search or simulated annealing.

[0042] In actual applications, other factors need to be considered to improve this initial processing sequence. For example, some garbage may have a higher processing priority, such as perishable garbage or hazardous garbage; some areas may have time restrictions and need to be cleaned up within a specific time period. The system will integrate these constraints into the sequence generation process to ensure that the generated initial processing sequence takes into account the distance factor and meets the needs of actual operations.

[0043] The initial processing sequence obtained by this method can usually significantly improve the efficiency of garbage collection. For example, in an area containing 50 garbage points, the optimized processing sequence may reduce the total driving distance by about 30% compared with the random order or the simple order from near to far. This not only means that the robot can complete the task faster, but also means less energy consumption and less equipment wear.

[0044] More importantly, this initial processing sequence provides a good basis for subsequent real-time adjustments. In actual operation, the environment may change, new garbage may appear, or some originally planned garbage points may have been manually cleaned. With this initial sequence, the system can make local adjustments more efficiently without having to recalculate the entire path every time.

[0045] S203, combining the initial processing sequence and the placement locations of each garbage, planning the path for the robot to move between the garbage, and generating the initial driving route of the robot.

[0046] In one example, considering that different garbage has different placement locations, when the robot is asked to process garbage according to the initial processing sequence in S202, the placement locations of different garbage should also be considered. Different garbage corresponds to different garbage placement locations. If the garbage placement location is far away, the initial processing sequence may need to be adjusted to optimize the robot's moving path and improve overall efficiency.

[0047] Suppose that in a community, there are the following types of garbage and corresponding disposal locations: ordinary household garbage (G1, G4, G7): placed in garbage cans near each building in the community; recyclables (G2, G5, G8): placed in community recycling stations; large garbage (G3, G6): placed in designated large garbage storage points in the community; kitchen waste (G9, G10): placed in special kitchen waste treatment stations.

[0048] The initial processing sequence generated in step S202 is: G1>G2>G3>G4>G5>G6>G7>G8>G9>G10; In step S203, the system will consider the specific placement of each type of garbage and optimize the robot's moving path accordingly. The optimization process is as follows: First, the system identifies the types of garbage that can be processed centrally: recyclables (G2, G5, G8) can be placed in recycling stations; large garbage (G3, G6) can be placed in large garbage temporary storage points; kitchen waste (G9, G10) can be placed in kitchen waste treatment stations.

[0049] Then, the system rearranges the processing order to minimize the number of round trips of the robot: the processing of recyclables is centralized: G2>G5>G8; the processing of large garbage is centralized: G3>G6; the processing of kitchen waste is centralized: G9>G10. Considering that the placement of ordinary household garbage (G1, G4, G7) is scattered near each building, the system will try to intersperse their processing between other garbage processing to optimize the overall path.

[0050] Based on the above considerations, the system may generate the following optimized processing sequence: G1>G2>G5>G8>G4>G3>G6>G7>G9>G10.

[0051] The corresponding optimized moving path may be as follows: move from the starting point to the building where G1 is located to handle ordinary domestic waste; move to the recycling station to handle the recyclables of G2, G5, and G8 in turn; pass through the building where G4 is located to handle ordinary domestic waste; move to the large waste temporary storage point to handle G3 and G6; on the way back to handle the ordinary domestic waste of G7; finally move to the food waste treatment station to handle G9 and G10.

[0052] This optimized path takes into account the placement of different types of garbage, greatly reducing the number of round trips and total driving distance of the robot. For example, instead of going to the recycling station three times (to process G2, G5, and G8 respectively), it now only takes one trip to process all recyclables. Similarly, large garbage and kitchen waste are also processed in one centralized manner.

[0053] Through this optimization, the total driving distance can be reduced and the garbage collection efficiency can be significantly improved. At the same time, this method also reduces the number of round trips of the robot in the community and reduces the interference with the daily life of residents.

[0054] S103, obtaining the remaining garbage storage space of the robot and the garbage characteristics of each garbage.

[0055] In one example, the robot's remaining garbage storage space needs to be monitored in real time. This can be achieved by installing weight sensors or volume sensors in the robot's storage compartment. These sensors can continuously monitor the weight or volume of the garbage currently in the storage compartment, thereby calculating the remaining storage space. This information is crucial for the subsequent garbage collection strategy formulation because it determines how much garbage the robot can collect before returning to the drop-off point.

[0056] At the same time, the system needs to obtain the garbage characteristics of each type of garbage. Garbage characteristics may include information such as the type of garbage (such as recyclable, non-recyclable, hazardous waste, etc.), estimated weight, volume, etc. This information can be obtained through the camera in the target area combined with image recognition technology. The system can use a pre-trained machine learning model to analyze the captured garbage images to identify the type of garbage. For the estimation of weight and volume, it can be calculated based on the size and shape of the garbage in the image combined with pre-set reference data.

[0057] The purpose of obtaining this information is to enable the system to plan the order of garbage collection more intelligently. For example, when the robot has less remaining storage space, the system can prioritize the collection of smaller garbage to maximize the use of the remaining space. Alternatively, the system can adjust the collection order according to the type of garbage to ensure that different types of garbage are not mixed and stored, which is convenient for subsequent classification and processing.

[0058] In addition, understanding the characteristics of garbage can also help the robot prepare for the collection task. For example, for heavier garbage, the robot may need to adjust the strength of its mechanical arm; for garbage with special shapes, it may need to adjust the grasping strategy. These advance preparations can improve the success rate and efficiency of garbage collection.

[0059] S104, adjusting the initial driving route according to the remaining garbage storage space of the robot and the garbage characteristics of each garbage, and generating a target driving route of the robot.

[0060] In one example, the process of adjusting the initial driving route first needs to consider the robot's remaining trash storage space. Based on the amount of remaining space, the system will evaluate the amount of trash the robot can collect before it needs to return to the trash drop-off point. This evaluation will directly affect the route adjustment. For example, if the remaining space is small, the system may prioritize the robot to collect a small amount of trash close to the current location and then go to the drop-off point to empty the storage space, rather than continue to move a long distance along the initial route.

[0061] At the same time, the characteristics of each type of garbage are also an important basis for adjusting the route. The system will consider factors such as the type, weight and volume of the garbage. For large or heavy garbage, the system may adjust its position in the collection order to ensure that the robot can handle the garbage when there is sufficient storage space. In addition, the system will optimize the route according to the type of garbage, concentrate the same type of garbage as much as possible, and reduce the number of round trips between different delivery points.

[0062] In the actual adjustment process, the system may adopt a dynamic programming method. It will continuously calculate and update the optimal next target point based on the current remaining storage space and the characteristics of the surrounding garbage. This dynamic adjustment method enables the robot to more flexibly respond to changes in the working environment, such as sudden large amounts of garbage or a sharp reduction in storage space.

[0063] The target driving route generated after adjustment will contain more detailed information. In addition to indicating the robot's moving path, it will also mark the processing order of each garbage point, the estimated storage space usage, and the possible plan to return to the delivery point midway. These detailed information can guide the robot to perform tasks more efficiently and also provide a basis for possible real-time adjustments.

[0064] Based on the above embodiment, as an optional implementation, in S104, the garbage characteristics include garbage type and garbage volume. According to the remaining garbage storage space of the robot and the garbage characteristics of each garbage, the initial driving route is adjusted, and the target driving route of the robot is generated, which specifically includes S401-S404: S401, determining target garbage that needs to be processed first among multiple garbage according to the garbage type of each garbage.

[0065] In one example, the type information of each garbage to be processed is obtained, which may come from the previous garbage identification step or the preset garbage classification data. Then, the system sorts these garbage according to the pre-set priority rules. The priority rules may be based on multiple factors, such as the corruption speed of the garbage, the environmental impact, the recycling value, etc. For example, perishable organic garbage may be given a higher priority because they may produce odors and attract pests; hazardous garbage such as batteries or chemicals may also have a high priority to prevent potential environmental pollution; and recyclables may be given different priorities according to their recycling value.

[0066] The system also considers the quantity and distribution of garbage when determining priorities. If a certain type of high-priority garbage is small in quantity and dispersed, the system may weigh the processing efficiency and choose to process the medium-priority garbage that is larger in quantity or concentrated first. This dynamic adjustment can avoid efficiency losses caused by frequent long-distance movements of robots.

[0067] S402, adjusting the initial driving route according to the location of the target garbage and the placement location of the target garbage to generate a first driving route for the robot.

[0068] In one example, when implementing step S402, the system first needs to integrate multiple key information: the list of priority target garbage determined in step S401, the initial driving route generated in step S203, the specific location of each target garbage and the corresponding delivery location. Based on this information, a series of adjustments and optimizations are made to the initial driving route.

[0069] During the specific implementation process, the system will first try to include the priority target garbage into the existing initial driving route. If the location of a high-priority target garbage is far away from the initial route, the system will evaluate the cost and benefit of deviating from the initial route to handle the garbage. If the benefit is greater than the cost, the system will adjust the route accordingly. At the same time, the system will also consider the placement location of the target garbage and try to design a route that can efficiently connect the garbage location and the placement location.

[0070] During this process, the system may use some optimization algorithms, such as the nearest neighbor algorithm or genetic algorithm, to find the best route adjustment solution. These algorithms will consider multiple factors, including distance, time cost, energy consumption, etc., to balance processing priority and overall efficiency.

[0071] S403, judging, based on the remaining garbage storage space of the robot and the garbage volume of each garbage, whether the remaining garbage storage space of the robot meets the processing requirements when the robot processes each garbage according to the first driving route.

[0072] In one example, two key data need to be obtained: the remaining garbage storage space of the robot and the garbage volume of each garbage point on the first driving route. The remaining storage space of the robot can be monitored in real time by sensors, and the volume of each garbage may come from the previous garbage identification steps or estimated data. Next, the system will simulate the process of the robot collecting garbage in the order of the first driving route and calculate the remaining storage space at each step. If the robot cannot complete the processing of all garbage according to the current first driving route, for example, there is a situation where there is insufficient storage space when processing a certain garbage.

[0073] This advance judgment and simulation greatly improves the reliability and efficiency of the garbage collection system. First, it avoids the situation where the robot is forced to interrupt the task due to insufficient storage space during the actual execution process. This interruption will not only reduce efficiency, but may also cause some garbage to not be processed in time, causing environmental problems. Second, this step provides the system with an opportunity to adjust the strategy in time. For example, the system may adjust the driving route in step S404, return to the garbage disposal station to empty the storage space after processing G4, and then process G5.

[0074] S404, if the remaining garbage storage space of the robot meets the processing requirements, the first driving route is used as the target driving route of the robot; if the remaining garbage storage space of the robot does not meet the processing requirements, the first driving route is adjusted to generate the target driving route of the robot.

[0075] In one example, if the judgment result of step S403 indicates that the remaining garbage storage space of the robot is insufficient to complete all garbage collection tasks on the first driving route, the system will start the route adjustment process. The purpose of this process is to generate a new target driving route so that the robot can complete the garbage collection task as efficiently as possible under the condition of storage space limitation.

[0076] For example, continuing the case in step S403, the system may generate the following new route: G1>G2>G3>G4>Return to processing station to empty>G5; This adjustment ensures that the robot has enough storage space before processing G5. In some cases, the system may adjust the route more aggressively, such as postponing some non-priority garbage points to the next round of collection to ensure that high-priority garbage is processed in a timely manner: G1>G3>return to processing station to empty>G2>G4>G5; This dynamic adjustment capability ensures the completeness of the garbage collection task and avoids task interruptions due to insufficient storage space. This not only improves the reliability of the system, but also avoids environmental problems that may be caused by garbage retention. Secondly, by intelligently adjusting the route, the system is able to maximize the robot's work efficiency when storage space is limited. For example, by properly arranging the timing of returning to the processing station, the number of unnecessary round trips can be minimized, saving time and energy.

[0077] Based on the above embodiment, as an optional implementation, in S404, if the remaining garbage storage space of the robot does not meet the processing requirements, the first driving route is adjusted, and generating the target driving route of the robot specifically includes S4001-S4003: S4001, obtaining the amount of garbage that the robot can process, and selecting a corresponding amount of garbage from the first driving route as garbage to be processed according to the amount of garbage that the robot can process.

[0078] In one example, the amount of garbage that the robot can handle is obtained, which may be affected by multiple factors, such as the robot's remaining garbage storage space, battery life, expected working time, etc. Assume that after calculation, the system determines that the maximum amount of garbage that the current robot can handle is 5.

[0079] After obtaining this number, a corresponding number of garbage is selected from the first driving route generated in step S402 as the garbage to be processed, that is, the garbage that is not processed temporarily. This selection process needs to consider multiple factors, including the priority of the garbage (from the result of step S401), the location of the garbage, the volume of the garbage, etc. The system will try to select the most appropriate combination of garbage as the garbage to be processed while ensuring that the high-priority garbage is processed in a timely manner.

[0080] Assume that the first driving route contains the following garbage points: G1 (high priority), G2 (medium priority), G3 (high priority), G4 (low priority), G5 (medium priority), G6 (high priority), G7 (low priority). If the robot can handle 5 garbage points, the system may select G4 and G7 as the garbage to be processed. This selection takes into account the priority of the garbage and ensures that the robot can handle the most important garbage.

[0081] S4002: According to the location of the waste to be processed and the location where the waste to be processed is placed, the first driving route is adjusted to generate a second driving route for the robot.

[0082] In one example, when implementing step S4002, the system first considers the locations of the garbage to be processed determined in S4001, which represent garbage points that can be temporarily skipped in the current task. At the same time, the system also needs to consider the locations where these garbage to be processed are placed, usually garbage treatment stations or transfer stations. Based on this information, the system uses various path optimization algorithms, such as genetic algorithms, ant colony algorithms, or heuristic algorithms, to adjust the first driving route generated in step S402. During the adjustment process, the system will avoid the garbage points to be processed, optimize the path length, consider the location of the garbage treatment station, and balance the task priority.

[0083] For example, assuming that the first driving route is: starting point>G1>G2>G3>G4>G5>G6>G7>processing station, where G4 and G7 are determined by S4001 as waste to be processed, then S4002 may generate the following second driving route: starting point>G1>G3>G2>G5>G6>processing station. This new route avoids G4 and G7, and may optimize the order of visiting other points to reduce the total driving distance.

[0084] S4003: Use the second driving route as the target driving route of the robot.

[0085] S105, obtaining dynamic obstacle information in the target area, adjusting the target driving route according to the dynamic obstacle information, and generating a final driving route of the robot, so that the robot processes each garbage in the target area according to the final driving route.

[0086] In one example, obtaining dynamic obstacle information mainly relies on a specialized monitoring system installed in the target area. This system may include high-definition cameras, infrared sensors, laser scanners and other devices, which are strategically placed throughout the area to provide comprehensive environmental monitoring. These devices can capture all dynamic changes in the area in real time, including dynamic obstacles such as pedestrians, moving vehicles or other moving objects.

[0087] The central control system continuously processes the data sent back by these monitoring devices, using computer vision and target tracking algorithms to identify and track dynamic obstacles. The system builds a real-time updated environmental map that contains information such as the location, movement direction, and speed of dynamic obstacles. This centralized environmental perception method can provide more comprehensive and accurate environmental information than methods that rely on the robot's own sensors, especially when dealing with larger areas or multiple robots working together.

[0088] Based on the dynamic obstacle information obtained, the central control system needs to adjust the previously generated target driving route. This process involves real-time path planning and obstacle avoidance strategies. The system will evaluate whether there is a potential collision risk on the current route. If a risk is found, the system will calculate a new path to bypass the obstacle. This may involve commanding the robot to temporarily change its direction of travel, slow down and wait for the obstacle to pass, or completely re-plan a new path.

[0089] During the adjustment process, the system not only considers avoiding current obstacles, but also predicts the possible movement trajectory of obstacles to avoid collision at a certain point in the future. This predictive planning can make the robot's movement smoother and reduce unnecessary pauses and direction changes.

[0090] In addition, route adjustment also needs to balance safety and efficiency. Although safety is the primary consideration, the system will also strive to find a solution that can avoid obstacles without excessively extending the driving distance. This may involve quickly evaluating and comparing multiple possible paths and selecting the best compromise.

[0091] Through these adjustments, the system finally generates the robot's final driving route. This route not only includes the garbage collection plan in the original target driving route, but also incorporates the adaptation strategy for the dynamic environment. The central control system will continue to send updated route information and control instructions to the robot, instructing the robot on how to efficiently complete the garbage disposal task while ensuring safety.

[0092] For example, the garbage collection robot was originally planned to start from the northwest corner of the area, go straight along the main pedestrian street for 300 meters, and then turn right to enter the small square to collect garbage. However, when the robot just started to move, the monitoring system detected that a group of about 20 tourists were moving slowly 150 meters away from the planned route, and a temporary street performance that attracted a large number of spectators was taking place near the small square. The central control system quickly processed this information and predicted that the robot might be blocked by the crowd at 150 meters if it drove according to the original plan, and it would be difficult to work effectively after arriving at the small square. Therefore, the system immediately adjusted the robot's driving route, instructing it to deviate 50 meters to the east, bypass the main pedestrian street, and move forward through a parallel path with less traffic. When the robot approached the small square, the system noticed that the street performance was still going on, so it further adjusted the route and instructed the robot to temporarily skip this area and go to a quieter area on the east side to collect garbage. At the same time, the system found that several trash cans near the robot's new route were close to overflowing. Although these trash cans were not in the original plan, in order to improve overall efficiency, the system decided to let the robot deal with these trash cans along the way. While the robot is performing a new task, the system continues to monitor the entire area. When it finds that the crowd in the small square begins to disperse, it will update the robot's route again and arrange for it to return to the small square after completing the current task.

[0093] Based on the above embodiment, as an optional implementation, in S105, the dynamic obstacle information includes the position, moving speed and moving direction, the dynamic obstacle information in the target area is obtained, and the target driving route is adjusted according to the dynamic obstacle information. The generation of the final driving route of the robot specifically includes S501-S502: S501, predicting the position and time point at which the robot encounters the dynamic obstacle while traveling along the target driving route according to the position, moving speed and moving direction of the dynamic obstacle.

[0094] In one example, real-time environmental data needs to be acquired and processed first, including information from the robot's own sensors (such as lidar, cameras, ultrasonic sensors, etc.), as well as information from possible external data sources (such as traffic monitoring systems, IoT devices, etc.). The system uses this data to build a dynamic model of the current environment and identify and track surrounding dynamic obstacles, such as pedestrians, vehicles, or other moving objects. For each identified dynamic obstacle, the system records its current position, estimates its moving speed, and predicts its possible direction of movement. At the same time, the system analyzes the robot's target driving route, including predetermined waypoints, estimated driving speed, and turning plans. Based on this information, the future trajectories of the robot and each dynamic obstacle are predicted. By comparing these predicted trajectories, the system can calculate potential encounter points and encounter times.

[0095] For example, if the system detects a pedestrian moving east at 0.5 m / s and the robot plans to move north at 1 m / s, the system calculates that they are likely to meet at a certain coordinate point in 30 seconds.

[0096] Based on the above embodiment, as an optional implementation, in S501, according to the position, moving speed and moving direction of the dynamic obstacle, predicting the position and time point at which the robot encounters the dynamic obstacle during the driving process along the target driving route specifically includes: The moving speed of the robot is obtained, and the estimated time for the robot to reach each target point on the target driving route is calculated according to the moving speed of the robot and the target driving route; the position of the dynamic obstacle at different time points is calculated according to the position, moving speed and moving direction of the dynamic obstacle; according to the estimated time for the robot to reach each target point on the target driving route and the position of the dynamic obstacle at different time points, it is determined whether the position of the robot coincides with the position of the dynamic obstacle at any time point or the distance is less than a preset safety distance; if the position of the robot coincides with the position of the dynamic obstacle at a first time point or the distance is less than a preset safety distance, the position and the first time point are determined to be the position and time point where the robot and the dynamic obstacle meet.

[0097] In one example, the robot's moving speed is obtained, which may come from the robot's internal sensors or control system. Then, the system calculates the estimated time for the robot to reach each target point on the route based on the robot's moving speed and the planned target driving route. This step uses basic physical motion formulas to assign an estimated arrival time to each target point, taking into account the length of the route, the robot's speed, and possible acceleration and deceleration processes. At the same time, the system uses similar motion formulas or more complex prediction models (such as Kalman filters) to calculate the position of dynamic obstacles at different points in time based on the initial position, moving speed, and direction of the dynamic obstacles. This process may need to consider factors such as the acceleration and steering ability of the obstacle to improve the accuracy of the prediction.

[0098] Next, the system compares the robot's predicted position with the predicted position of the dynamic obstacle on the timeline. For each time point, the system calculates the distance between the robot and the obstacle to determine whether there is a position overlap or the distance is less than the preset safety distance. This preset safety distance is a key parameter, which needs to be determined based on factors such as the size of the robot, the stopping distance, and the operating environment. If the system finds that the distance between the robot and the obstacle is less than the safety distance at a certain time point (called the first time point), it will mark that position and time point as a potential encounter point.

[0099] S502, adjusting the target driving route based on the position and time point to obtain the final driving route of the robot.

[0100] In one example, the prediction data provided by step S501 is comprehensively analyzed, including possible collision points, encounter times, and motion characteristics of dynamic obstacles. At the same time, the system also needs to consider factors such as the robot's current state, task priority, and energy level. Based on this information, the system will use complex path planning algorithms, such as dynamic window methods or sampling-based path planning methods, to dynamically adjust the target driving route. During the adjustment process, the system will weigh multiple goals, including avoiding collisions, minimizing driving distance, optimizing energy use, and ensuring task completion time.

[0101] For example, if S501 predicts that a fast-moving vehicle may meet at a certain intersection in 30 seconds, S502 may choose to let the robot slow down slightly or change its route to avoid this potential danger point. Alternatively, if it is predicted that a certain section of road will be crowded with pedestrians within a specific time period, the system may choose a slightly longer but safer alternative route.

[0102] After generating the robot's final driving route, it also includes: Monitor the robot's operating status, garbage distribution change information in the target area and the robot's current position; optimize the final driving route and generate the robot's dynamic driving route based on the robot's current position, the robot's operating status and garbage distribution change information.

[0103] In one example, the system first continuously monitors three key factors: the robot's operating status, information about changes in the distribution of garbage in the target area, and the robot's current location. The robot's operating status includes information such as its power level, work efficiency, and whether it has encountered any faults, which can be obtained through the robot's internal sensors and control system.

[0104] Information about changes in garbage distribution in the target area may come from multiple sources, such as real-time reporting systems, environmental monitoring sensor networks, or feedback from other robots. The current location of the robot is accurately obtained through GPS or other positioning systems. The system uses this real-time data, combined with the final driving route generated previously, and uses intelligent algorithms (such as dynamic programming, reinforcement learning, etc.) to optimize the route.

[0105] For example, if a large amount of garbage is detected in a certain area, the system may adjust the route to prioritize the robot in that area. Or, if the robot's battery is lower than expected, the system may re-plan the route to get it to the charging station faster. Or, if a minor malfunction is detected in a function of the robot, the system may adjust the route to avoid tasks that require that function. This dynamic optimization process is ongoing, and the system will continuously update and adjust the driving route based on the latest information to generate a dynamic driving route.

[0106] In one embodiment of the present invention, after the domestic waste robot completes the waste collection, it needs to transport the waste to a sorting line for further processing. However, directly transporting all the waste to the sorting line may cause equipment damage or reduce processing efficiency. In order to solve this problem, the robot of the present invention will perform a pre-sorting before transporting the waste to the sorting line.

[0107] Specifically, the robot will first identify and screen out large non-recyclable materials and materials that may damage the sorting equipment. The implementation of this step relies on the robot's visual recognition system and material database. The robot's visual system scans each piece of garbage and compares the scan results with the pre-stored material database. If it is identified as belonging to the above two categories of special materials, the robot will store them separately in a special container instead of sending them to the sorting line.

[0108] The purpose of this pre-sorting operation is twofold: first, it can effectively prevent materials that may damage the sorting equipment from entering the assembly line, thereby increasing the service life of the equipment and reducing maintenance costs; second, by eliminating large non-recyclable materials that the assembly line cannot handle, the efficiency of the subsequent sorting process can be significantly improved.

[0109] After completing the pre-sorting, the robot will transport the remaining garbage to the sorting line. It is worth noting that the robot of the present invention has unique adaptive capabilities when working on the sorting line. The robot is equipped with an advanced motion control system and a flexible robotic arm, which can adjust its position and movement in real time according to the grabbing situation of the garbage. Specifically, the robot will continuously monitor the distribution and movement of the garbage on the assembly line through the visual system, and adjust its own movement speed and position as needed to always maintain the best operating position. At the same time, the robot's robotic arm will also dynamically adjust its grasping posture and strength according to the shape, size and position of different garbage.

[0110] This adaptive capability brings significant benefits. First, it greatly improves the efficiency and accuracy of sorting, because the robot can always maintain the best working condition; second, it enhances the flexibility of the system, allowing the robot to cope with various types and states of garbage; finally, this intelligent operation mode also reduces the need for manual intervention, further reducing labor costs.

[0111] Through the above measures, the domestic garbage robot of the present invention can not only efficiently complete the collection and preliminary classification of garbage, but also ensure the safe operation and efficient processing of the sorting line. This intelligent garbage disposal method greatly improves the efficiency and reliability of the entire garbage management system, and provides strong technical support for the intelligent management of urban domestic garbage.

[0112] Based on the above method, the present application also discloses a household garbage robot automatic garbage sorting device, such as Figure 2 As shown, Figure 2 : is a structural diagram of a household garbage robot automatic garbage sorting device provided in an embodiment of the present application, the device comprises: a first acquisition module, a combination module, a second acquisition module, a first adjustment module and a second adjustment module; wherein, The first acquisition module is used to acquire the position of the robot in the target area, the positions of multiple garbage in the target area and the placement positions of each garbage; the combination module is used to formulate the initial driving route of the robot in combination with the position of the robot, the positions of each garbage and the placement positions of each garbage; the second acquisition module is used to acquire the remaining garbage storage space of the robot and the garbage characteristics of each garbage; the first adjustment module is used to adjust the initial driving route according to the remaining garbage storage space of the robot and the garbage characteristics of each garbage, and generate the target driving route of the robot; the second adjustment module is used to acquire the dynamic obstacle information in the target area, adjust the target driving route according to the dynamic obstacle information, and generate the final driving route of the robot, so that the robot can process each garbage in the target area according to the final driving route.

[0113] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0114] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0115] The communication bus 1002 is used to realize the connection and communication between these components.

[0116] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0117] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0118] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0119] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for automatically sorting garbage by a domestic garbage robot.

[0120] exist Figure 3 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program stored in the memory 1005 for a method for automatically sorting garbage by a domestic garbage robot. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0121] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0122] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0123] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0128] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for automatically sorting domestic waste by a robot, characterized in that: The method comprises: Obtaining the position of the robot in the target area, the positions of multiple garbage in the target area, and the placement positions of each garbage; Formulate an initial driving route of the robot based on the position of the robot, the position of each piece of garbage and the placement location of each piece of garbage; Obtaining the remaining garbage storage space of the robot and garbage characteristics of each of the garbage; According to the remaining garbage storage space of the robot and the garbage characteristics of each of the garbage, the initial driving route is adjusted to generate a target driving route of the robot; The dynamic obstacle information in the target area is obtained, and the target driving route is adjusted according to the dynamic obstacle information to generate a final driving route of the robot, so that the robot processes the garbage in the target area according to the final driving route.

2. The method for automatically sorting garbage by a domestic garbage robot according to claim 1, characterized in that: The step of formulating the initial driving route of the robot in combination with the position of the robot, the position of each piece of garbage and the placement position of each piece of garbage includes: Calculating the distance from the robot to each piece of garbage and the distance between each piece of garbage according to the position of the robot and the position of each piece of garbage; Determine the order in which the robot processes the garbage in sequence based on the distance from the robot to each garbage and the distance between each garbage, and obtain the initial processing sequence of the robot; In combination with the initial processing sequence and the placement locations of the garbage, a path for the robot to move between the garbage is planned to generate an initial driving route for the robot.

3. The method for automatically sorting garbage by a domestic garbage robot according to claim 1, characterized in that: The garbage characteristics include garbage type and garbage volume. The initial driving route is adjusted according to the remaining garbage storage space of the robot and the garbage characteristics of each garbage to generate the target driving route of the robot, including: Determining target garbage that needs to be processed first among the multiple garbage according to the garbage type of each garbage; According to the location of the target garbage and the placement location of the target garbage, the initial driving route is adjusted to generate a first driving route of the robot; According to the remaining garbage storage space of the robot and the garbage volume of each garbage, determining whether the remaining garbage storage space of the robot meets the processing requirements when the robot processes each garbage according to the first driving route; If the remaining garbage storage space of the robot meets the processing requirements, the first driving route is used as the target driving route of the robot; if the remaining garbage storage space of the robot does not meet the processing requirements, the first driving route is adjusted to generate the target driving route of the robot.

4. The method for automatically sorting garbage by a domestic garbage robot according to claim 3 is characterized in that: If the remaining garbage storage space of the robot does not meet the processing requirement, adjusting the first driving route to generate a target driving route of the robot includes: Acquire the amount of garbage that the robot can process, and select a corresponding amount of garbage from the first driving route as garbage to be processed according to the amount of garbage that the robot can process; According to the location of the waste to be processed and the placement location of the waste to be processed, the first driving route is adjusted to generate a second driving route of the robot; The second driving route is used as the target driving route of the robot.

5. The method for automatically sorting garbage by a domestic garbage robot according to claim 1, characterized in that: The dynamic obstacle information includes a position, a moving speed, and a moving direction. The acquiring of the dynamic obstacle information in the target area, adjusting the target driving route according to the dynamic obstacle information, and generating a final driving route of the robot include: According to the position, the moving speed and the moving direction of the dynamic obstacle, predicting the position and time point at which the robot encounters the dynamic obstacle while traveling along the target driving route; Based on the position and time point, the target driving route is adjusted to obtain a final driving route of the robot.

6. The method for automatically sorting household garbage by a household garbage robot according to claim 5, characterized in that: The predicting, based on the position, the moving speed, and the moving direction of the dynamic obstacle, the position and time point at which the robot encounters the dynamic obstacle during the driving process along the target driving route includes: Obtaining the moving speed of the robot, and calculating the estimated time for the robot to reach each target point on the target driving route according to the moving speed of the robot and the target driving route; Calculating the position of the dynamic obstacle at different time points according to the position, the moving speed and the moving direction of the dynamic obstacle; According to the estimated time for the robot to reach each target point on the target driving route and the position of the dynamic obstacle at different time points, it is determined whether the position of the robot coincides with the position of the dynamic obstacle at any time point or the distance between the robot and the dynamic obstacle is less than a preset safety distance; If the position of the robot coincides with the position of the dynamic obstacle at a first time point or the distance therebetween is less than the preset safety distance, the position and the first time point are determined as the position and time point at which the robot meets the dynamic obstacle.

7. The method for automatically sorting household garbage by a household garbage robot according to claim 1, characterized in that: After generating the final driving route of the robot, the method further includes: Monitoring the operating status of the robot, garbage distribution change information in the target area, and the current position of the robot; According to the current position of the robot, the operating status of the robot and the garbage distribution change information, the final driving route is optimized and the dynamic driving route of the robot is generated.

8. A domestic waste robot automatic garbage sorting device, characterized in that: The device comprises: a first acquisition module, a combination module, a second acquisition module, a first adjustment module and a second adjustment module; wherein, The first acquisition module is used to acquire the position of the robot in the target area, the positions of multiple garbage in the target area, and the placement positions of each garbage; The combining module is used to formulate an initial driving route of the robot by combining the position of the robot, the position of each piece of garbage and the placement position of each piece of garbage; The second acquisition module is used to acquire the remaining garbage storage space of the robot and the garbage characteristics of each garbage; The first adjustment module is used to adjust the initial driving route according to the remaining garbage storage space of the robot and the garbage characteristics of each garbage, and generate a target driving route for the robot; The second adjustment module is used to obtain dynamic obstacle information in the target area, adjust the target driving route according to the dynamic obstacle information, and generate a final driving route of the robot, so that the robot can process the garbage in the target area according to the final driving route.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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