Logistics carrying control method based on intelligent navigation and multi-terrain adaptation system

Abstract

The invention discloses a logistics carrying control method based on intelligent navigation and a multi-terrain adaptation system. The method comprises the following steps: acquiring a starting point position and an ending point position corresponding to goods carried by a carrying device, and road surface information of a target road; determining a driving mode of the carrying device based on the road surface type; and controlling the transport device to travel from the starting point position to the ending point position along the target road based on the travel mode and the obstacle information. The technical problem that in the prior art, logistics goods carrying flexibility is poor is solved.

Description

Technical Field

[0001] This invention relates to the field of logistics automation, and more specifically, to a logistics handling control method based on an intelligent navigation and multi-terrain adaptation system. Background Technology

[0002] In modern supply chain management and logistics, efficient material handling and transportation are key elements for ensuring smooth logistics, reducing costs, and improving service quality. With the rapid development of e-commerce, logistics demands are becoming increasingly diversified and customized, especially in urban distribution, large warehouse management, and industrial production lines. These areas place higher demands on logistics handling equipment, requiring not only rapid and accurate cargo transfer but also the ability to cope with complex and ever-changing operating environments. However, current logistics handling technologies lack flexibility, resulting in lower efficiency in complex and variable operating environments.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a logistics handling control method based on an intelligent navigation and multi-terrain adaptation system, which at least solves the technical problem of poor flexibility in the handling of logistics goods in related technologies.

[0005] According to one aspect of the present invention, a logistics handling control method based on an intelligent navigation and multi-terrain adaptation system is provided, comprising: acquiring the starting point position, the ending point position, and the road surface information of the target road corresponding to the goods being handled by the handling device, wherein the target road represents the road from the starting point position to the ending point position, and the road surface information includes at least the road surface type and obstacle information; determining the driving mode of the handling device based on the road surface type; and controlling the handling device to travel from the starting point position to the ending point along the target road based on the driving mode and obstacle information.

[0006] Furthermore, based on the road surface type, the driving mode of the transport device is determined, including: when the road surface type is a preset type, the driving mode is determined to be a first preset mode, wherein the first preset mode is used to characterize the mode in which the transport device uses foldable multi-section stair-climbing wheels to travel; when the road surface type is not a preset type, the driving mode is determined to be a second preset mode, wherein the second preset mode is used to characterize the mode in which the transport device uses a tracked walking mechanism to travel.

[0007] Furthermore, based on the driving mode and obstacle information, controlling the transport device to travel from the starting point to the destination along the target road includes: obtaining the current position of the transport device; determining the target driving path between the current position and the destination based on obstacle information; and controlling the transport device to travel to the destination according to the target driving path based on the driving mode.

[0008] Furthermore, obstacle information includes: the location of obstacles; based on the obstacle information, determining the target travel path of the transport device from the current location to the destination location includes: based on the path planning algorithm, determining the initial travel path of the transport device from the current location to the destination location; inputting the location of obstacles and the initial travel path into the path adjustment model, and using the path adjustment model to adjust the initial travel path according to the location of obstacles to obtain the target travel path.

[0009] Furthermore, the handling device is equipped with a gripping device; the method also includes: controlling the handling device to travel to the placement position of the goods; acquiring image data of the goods when the handling device reaches the placement position; inputting the image data into an image analysis model, analyzing the image data using the image analysis model to obtain the physical properties of the goods; and controlling the gripping device to grip the goods based on the physical properties.

[0010] Furthermore, the method also includes: monitoring the remaining power and operating status of the transport device during its operation, wherein the operating status is used to characterize whether the transport device is transporting goods; and charging the transport device when the remaining power is less than the power threshold and the transport device is not transporting goods.

[0011] Further, charging the transport device includes: obtaining at least one preset charging location based on a map database; determining a target charging location from the at least one preset charging location based on the current location of the transport device, wherein the travel distance between the target charging location and the current location is less than the travel distance between the current location and the other preset charging locations (excluding the target charging location) among the at least one preset charging locations; determining a charging travel path based on the current location and the target charging location, and controlling the transport device to travel to the target charging location based on the charging travel path; and charging the transport device when it reaches the target charging location.

[0012] According to another aspect of the present invention, a logistics handling control device based on an intelligent navigation and multi-terrain adaptation system is also provided, comprising: a first acquisition module, configured to acquire the starting position and ending position of the goods being handled by the handling device, and the road surface information of the target road, wherein the target road represents the road from the starting position to the ending position, and the road surface information includes at least the road surface type and obstacle information; a mode determination module, configured to determine the driving mode of the handling device based on the road surface type; and a first control module, configured to control the handling device to travel from the starting position to the ending position along the target road based on the driving mode and obstacle information.

[0013] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0018] In this embodiment of the invention, the method involves acquiring the starting and ending positions of the goods being transported by the transport device, as well as the road surface information of the target road; determining the driving mode of the transport device based on the road surface type; and controlling the transport device to travel from the starting position to the ending position along the target road based on the driving mode and obstacle information. By acquiring the starting and ending positions, as well as the road surface information of the target road, rigorous data support can be provided for subsequent path planning. Furthermore, based on the road surface type of the target road, the transport device can flexibly switch between different driving modes, improving its adaptability to different types of road surfaces. Finally, by controlling the transport device's movement based on the driving mode and obstacle information, barrier-free movement of the transport device in complex logistics scenarios can be ensured, achieving the goal of flexibly switching the transport device's driving mode and planning obstacle avoidance paths. This achieves the technical effect of increasing the flexibility of logistics goods transport, thereby solving the technical problem of poor flexibility in logistics goods transport in related technologies. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0020] Figure 1This is a flowchart of a logistics handling control method based on an intelligent navigation and multi-terrain adaptation system according to an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of a logistics handling control device based on an intelligent navigation and multi-terrain adaptation system according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] According to an embodiment of the present invention, an embodiment of a logistics handling control method based on an intelligent navigation and multi-terrain adaptation system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a flowchart of a logistics handling control method based on an intelligent navigation and multi-terrain adaptation system according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0026] Step S102: Obtain the starting point position, ending point position, and road surface information of the target road corresponding to the goods being transported by the transport device. The target road represents the road from the starting point position to the ending point position, and the road surface information includes at least the road surface type and obstacle information.

[0027] The aforementioned handling device can be used to move and transport goods. It can bear a certain weight and can achieve efficient movement on different surfaces through different walking mechanisms. It can also include functions such as gripping and placing to achieve automated handling operations.

[0028] The aforementioned starting point location can be the geographical location of the handling device at the start of the handling operation, and may include, but is not limited to: the location where the goods are stored, the location where goods are waiting to be loaded, etc.

[0029] The aforementioned endpoint can be the final geographical location that the handling equipment needs to reach when the handling operation is completed, and may include, but is not limited to: the delivery destination of the goods, the warehouse area where the goods need to be unloaded, etc.

[0030] The aforementioned target road can refer to the road traversed by the transport device from the starting point to the ending point.

[0031] The aforementioned road surface information can be information about specific attributes and characteristics of the target road, including but not limited to: road surface material, smoothness, humidity, coefficient of friction, etc., which is crucial for the selection of a suitable travel mode for the transport device.

[0032] The aforementioned road surface types can be different materials and structures on the target road, including but not limited to: cement ground, asphalt road, grass, sand, mud, stairs, etc. Different types of roads require the transport device to have different adaptability and travel strategies.

[0033] The aforementioned obstacle information can refer to any entity or phenomenon that may exist on the target road that obstructs the normal movement of the transport device, including static obstacles (such as walls, pillars, and stacks of goods) and dynamic obstacles (such as pedestrians and other vehicles). This information is crucial for the design of the obstacle avoidance algorithm for the transport device and the planning of the transport route.

[0034] In one optional embodiment, considering that the starting and ending points of the transported goods are the basis for formulating the transport path, without information on these two locations, the logistics transport control system (hereinafter referred to as the control system) cannot clearly define the specific objectives of the transport task, and therefore cannot proceed with subsequent path planning and navigation. Therefore, the control system can first obtain the starting and ending points of the transported goods. Furthermore, considering that different road surface types (such as cement, grass, sand, stairs, etc.) have different requirements for the transport device's driving capabilities, and that obstacle information on the road surface also affects the construction of the driving path, understanding the road surface type and obstacle information can help the control system select a more suitable driving mode and formulate a more efficient driving path to ensure that the transport device can drive smoothly and efficiently on various terrains. Therefore, the control system can also obtain the road surface type and obstacle information of the road from the starting point to the ending point, thereby obtaining the road surface information of the aforementioned target road. The aforementioned starting point, ending point, and road information together form the basis of intelligent navigation for the handling device. This enables the handling device to autonomously plan the best handling path and select the most suitable operating mode in complex logistics environments, based on real-time road conditions and dynamic obstacles. This not only improves handling efficiency but also greatly enhances the safety and reliability of operations.

[0035] For example, the control system can utilize RFID (Radio Frequency Identification) readers and GPS (Global Positioning System) positioning systems installed at the entrances of various buildings, inside warehouses, and in work areas to obtain the starting and ending points. Specifically, when goods with RFID tags enter or leave these areas, the control system can automatically record the location information of the goods, thereby determining the starting and ending points. Simultaneously, the control system can also receive specific locations input by staff via handheld devices or the management system, storing these locations as the ending points in a database. Furthermore, the control system can leverage a sensor network distributed throughout the logistics park, including but not limited to infrared sensors, ultrasonic sensors, and cameras, to monitor road conditions in real time, collecting information on road surface types (such as cement, asphalt, grass, mud, etc.) and obstacles (such as other vehicles, pedestrians, and temporarily stacked goods). This sensor network can transmit the collected data to the control system, which can then process the received data using intelligent analysis algorithms to generate real-time road surface information for the target road.

[0036] For example, staff can input the specific coordinates of the starting and ending points through the equipment's operating interface or a mobile application. These coordinates can directly obtain precise latitude and longitude information via the map API (Application Programming Interface) and transmit it to the control system to generate the starting and ending points. Simultaneously, the control system can pre-set fixed station locations within the park, such as warehouse entrances and unloading areas. In actual application, these preset stations can be directly selected as the starting or ending points. Subsequently, the control system can access a pre-built park map database, which records detailed attribute information for each road within the park, including road surface type, width, and maximum load-bearing capacity. Furthermore, the control system can use the lidar and vision cameras mounted on the handling device to monitor the actual road conditions ahead in real time during movement, updating the obstacle information for the corresponding road segments in the database, thereby constructing real-time and accurate road surface information.

[0037] For example, the control system can analyze historical handling records to predict the probabilities of the start and end points of the next handling task. Combining this with real-time handling information and inventory status, it can automatically calculate the most likely start and end points, thereby reducing user input and avoiding human error. Subsequently, the control system can combine historical data and machine learning models to predict the types of road surfaces and obstacles that may appear on the target road. It also utilizes the environmental sensing devices built into the handling equipment to continuously collect environmental data and adjust the prediction results in real time, ensuring the accuracy of the handling route and strategy.

[0038] Step S104: Determine the driving mode of the transport device based on the road surface type.

[0039] The aforementioned driving modes refer to the specific driving methods adopted by the transport device according to different road surface types and working environments. The transport device can switch between different driving modes through automatic switching or remote control switching.

[0040] In one alternative embodiment, considering that different road conditions have different requirements for the driving performance of the transport device, by determining a driving mode that is more suitable for the road type, it can be ensured that the transport device can operate efficiently and stably under various road conditions, avoiding unnecessary stops and energy waste, thereby improving the overall transport efficiency.

[0041] For example, in order to improve the response rate of the transport device to different road surface types, the control system can pre-set the mapping relationship between road surface types and driving modes. In actual application, the control system can quickly determine the driving mode suitable for driving on roads of that type based on the above road surface type and the mapping relationship, thereby improving the driving efficiency and driving stability of the transport device.

[0042] For example, in order to improve the adaptability of the transport device to rare road surface types, the control system can also display the road surface types to the staff through a graphical interface and receive control commands transmitted by the staff. Subsequently, the control system can determine the driving mode of the transport device based on the control commands, thereby improving the flexibility of the transport device in switching driving modes.

[0043] Step S016: Based on the driving mode and obstacle information, control the transport device to travel from the starting position to the destination position along the target road.

[0044] In one alternative embodiment, considering that switching driving modes can help the transport device adapt to different types of road surfaces, and obstacle information can help the transport device avoid obstacles during travel, thereby ensuring the safety of the transport device during travel, the control system can, based on the driving mode and obstacle information, control the transport device to travel from the starting point to the destination along the target road. This not only ensures the safety of transport but also greatly improves the efficiency and flexibility of transport.

[0045] In this embodiment of the invention, the method involves acquiring the starting and ending positions of the goods being transported by the transport device, as well as the road surface information of the target road; determining the driving mode of the transport device based on the road surface type; and controlling the transport device to travel from the starting position to the ending position along the target road based on the driving mode and obstacle information. By acquiring the starting and ending positions, as well as the road surface information of the target road, rigorous data support can be provided for subsequent path planning. Furthermore, based on the road surface type of the target road, the transport device can flexibly switch between different driving modes, improving its adaptability to different types of road surfaces. Finally, by controlling the transport device's movement based on the driving mode and obstacle information, barrier-free movement of the transport device in complex logistics scenarios can be ensured, achieving the goal of flexibly switching the transport device's driving mode and planning obstacle avoidance paths. This achieves the technical effect of increasing the flexibility of logistics goods transport, thereby solving the technical problem of poor flexibility in logistics goods transport in related technologies.

[0046] Furthermore, based on the road surface type, the driving mode of the transport device is determined, including: when the road surface type is a preset type, the driving mode is determined to be a first preset mode, wherein the first preset mode is used to characterize the mode in which the transport device uses foldable multi-section stair-climbing wheels to travel; when the road surface type is not a preset type, the driving mode is determined to be a second preset mode, wherein the second preset mode is used to characterize the mode in which the transport device uses a tracked walking mechanism to travel.

[0047] The aforementioned preset type can refer to a pre-defined road surface type, which may include, but is not limited to: stairs, irregular slopes, or other road surfaces that require vertical movement or climbing.

[0048] The aforementioned first preset mode can be a mode for efficient travel on preset type road surfaces. In this mode, the transport device can be activated and use folding multi-section stair-climbing wheels instead of ordinary wheels or tracks to achieve stable and efficient stair climbing.

[0049] The aforementioned folding multi-section stair-climbing wheel can be a walking mechanism consisting of multiple small wheels whose angles can be adjusted independently. It can adaptively adjust according to the height and width of the stairs, enabling the transport device to move efficiently on the stairs.

[0050] The aforementioned second preset mode can be a mode for efficient travel on non-preset road surfaces. In this mode, the transport device can use a tracked walking mechanism to provide better ground adaptability and load-bearing capacity.

[0051] The aforementioned tracked walking mechanism can be a walking assistance device that can increase the contact area between the handling device and the ground, thereby providing stronger traction and stability. It can effectively prevent the handling device from slipping or sinking into the ground, and can also carry heavier goods.

[0052] In one optional embodiment, considering that different road surface types place different demands on the driving performance of the handling device, by intelligently determining the road surface type and adjusting the driving mode, the handling device can flexibly cope with various working scenarios. Whether it is a flat cement floor in a warehouse, a complex outdoor terrain, or even indoor or outdoor stairs, it can find a suitable driving mode, thereby greatly enhancing the environmental adaptability of the handling device and improving the flexibility of goods handling. Specifically, when the road surface type is determined to be a preset type, such as stairs, the control system can switch the driving mode of the handling device to the first preset mode, that is, to use folding multi-section stair-climbing wheels for stair-climbing operations. The design of the stair-climbing wheels allows each wheel section to adjust its angle independently, thereby adapting to different step heights and shapes, ensuring that the equipment climbs the stairs smoothly and efficiently. Correspondingly, when the road surface type is not a preset type, such as uneven or flat road surfaces, the control system can switch the driving mode of the handling device to the second preset mode, using a tracked walking mechanism. The track design increases the contact area with the ground, improving the equipment's passability and stability on complex terrain, especially performing well on muddy and gravel roads. Through this intelligent switching strategy, the handling equipment can flexibly adapt to various working environments, improving operational efficiency and safety. At the same time, it reduces the need for manual intervention, realizing the automation and intelligence of handling operations, thereby effectively improving the flexibility of logistics handling.

[0053] For example, when a transport device enters a multi-story warehouse for operation, the control system can identify the ground type of the current floor. If the ground type is a preset type, i.e., there are stairs or other vertical structures that need to be climbed, the transport device will automatically determine the first preset travel mode and activate its folding multi-section stair-climbing wheels to smoothly climb the stairs. Conversely, if the ground type is a flat warehouse floor, which is not a preset type, the transport device will determine the second preset travel mode, i.e., it will use a tracked walking mechanism to ensure efficient and stable travel on flat ground.

[0054] Furthermore, based on the driving mode and obstacle information, controlling the transport device to travel from the starting point to the destination along the target road includes: obtaining the current position of the transport device; determining the target driving path between the current position and the destination based on obstacle information; and controlling the transport device to travel to the destination according to the target driving path based on the driving mode.

[0055] The aforementioned target travel path can refer to the optimal or feasible path calculated by the intelligent planning algorithm from the current position of the transport device, taking into account all known obstacle information. This path aims to avoid obstacles and ensure that the transport device can safely and efficiently reach the predetermined destination.

[0056] In one alternative embodiment, considering that understanding the current location of the handling device is fundamental for path planning, the control system, with the help of real-time positioning technology, can accurately determine the specific location of the handling device. This is crucial for constructing global path planning because, whether in an indoor warehouse or an outdoor logistics park, the handling device needs to calculate the optimal route to the destination based on its current location to avoid unnecessary detours and wasted time. Therefore, the control system can first obtain the current location of the handling device; the accuracy of this information is critical for subsequent path planning. Next, based on the acquired obstacle information, the control system can use advanced path planning algorithms to determine a superior target travel path from the current location of the handling device to the destination. This path planning fully considers terrain features, obstacle distribution, and the driving performance of the handling device, thereby ensuring the feasibility and safety of the path. Finally, the control system can, according to the selected driving mode, control the handling device to strictly follow the target travel path towards the destination. During this process, the control system can also continuously monitor the obstacle status and dynamically adjust the path to avoid sudden obstacles, ensuring the smooth progress of the handling task. The above steps, through precise positioning, intelligent path planning, and flexible driving mode control, significantly improve handling efficiency and safety, and effectively enhance the flexibility of handling devices for cargo handling in complex environments.

[0057] For example, within a logistics park, when a handling device receives a task to move goods from warehouse A to warehouse B, the control system first uses the device's built-in GPS system to determine its exact location within warehouse A. Then, based on obstacle information collected by lidar and vision cameras deployed on the device, the control system calculates a shortest route that avoids other vehicles and personnel using a path planning algorithm. Finally, according to a predetermined driving mode, the control system automatically guides the handling device along the calculated route, smoothly and safely moving the goods from warehouse A to warehouse B. During this process, the control system can intervene at any time to adjust the handling device's speed and direction to accommodate potential changes in dynamic obstacles, ensuring the smooth operation of the entire handling process.

[0058] Furthermore, obstacle information includes: the location of obstacles; based on the obstacle information, determining the target travel path of the transport device from the current location to the destination location includes: based on the path planning algorithm, determining the initial travel path of the transport device from the current location to the destination location; inputting the location of obstacles and the initial travel path into the path adjustment model, and using the path adjustment model to adjust the initial travel path according to the location of obstacles to obtain the target travel path.

[0059] The aforementioned path planning algorithm can refer to an algorithm that calculates a better path from the current location to the destination location based on map data, given the current location and the destination location. This algorithm may include, but is not limited to: A Algorithms such as A-Star, Dijkstra, and RRT (Rapidly-exploring Random Trees) are used.

[0060] The initial travel path mentioned above can be an ideal path from the current position of the transport device to the destination position calculated by the path planning algorithm without taking into account the position of obstacles.

[0061] The aforementioned path adjustment model can be a mathematical model or a machine learning model, used to adjust the initial travel path after taking obstacle information into account. The model can dynamically evaluate the position of obstacles on the path and calculate an alternative path to bypass the obstacles based on their position and characteristics, so as to ensure that the transport device can reach its destination safely and efficiently.

[0062] In one optional embodiment, considering that the handling device may encounter various static and dynamic obstacles in the working environment, such as other handling devices, employees, fixed obstacles, and stacked goods, accurately obtaining the positions of these obstacles is a prerequisite for avoiding collisions and ensuring the safety of the handling device and personnel. Therefore, the final target travel path needs to fully consider the aforementioned obstacle information. Based on this, the control system can first use a path planning algorithm to calculate the initial travel path of the handling device from its current position to its destination. This path only considers travel efficiency and does not cover the aforementioned obstacle information. Subsequently, the control system can input the obstacle positions and the initial travel path as input parameters into the path adjustment model. This model can use an algorithm to adjust the initial travel path in real time according to the actual positions of the obstacles, thereby generating a target travel path that avoids obstacle interference. This series of operations ensures that the handling device can promptly identify and avoid potential obstacles when facing complex environments, maintaining a smooth and safe operating state, and significantly improving operational efficiency and safety in dynamic environments.

[0063] For example, in a material handling scenario within a logistics park, the handling device starts from warehouse A and its destination is warehouse B. The path planning algorithm first determines an initial travel path from warehouse A to warehouse B. However, an unexpected forklift suddenly appears as an obstacle within the park. The forklift's position is captured in real time by the handling device's built-in LiDAR and vision camera. The obstacle's position and the initial travel path are input into the path adjustment model. Based on the obstacle's position, the model automatically adjusts the initial travel path to generate the target travel path, thereby avoiding the obstacle and ensuring that the handling device can safely and smoothly reach its destination.

[0064] Furthermore, the handling device is equipped with a gripping device; the method also includes: controlling the handling device to travel to the placement position of the goods; acquiring image data of the goods when the handling device reaches the placement position; inputting the image data into an image analysis model, analyzing the image data using the image analysis model to obtain the physical properties of the goods; and controlling the gripping device to grip the goods based on the physical properties.

[0065] The aforementioned gripping equipment can be used to automatically adjust the gripping method according to the shape and size of the goods, so as to achieve precise gripping and placement of the goods. It may include, but is not limited to: multi-joint gripping arms and adaptive gripping jaws, electromagnetic grippers, etc.

[0066] The aforementioned image data can be cargo image information captured by a camera or other image acquisition device, and may include, but is not limited to, the appearance characteristics of the cargo, such as shape, size, color, texture, etc., which form the basis for the image analysis model to perform analysis.

[0067] The aforementioned image analysis model can be an algorithm model based on machine learning or deep learning, capable of processing and parsing image data, and identifying physical attributes of goods such as size, weight, and center position, providing necessary reference data for subsequent grasping operations.

[0068] The aforementioned physical attributes can be the actual physical characteristics of the goods output by the image analysis model, including but not limited to: the size, shape, center of gravity, and weight of the goods, which can determine how to safely and effectively grasp and transport the goods.

[0069] In one optional embodiment, considering that different goods possess different physical properties, in order for the handling device to adopt corresponding gripping methods based on different goods to avoid damage, the control system needs to formulate corresponding gripping control parameters based on the physical properties of the goods. Specifically, when the handling device travels to the placement position of the goods, the image acquisition device on the handling device can automatically collect image data of the goods. This image data is then input into a pre-trained image analysis model, which can quickly analyze the input image data, accurately identify and interpret the physical properties of the goods, including but not limited to the size, shape, and weight of the goods. Subsequently, the control system can intelligently adjust the gripping device's gripping force, angle, and position control parameters based on the physical properties derived from the model analysis, thereby achieving precise gripping of the goods. This design not only improves handling efficiency but also ensures the safety and integrity of the goods during the gripping process, reducing damage caused by improper gripping. Furthermore, through automated and intelligent control of the gripping process, the handling device can operate efficiently in complex and ever-changing logistics environments, reducing reliance on manual intervention and improving the operational quality and safety of the entire logistics system.

[0070] For example, after receiving a cargo handling instruction from the control system, the handling device automatically travels to the designated cargo placement location. Upon arrival, a high-definition camera on top of the device captures image data of the cargo. This image data is then transmitted to an image analysis model for analysis. The model identifies the cargo's physical attributes, such as size, weight, and shape, and feeds this information back to the control system. Based on these physical attributes, the control system precisely calculates the gripping angle and force, thus constructing the aforementioned gripping control parameters. This ensures that the gripping device in the handling device can smoothly grasp the cargo and stably place it on the handling platform, preparing it for the next stage of transportation. Throughout the entire gripping process, the handling device requires no manual intervention, achieving automated gripping and placement, significantly improving the flexibility of cargo handling.

[0071] Furthermore, the method also includes: monitoring the remaining power and operating status of the transport device during its operation, wherein the operating status is used to characterize whether the transport device is transporting goods; and charging the transport device when the remaining power is less than the power threshold and the transport device is not transporting goods.

[0072] In one optional embodiment, considering that if the handling device stops working due to power depletion during a handling task, it will interrupt the handling process, leading to operational delays and potentially triggering a series of chain reactions, such as production line shutdowns and extended waiting times, ultimately affecting the smooth operation of the entire logistics system, the control system can take action when the power level drops to a threshold by monitoring the remaining power in real time to prevent the handling device from shutting down due to insufficient power. Furthermore, considering that forced charging might interrupt the handling process and increase additional energy consumption if the handling device is currently handling goods, charging is more ideal when the handling device is idle or not in a handling state. Based on this, the control system can also monitor the remaining power and operating status of the handling device. When the handling device is not in a handling state and the power level is below a preset threshold, the control system can automatically trigger a charging process to charge the handling device, thereby ensuring that the handling device can replenish its energy in a timely manner when there is no handling task, avoiding disruption to task execution due to insufficient power. This mechanism not only improves the efficiency of the handling device but also reduces unplanned downtime caused by poor power management, enhancing the stability and reliability of the handling process.

[0073] For example, in logistics handling operations, after a handling device completes the delivery of a shipment, the control system automatically monitors the device's remaining battery power. Assuming the remaining battery power is A, which is lower than a preset battery threshold B, and the device is idle and not currently handling any goods, the control system can immediately initiate a charging process, dispatching the device to the nearest charging area to replenish its power and ensure sufficient charge for the next delivery. Once the device enters the charging area, the charging system automatically identifies it and begins charging until the battery power is restored to normal operating levels. The device then returns to standby mode, awaiting the next task. This process requires no manual intervention, ensuring the continuity and efficiency of the handling operations.

[0074] Further, charging the transport device includes: obtaining at least one preset charging location based on a map database; determining a target charging location from the at least one preset charging location based on the current location of the transport device, wherein the travel distance between the target charging location and the current location is less than the travel distance between the current location and the other preset charging locations (excluding the target charging location) among the at least one preset charging locations; determining a charging travel path based on the current location and the target charging location, and controlling the transport device to travel to the target charging location based on the charging travel path; and charging the transport device when it reaches the target charging location.

[0075] The aforementioned map database can be a database used to store detailed geographic information of the entire working area of ​​the handling equipment. It can cover the location and layout of all roads, buildings, and fixed facilities, as well as dynamic information related to handling operations, such as cargo stacking points and employee activity areas. It is the basis for realizing automatic navigation and path planning of the handling equipment.

[0076] The aforementioned preset charging locations can be within the work area. To ensure that the transport device can replenish its energy at any time, some charging stations are pre-set at these locations. Charging infrastructure such as charging sockets and wireless charging pads can be provided at these preset charging locations so that the transport device can be charged via wired or wireless means.

[0077] The aforementioned target charging location can be a preset charging location that is close to the current location and is read from the map database when the battery level of the conveying device is detected to be lower than the battery threshold. This ensures that the conveying device can reach the charging location within the shortest possible distance, thereby reducing the downtime caused by charging.

[0078] The aforementioned charging path can be considered the optimal route from the current location of the transport device to the target charging location. Specifically, the path planning algorithm can take into account traffic rules, obstacle distribution, and the travel routes of other transport devices within the work area to avoid collisions and congestion. To improve the accuracy of the charging path construction, it can be based on real-time environmental information and pre-stored map data, ensuring that the transport device can smoothly and safely reach the target charging location.

[0079] In one optional embodiment, by calculating the distance from the current position of the transport device to each preset charging location and selecting the preset charging location with the shortest distance as the target charging location, it can be ensured that the transport device can charge quickly, thereby shortening the total transport downtime caused by the transport device charging. Therefore, the control system can obtain multiple preset charging locations based on a map database and, using a high-precision navigation module, intelligently analyze and select the target charging location closest to the current position of the transport device, minimizing the time it takes for the transport device to travel from the current position to the target charging location. Subsequently, the control system can automatically plan an optimal charging route based on the current position and the target charging location, guiding the transport device to autonomously travel to the target charging location. Upon arrival at the target charging location, the transport device can automatically start the charging process, for example, by using wireless charging technology to replenish power or by automatically docking with the charging plug at the target charging location for wired fast charging. This series of operations not only achieves autonomous energy management of the transport device but also significantly improves charging efficiency, ensuring the continuous operation capability of the transport device during high-intensity operations. In addition, by intelligently selecting charging locations and planning charging paths, the handling device can quickly restore energy during work breaks without manual intervention, saving valuable operating time and reducing equipment maintenance and management costs.

[0080] For example, in a large logistics park, after completing a series of handling tasks, the control system identifies three preset charging locations A, B, and C based on a map database. The handling device is currently located at a designated position in the warehouse area. The control system calculates the travel distance from the current location to each of the three preset charging locations (A, B, and C). Finding that the distance to preset charging location B is the shortest, the control system plans a charging path based on the current location and preset charging location B, and controls the handling device to automatically travel along this path to preset charging location B. Upon arrival at preset charging location B, a wireless charging pad can be deployed there, automatically initiating the wireless charging process to ensure the device can continue operating efficiently after being fully charged.

[0081] According to an embodiment of the present invention, a logistics handling control device based on an intelligent navigation and multi-terrain adaptation system is provided. It should be noted that this device can be used to execute the aforementioned logistics handling control method based on an intelligent navigation and multi-terrain adaptation system. The specific implementation process and application scenarios are the same as those in the above embodiment, and will not be repeated here. Figure 2 This is a schematic diagram of a logistics handling control device based on an intelligent navigation and multi-terrain adaptation system according to an embodiment of the present invention, such as... Figure 2 As shown, the device includes:

[0082] The first acquisition module 202 is used to acquire the starting position, the ending position, and the road surface information of the target road corresponding to the goods being transported by the transport device. The target road represents the road from the starting position to the ending position, and the road surface information includes at least the road surface type and obstacle information.

[0083] The mode determination module 204 is used to determine the driving mode of the transport device based on the road surface type.

[0084] The first control module 206 is used to control the transport device to travel from the starting position to the destination position along the target road based on the driving mode and obstacle information.

[0085] Furthermore, the mode determination module is also used to: determine the driving mode as a first preset mode when the road surface type is a preset type, wherein the first preset mode is used to characterize the mode in which the transport device uses foldable multi-section stair-climbing wheels to travel; and determine the driving mode as a second preset mode when the road surface type is not a preset type, wherein the second preset mode is used to characterize the mode in which the transport device uses a tracked walking mechanism to travel.

[0086] Furthermore, the first control module is also used to: obtain the current position of the transport device; determine the target travel path of the transport device from the current position to the destination position based on obstacle information; and control the transport device to travel towards the destination position according to the target travel path based on the travel mode.

[0087] Furthermore, the obstacle information includes: the location of the obstacle; the first control module is also used to: determine the initial travel path of the transport device from the current position to the destination position based on the path planning algorithm; input the location of the obstacle and the initial travel path into the path adjustment model, and use the path adjustment model to adjust the initial travel path according to the location of the obstacle to obtain the target travel path.

[0088] Furthermore, the handling device is equipped with a gripping device; the device also includes: a second control module for controlling the handling device to travel to the placement position of the goods; a second acquisition module for acquiring image data of the goods when the handling device reaches the placement position; an image analysis module for inputting the image data into an image analysis model and using the image analysis model to analyze the image data to obtain the physical properties of the goods; and a gripping control module for controlling the gripping device to grip the goods based on the physical properties.

[0089] Furthermore, the device also includes: a first monitoring module, used to monitor the remaining power and working status of the transport device during its operation, wherein the working status is used to indicate whether the transport device is transporting goods; and a charging control module, used to charge the transport device when the remaining power is less than the power threshold and the transport device is not transporting goods.

[0090] Furthermore, the charging control module is also used to: obtain at least one preset charging location based on a map database; determine a target charging location from the at least one preset charging location based on the current location of the transport device, wherein the travel distance between the target charging location and the current location is less than the travel distance between the current location and the other preset charging locations (excluding the target charging location) among the at least one preset charging locations; determine a charging travel path based on the current location and the target charging location, and control the transport device to travel to the target charging location based on the charging travel path; and charge the transport device when it reaches the target charging location.

[0091] Embodiments of the present invention also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0092] Embodiments of the present invention also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0093] Embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0094] Embodiments of the present invention also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the methods of various embodiments of the present invention.

[0095] Embodiments of the present invention also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0096] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0097] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0099] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0100] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0101] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0102] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A logistics handling control method based on intelligent navigation and multi-terrain adaptation system, characterized in that, include: The starting point and ending point of the goods being transported by the transport device, as well as the road surface information of the target road, are obtained. The target road represents the road from the starting point to the ending point, and the road surface information includes at least the road surface type and obstacle information. The driving mode of the transport device is determined based on the road surface type; Based on the driving mode and the obstacle information, the transport device is controlled to travel from the starting position to the destination position along the target road.

2. The logistics handling control method based on intelligent navigation and multi-terrain adaptation system according to claim 1, characterized in that, Based on the road surface type, the driving mode of the transport device is determined, including: When the road surface type is a preset type, the driving mode is determined to be a first preset mode, wherein the first preset mode is used to characterize the mode in which the transport device uses foldable multi-section stair-climbing wheels to travel; When the road surface type is not the preset type, the driving mode is determined to be the second preset mode, wherein the second preset mode is used to characterize the mode in which the transport device uses a tracked walking mechanism to travel.

3. The logistics handling control method based on intelligent navigation and multi-terrain adaptation system according to claim 1, characterized in that, Based on the driving mode and the obstacle information, controlling the transport device to travel from the starting position to the destination position along the target road includes: Obtain the current position of the conveying device; Based on the obstacle information, the target travel path of the transport device from the current position to the destination position is determined; Based on the driving mode, the transport device is controlled to travel towards the destination location according to the target driving path.

4. The logistics handling control method based on intelligent navigation and multi-terrain adaptation system according to claim 3, characterized in that, The obstacle information includes: the location of the obstacle; based on the obstacle information, determining the target travel path of the transport device from the current location to the destination location includes: Based on the path planning algorithm, the initial travel path of the transport device from the current position to the destination position is determined; The location of the obstacle and the initial driving path are input into the path adjustment model. The path adjustment model is then used to adjust the initial driving path based on the location of the obstacle to obtain the target driving path.

5. The logistics handling control method based on intelligent navigation and multi-terrain adaptation system according to claim 1, characterized in that, The conveying device is equipped with a gripping device; the method further includes: Control the transport device to travel to the placement position of the goods; When the transport device reaches the placement position, image data of the goods is acquired; The image data is input into an image analysis model, and the image analysis model is used to analyze the image data to obtain the physical properties of the goods. Based on the physical properties, the grasping device is controlled to grasp the goods.

6. The logistics handling control method based on intelligent navigation and multi-terrain adaptation system according to any one of claims 1 to 5, characterized in that, The method further includes: During the operation of the conveying device, the remaining power and working status of the conveying device are monitored, wherein the working status is used to indicate whether the conveying device is carrying out cargo conveying; If the remaining power is less than the power threshold and the handling device is not handling goods, the handling device is charged.

7. The logistics handling control method based on intelligent navigation and multi-terrain adaptation system according to claim 6, characterized in that, Charging the conveying device includes: Based on the map database, obtain at least one preset charging location; Based on the current position of the transport device, a target charging position is determined from the at least one preset charging position, wherein the travel distance between the target charging position and the current position is less than the travel distance between the current position and the other preset charging positions (excluding the target charging position) among the at least one preset charging positions. Based on the current location and the target charging location, a charging travel path is determined, and based on the charging travel path, the transport device is controlled to travel to the target charging location; When the transport device reaches the target charging position, the transport device is charged.

8. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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