Intelligent warehousing logistics automated handling robot

Abstract

The present application relates to the technical field of carrying robot, in particular to an intelligent warehouse logistics automated carrying robot, which comprises a scraper and a suction cup on the robot body, the scraper is equipped with specially designed pressure groove teeth, which can form an arc-shaped groove with guiding function on the surface of the box, and the design ensures that the frost layer is effectively removed along the predetermined path. The system collects temperature gradient, surface topography and contact stress data in real time through the integrated multi-modal environment perception unit, and the intelligent decision-making unit establishes a dynamic control model based on these data to realize accurate evaluation and adaptive processing of the frost layer state. The complete system builds a complete closed-loop control chain, and the present application overcomes the limitations of traditional defrosting methods, improves the working efficiency while maintaining the stability of adsorption, and is especially suitable for low-temperature warehouse environment below-25 DEG C, providing a reliable solution for cold-chain logistics automation.

Description

Technical Field

[0001] This invention relates to the field of handling robot technology, and more specifically, to an intelligent warehousing and logistics automated handling robot. Background Technology

[0002] With the rapid development of pharmaceutical cold chain logistics, the demand for automated handling equipment in the storage and transportation of sensitive materials such as vaccines and pharmaceuticals is becoming increasingly prominent. Automated Guided Vehicles (AGVs), with their advantages of autonomous navigation, efficient operation, and flexible deployment, have become core equipment in modern intelligent warehousing. However, in low-temperature storage environments below -25°C, traditional handling robots face technical challenges such as insufficient gripping stability due to condensation and frost formation. Existing technologies generally use vacuum suction cups as the primary gripping device, supplemented by electrically heated defrosting devices to address this issue.

[0003] While existing technologies have addressed the challenges of handling materials in low-temperature environments to some extent, there are still shortcomings in the adaptive performance of the gripping devices. Specifically, when the frost layer on the box surface is uneven, existing gripping mechanisms cannot detect changes in the frost layer in real time and automatically adjust the clamping parameters, leading to localized air leakage and failure of the vacuum suction cup, making it difficult to maintain a stable suction force. Summary of the Invention

[0004] This invention provides an intelligent automated handling robot for warehousing and logistics. Through a smart decision-making unit's multi-dimensional real-time perception of frost conditions and a three-level adaptive control strategy, combined with the arc-shaped groove guidance design of the scraper and the dynamic compensation adjustment of the suction cup, it solves the problems mentioned in the background art, namely:

[0005] When the frost layer on the surface of the box is uneven, the existing gripping mechanism cannot sense the changes in the frost layer in real time and automatically adjust the clamping parameters, which makes the vacuum suction cup prone to local air leakage and failure, making it difficult to maintain a stable adsorption force.

[0006] To achieve the above objectives, the handling robot includes a robot body, which is fixed to a base via a robotic arm. The base is equipped with a controllable, extendable suction cup. The robot body also integrates an environmental sensing unit that collects multimodal environmental feature sets. Furthermore, it includes:

[0007] A scraper, which is rotatable and retractable and positioned above the suction cup on the base;

[0008] The scraper has several pressing teeth on the opposite side of the base, which are used to press out grooves on the frost layer on the surface of the box to guide the frost layer to fall off. The rotation diameter of the scraper is greater than the length of the line segment from the scraper axis to the bottom of the box, so that the groove forms a semi-circular arc and the bottom opening faces downwards towards the box.

[0009] The intelligent decision-making unit receives and analyzes a multimodal environmental feature set, generates a frost layer area classification assessment strategy, establishes a frost layer status assessment model, and divides the work surface into different level areas.

[0010] The intelligent decision-making unit generates a dynamic operation control strategy based on the frost layer area classification assessment strategy, and generates differentiated mechanical control parameters based on different level areas.

[0011] The intelligent decision-making unit generates an adsorption compensation strategy based on a dynamic operation control strategy, establishes a contact surface deformation prediction model, and dynamically levels the contact surface of the suction cup for the robot to stably transport the box.

[0012] The above technical solution addresses two key issues of traditional defrosting methods through the collaborative design of the scraper and suction cup: if only the suction cup is used, a thick frost layer will result in poor adhesion, and uneven frost thickness will inevitably lead to localized air leakage; if only a regular scraper is used, although it can remove frost, frost in unscraped areas will still fall off and interfere with the suction cup's operation. This invention innovatively designs the scraper diameter to be larger than the center distance of the housing, allowing the arc-shaped groove formed by the pressure teeth to serve both guiding and isolating functions: actively guiding frost layer detachment while physically isolating and protecting the adsorption area. The intelligent decision-making unit's three-level control strategy overcomes the shortcomings of traditional fixed-parameter operation modes: the first level, area assessment, solves the problem of perception blind spots and avoids resource waste; the second level, dynamic operation, implements precise processing for different frost layer states, preventing insufficient or excessive processing caused by the scraper's one-size-fits-all approach; and the third level, adsorption compensation, bridges the control gap between mechanical action and adsorption stability.

[0013] Based on this, a silicon-based piezoresistive pad is installed on the large circular outline, and the silicon-based piezoresistive pad is connected to the second servo electric cylinder through a buffer assembly. The suction cup is symmetrically fixed to the end of the second servo electric cylinder.

[0014] In another technical solution, the contact center area of ​​the vacuum suction cup is provided with an adsorption area, and its outer edge maintains a preset distance from the groove formed by the pressure teeth, which is used to isolate the working area of ​​the groove from the adsorption area.

[0015] This technical solution overcomes the rigidity limitations of traditional adsorption systems through the linkage design of a silicon-based piezoresistive pad and a servo electric cylinder. Simply increasing the adsorption pressure can lead to deformation of the chamber; directly connecting the buffer component without it can not absorb mechanical vibrations during operation, affecting adsorption accuracy. This invention innovatively connects the silicon-based piezoresistive pad to a second servo electric cylinder via a buffer component, forming a three-stage flexible adjustment system. The spacing design between the adsorption point and the trench resolves the contradiction of "defrosting interfering with adsorption" in traditional solutions: ensuring the trench's frost drainage function while maintaining a complete sealed interface at the adsorption point through precise spatial isolation.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] The core breakthrough of this invention lies in the construction of a frost treatment system with autonomous evolution capabilities. Its innovatively designed arc-shaped grooves not only serve as physical channels for frost removal but also establish a quantitative mapping relationship between geometric parameters and frost flowability. The three-level strategy of the intelligent decision-making unit essentially constructs a dynamically adjustable "mechanical-environment" coupling model, enabling the system to adapt to frost layers in different crystallization states. Furthermore, the synergistic effect of the silicon-based piezoresistive pad and the servo mechanism achieves, for the first time in a mechanical system, a "perception-response" closed-loop mechanism similar to that of biological tissue. This design paradigm, which deeply couples physical structure with control algorithms, allows the system to exhibit agent-like adaptability when dealing with non-uniform frost layers, fundamentally changing the limitations of traditional defrosting technology that relies on empirical parameters and providing a new technical path for robotic operations in extreme environments. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall three-dimensional structure of the handling robot of the present invention;

[0019] Figure 2 This is a schematic diagram of the overall process structure of the present invention;

[0020] Figure 3 This is a side view of the suction cup mechanism and the frost scraping mechanism of the present invention.

[0021] Figure 4 This is a top view schematic diagram of a portion of the suction cup buffer mechanism of the present invention;

[0022] Figure 5 This is a schematic diagram of the scraper structure of the present invention;

[0023] Figure 6 This is a schematic diagram of the working state of the frost scraping mechanism of the present invention;

[0024] Figure 7 This is a schematic diagram of the frost layer and groove state of the box body according to the present invention;

[0025] Figure 8 This is a schematic diagram of the intelligent decision-making unit of the present invention.

[0026] The meanings of the labels in the diagram are as follows:

[0027] 100. Robot body; 200. Base; 300. Scraper; 3001. Motor; 3002. First servo electric cylinder; 3003. Pressing teeth; 3004. Groove; 400. Suction cup; 4001. Buffer assembly; 4002. Second servo electric cylinder; 4003. Silicon-based piezoresistive pad; 4004. Adsorption point; 500. Environmental perception unit; 600. Intelligent decision-making unit; 700. Power execution unit; 800. Operation management unit. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0029] Currently, when the frost layer thickness on the box surface is uneven, existing gripping mechanisms cannot detect changes in the frost layer in real time, leading to localized air leakage and failure of the vacuum suction cup, making it difficult to maintain stable suction force. This invention provides an intelligent automated handling robot for warehousing and logistics, aiming to achieve real-time diagnosis and adaptive gripping control of the frost layer state through an intelligent decision-making system that integrates multi-source data. (See also...) Figures 1-2 As shown, stable operation in a low-temperature environment of -25℃ is achieved through a handling robot and its integrated environmental perception unit 500, intelligent decision-making unit 600, power execution unit 700 and operation management unit 800.

[0030] The positional relationship of the components of the handling robot of the present invention is as follows: The handling robot includes a robot body 100, which adopts a fixed installation design and is directly anchored to a predetermined workstation on the warehouse floor using high-strength anchor bolts. This fixed installation method ensures absolute stability during operation and is particularly suitable for cold chain storage environments requiring precise positioning. A rotatable worktable is mounted on the top of the base 200, which can adjust the working direction according to task requirements to achieve multi-angle coverage. A working mechanism is installed above the worktable, which adopts a folding robotic arm design. In the non-working state, it can be folded inward to reduce space occupation, and in the working state, it unfolds into a predetermined posture. The base 200 is assembled at the end of the robot body 100.

[0031] The base 200 consists of two concentric circles with different radii. The larger circle forms the lower part of the base and is used to connect the adsorption structure. The smaller circle extends upwards and connects to the scraper 300 functional components. Figure 4 , Figure 6As shown, a silicon-based piezoresistive pad 4003 is installed on the large circular outline body, and several buffer components 4001 are fixedly provided on the other end of the pad. The other end of the buffer component 4001 is directly connected to the cylinder body of the second servo electric cylinder 4002, and two suction cups 400 are symmetrically fixed at the bifurcation end of the second servo electric cylinder 4002.

[0032] like Figure 3 , Figure 5 As shown, a motor 3001 is vertically mounted on the small circular outline of the base 200. The output shaft of the motor 3001 is coaxially connected to the cylinder body of the first servo electric cylinder 3002 via a flange coupling. A scraper 300 is mounted on the other output end of the first servo electric cylinder 3002 via a quick-change interface. The working surface of the scraper 300 integrates a single, continuous arrangement of pressure groove teeth 3003 along the longitudinal direction of its blade.

[0033] After optimizing the mechanical structure, the environmental perception unit 500 of this invention achieves real-time status monitoring of the working surface through a multi-dimensional sensor network. This unit is deeply integrated with the functional components of the robot body 100, forming a perception closed loop covering the entire grasping operation process.

[0034] An infrared thermal imaging module is embedded in the annular edge of the suction cup 400 as a temperature sensing layer. It adopts a ring array layout to form the temperature field distribution spectrum of the contact surface. This module captures the micro-temperature difference characteristics of the interface between the frost layer and the air on the surface of the box in real time through non-contact thermal radiation detection. Its output temperature gradient spectrum can accurately reflect the spatial distribution law of the frost layer crystallization state.

[0035] The honeycomb support structure of the silicon-based piezoresistive pad 4003 integrates a distributed strain sensing network, which generates a dynamic contact stress matrix through three-dimensional stress sensing. During the adsorption process of the suction cup 400, the sensor nodes simultaneously collect strain fluctuation data along the pressure transmission path, constructing a mechanically coupled characteristic field characterizing the adsorption stability, which can quantitatively assess the risk of local leakage.

[0036] An optical surface scanning device is fixedly installed on the back of the scraper 300, which generates a surface topology map through the principle of laser interference. This device acquires the microscopic geometric features of the working surface in real time during the movement of the scraper 300, and the output roughness distribution model can identify areas of abrupt changes in frost thickness, providing a deformation reference benchmark for adaptive defrosting.

[0037] Three sets of sensors form a composite sensing system through a spatiotemporal synchronization protocol, and are ultimately fused into a multimodal environmental feature set including temperature gradient maps, dynamic contact stress matrices, and surface topology maps. This dataset, after normalization, is transmitted to the intelligent decision unit 600 in a standardized format of environmental state tensors, serving as the core criterion for dynamic optimization of clamping parameters.

[0038] While traditional handling robots address the issue of low-temperature frost layers using vacuum suction cups and electric heating devices, their core deficiency lies in the lack of real-time perception and adaptive adjustment capabilities to the dynamic changes in the frost layer. Especially when the frost layer thickness on the box surface is uneven, fixed-parameter scraping and adsorption strategies easily lead to localized air leakage and failure of the vacuum suction cups, making it impossible to maintain stable adsorption force. This deficiency stems from the separation between the mechanical execution unit and the sensing unit, preventing the formation of closed-loop control. Therefore, this invention introduces an intelligent decision-making unit 600 to construct a dynamic decision-making core based on multi-source data fusion, addressing the technical challenges posed by the spatial heterogeneity of the frost layer. This unit analyzes the frost layer state in real time based on environmental perception data and coordinates the layout of the scraper 300 and groove 3004, ultimately achieving a leap from "passive response" to "active control."

[0039] See Figure 8 As shown, the intelligent decision-making unit 600, as the core control center of this invention, has a workflow that is inseparable from the innovative design of its mechanical structure. For example... Figure 7 As shown, the key improvement in the mechanical structure of this invention lies in the special design of the frost scraping assembly: when the scraper 300 drives the pressing teeth 3003 to rotate 360°, because the diameter of the scraper 300 is specially designed to be longer than the length of the line segment from the axis of the scraper 300 to the bottom of the box, the grooves 3004 pressed out by the pressing teeth 3003 on the outside of the adsorption area 4004 present a unique semi-circular arc shape. This innovative design ensures that the frost layer that has not been scraped off on the upper part of the box can slide directly down to the bottom of the box along the arc-shaped grooves 3004 when it falls freely, rather than accumulating on the surface of the box and affecting the adsorption effect.

[0040] The intelligent decision-making unit 600 first receives three types of core data from the environmental sensing unit 500: a surface topology map generated by a laser scanning device, a temperature field distribution spectrum acquired by an infrared thermal imaging module, and a dynamic contact stress matrix fed back by the silicon-based piezoresistive pad 4003. Based on this data, the unit performs the following analysis process: extracting the frost thickness distribution characteristics from the surface topology map to identify key areas with severe frost accumulation; analyzing the crystallization state of the frost layer in conjunction with the temperature field distribution spectrum to determine its ease of detachment; and finally, evaluating the stability of the current adsorption state through the contact stress matrix. The fusion analysis of these three types of data enables the unit to comprehensively understand the frost condition of the working surface.

[0041] Based on the above analysis, the intelligent decision-making unit 600 generates a three-level control strategy. The first-level strategy is a frost layer regional classification and evaluation strategy, the core of which lies in establishing a dynamic zoning model of the working surface. The intelligent decision-making unit 600 constructs a frost layer state evaluation matrix by fusing and analyzing frost layer thickness distribution data obtained from laser scanning and temperature gradient data acquired from infrared thermal imaging. In this matrix, the working surface is divided into three levels of regions: the first-level critical region corresponds to locations where the frost layer thickness exceeds a set threshold and the temperature gradient is significant; the frost layer structure in these regions is unstable and easily leads to adsorption failure; the second-level region of interest consists of areas with moderate frost layer thickness but local temperature fluctuations; and the third-level normal region is a surface with uniform frost layer and stable temperature. This classification method provides a precise spatial positioning basis for subsequent differentiated processing.

[0042] The second-level strategy is a dynamic operation control strategy, which implements precise mechanical control schemes and generates differentiated mechanical control parameters for different levels of areas. For the first-level critical area, the "depth mode" operation scheme is activated: the control motor 3001 reduces the rotation speed of the scraper 300 to 60% of the standard value, while the first servo electric cylinder 3002 increases the downward pressure of the groove pressing teeth 3003 to 80% of the maximum value, ensuring that the depth of the formed arc-shaped groove 3004 meets the design requirements. For the second-level focus area, the "balanced mode" is adopted: the scraper 300 rotation speed is maintained at the standard value, and the downward pressure of the groove pressing teeth 3003 is adjusted to 120% of the standard value. For the third-level regular area, the "fast mode" is implemented: the scraper 300 rotation speed is increased to 150% of the standard value, and only the basic downward pressure is applied. This tiered operation mode improves operation efficiency while ensuring processing effect.

[0043] The third-level strategy is the adsorption compensation strategy, which focuses on compensating for the impact of the formed trench 3004 on vacuum adsorption. The intelligent decision-making unit 600 establishes a contact surface deformation prediction model based on the distribution characteristics of the formed trench 3004. This model comprehensively considers parameters such as the depth, width, and distribution density of the trench 3004 to calculate the height compensation amount of the suction cup 400 at each contact point. Through precise control of the second servo electric cylinder 4002, dynamic leveling of the suction cup 400's contact surface is achieved. The specific compensation process is divided into two stages: the pre-compensation stage adjusts the suction cup 400's posture in advance based on the prediction model; the real-time compensation stage relies on stress feedback data from the silicon-based piezoresistive pad 4003 for fine-tuning. When the system detects that a local pressure deviation exceeds the safety threshold, it immediately initiates a recalculation process for the compensation parameters to ensure a uniform distribution of adsorption force. This compensation mechanism effectively solves the technical problem of adsorption instability caused by surface treatment in traditional solutions.

[0044] The entire control process forms an intelligent closed loop: the mechanical execution results are fed back in real time through the environmental sensing system, and the decision-making unit dynamically adjusts subsequent control parameters accordingly. This design, which deeply integrates a special mechanical structure with intelligent control, perfectly solves the problem of frost accumulation affecting adsorption in traditional solutions. Particularly noteworthy is the arc-shaped design of the groove 3004, which not only improves the efficiency of frost removal but also ensures, through its specific spatial layout, that it does not interfere with the normal adsorption operation of the suction cup 400. This comprehensive innovative design enables the invention to maintain stable handling performance even at -25℃, significantly improving the reliability of automated cold chain logistics operations.

[0045] The intelligent decision-making unit 600, acting as the control center of the entire system, completes the crucial transition from environmental perception to mechanical execution. Through the coordinated operation of a three-level control strategy, this unit transforms the complex problem of frost layer treatment into executable mechanical control commands. After completing the entire decision-making process of regional hierarchical assessment, differentiated operation control, and adaptive adsorption compensation, the intelligent decision-making unit 600 ultimately generates three sets of core control commands and transmits them to the power execution unit 700: the first set consists of motion parameter commands for the scraper 300 components, including the rotational speed, downward pressure, and angle adjustment values ​​for each region; the second set consists of compensation parameter commands for the suction cups 400, specifying in detail the height and angle compensation amounts for each suction cup 400; and the third set consists of operation mode selection commands, clarifying the specific operation mode adopted for different regions. These commands integrate all the key features of the environmental perception data, providing precise operational basis for subsequent mechanical execution.

[0046] As the system's execution terminal, the power execution unit 700 immediately initiates a multi-axis collaborative control mechanism upon receiving control commands from the intelligent decision-making unit 600. The unit's built-in high-performance motion controller first parses the command content, decomposing the complex command into basic control commands for each actuator. For the scraping assembly, the controller adjusts the speed of the motor 3001 through a precision frequency conversion drive system to strictly match the rotational speed required by the command. Simultaneously, closed-loop servo control technology drives the first servo electric cylinder 3002, precisely controlling the downward pressure and working angle of the groove-pressing teeth 3003. During execution, the unit collects the scraper 300's position information in real time through a high-precision encoder and monitors the actual downward pressure through a torque sensor, ensuring that the formed arc-shaped groove 3004 fully meets design requirements. This real-time operating data is continuously recorded at a sampling frequency of 100Hz and transmitted to the operation management unit 800 via industrial Ethernet.

[0047] In terms of controlling the adsorption components, the power execution unit 700 adopts a distributed control architecture. Each suction cup 400 is equipped with an independent second servo electric cylinder 4002, coordinated and controlled by a multi-axis motion controller within the unit. Based on received compensation parameter commands, the controller first plans the motion trajectory to determine the optimal adjustment path for each suction cup 400, and then uses a position-speed-current three-loop control algorithm to achieve micron-level precision position adjustment. During adjustment, a six-dimensional force sensor mounted on each suction cup 400 monitors the contact pressure distribution in real time to ensure the uniformity of the adsorption force. All execution data, including position, speed, current, and force sensor readings for each axis, are packaged into data frames and uploaded to the operation management unit 800 in real time.

[0048] The operation management unit 800, acting as the system's command center, continuously receives execution data from the power execution unit 700 via a high-speed industrial network. This data primarily falls into three categories: first, equipment operating status data, including real-time position, speed, and current parameters of each actuator; second, load monitoring data, recording equipment health indicators such as motor 3001 temperature and vibration amplitude; and third, quality verification data, including operational effect parameters such as the forming dimensions of the groove 3004 and the contact pressure distribution of the suction cup 400. The unit's built-in data processing engine analyzes and stores this information in real time, establishing a complete task execution profile.

[0049] Based on this real-time data, the Operation Management Unit 800 performs multi-dimensional system coordination. At the task scheduling level, the unit dynamically adjusts the job sequence and resource allocation by analyzing the execution progress and effect data of each area. When it detects that the processing effect of a certain area does not meet expectations, it automatically generates a supplementary processing instruction. At the equipment management level, it assesses the health status of the equipment by monitoring the changing trends of parameters such as the current and temperature of motor 3001, and provides early warnings before potential failures occur. At the quality control level, it compares the actual operation effect with the expected target and establishes a quality traceability chain. All analysis results are fed back to the Intelligent Decision Unit 600 to optimize subsequent control strategies. The Operation Management Unit 800 also has an intelligent learning function. Through long-term accumulated execution data, the unit continuously optimizes the task allocation algorithm and equipment parameter settings. For example, for a specific type of frost distribution pattern, the system will memorize the most effective combination of processing parameters and prioritize its use under similar working conditions. This self-learning mechanism enables continuous improvement in system performance.

[0050] In summary, by leveraging the intelligent decision-making unit 600's multi-dimensional perception of frost conditions and the intelligent generation of a three-level control strategy, combined with the precise mechanical control of the power execution unit 700 and the efficient system coordination of the operation management unit 800, this invention achieves autonomous diagnosis, intelligent handling, and stable adsorption of frost in a handling robot operating at -25℃, constructing a complete intelligent control closed loop from environmental perception to mechanical execution. This innovative design not only effectively solves the adsorption failure problem caused by frost in traditional handling robots but also enhances the intelligence level and operational reliability of cold chain logistics warehousing operations through deep collaboration among various functional units.

[0051] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent warehousing and logistics automated handling robot, comprising a robot body (100), wherein the robot body (100) is fixedly mounted on a base (200) via a robotic arm, the base (200) is controllably equipped with a suction cup (400), and the robot body (100) further integrates an environmental sensing unit (500), the environmental sensing unit (500) collecting a multimodal environmental feature set, characterized in that, Also includes: A scraper (300) is rotatably and retractably positioned above the suction cup (400) on the base (200); The scraper (300) has several pressing teeth (3003) on the opposite side of the base (200) for pressing out grooves (3004) on the frost layer on the surface of the box to guide the frost layer to fall off. The rotation diameter of the scraper (300) is greater than the length of the line segment from the axis of the scraper (300) to the bottom of the box, so that the groove (3004) forms a semi-circular arc and the bottom opening faces the bottom of the box. The intelligent decision-making unit (600) receives and analyzes a multimodal environmental feature set, generates a frost layer area classification assessment strategy, establishes a frost layer status assessment model, and divides the work surface into different level areas. The intelligent decision-making unit (600) generates a dynamic operation control strategy based on the frost layer area classification assessment strategy, and generates differentiated mechanical control parameters based on different level areas; The intelligent decision-making unit (600) generates an adsorption compensation strategy based on the dynamic operation control strategy, establishes a contact surface deformation prediction model, and dynamically levels the contact surface of the suction cup (400) for the robot to stably transport the box.

2. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The base (200) includes a large circular outline and a small circular outline. A motor (3001) is mounted on the small circular outline. The output end of the motor (3001) is connected to a first servo electric cylinder (3002). The scraper (300) is mounted on the output end of the first servo electric cylinder (3002).

3. The intelligent warehousing and logistics automated handling robot according to claim 2, characterized in that: A silicon-based piezoresistive pad (4003) is installed on the large circular outline. The silicon-based piezoresistive pad (4003) is connected to the second servo electric cylinder (4002) through a buffer assembly (4001). The suction cup (400) is symmetrically fixed to the end of the second servo electric cylinder (4002).

4. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The suction cup (400) has an infrared thermal imaging module embedded in its annular edge, which is used to collect the temperature gradient spectrum of the box surface.

5. The intelligent warehousing and logistics automated handling robot according to claim 3, characterized in that: The honeycomb support structure of the silicon-based piezoresistive pad (4003) integrates a distributed strain sensing network for generating a dynamic contact stress matrix.

6. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The scraper (300) is equipped with a laser scanning device fixed on its back for generating a surface topology map.

7. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The suction cup (400) has an adsorption area (4004) in the contact center area. Its outer edge and the groove (3004) formed by the pressure teeth (3003) maintain a preset distance to isolate the working area of ​​the groove (3004) from the working area of ​​the adsorption area (4004).

8. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The intelligent decision-making unit (600) generates three sets of control commands and transmits them to the power execution unit (700), including motion parameter commands, compensation parameter commands, and operation mode selection commands.

9. The intelligent warehousing and logistics automated handling robot according to claim 8, characterized in that: The power execution unit (700) receives three sets of control commands, generates equipment operating status data, load monitoring data and quality verification data, and transmits them to the operation management unit (800).

10. The intelligent warehousing and logistics automated handling robot according to claim 9, characterized in that: The operation management unit (800) receives equipment operating status data, load monitoring data and quality verification data, performs task progress monitoring, equipment health status assessment and quality traceability analysis, and feeds back the optimized control parameters to the intelligent decision-making unit (600) for adjusting subsequent operation strategies.

Images