Industrial logistics material carrying scheduling system and method

Abstract

The invention discloses an industrial logistics material handling dispatching system which comprises a central dispatching system (CSS) serving as a core hub and performing real-time two-way communication with the following modules; the monitoring module is used for collecting environment data and unmanned aerial vehicle state information; the task / path planning module is used for realizing multi-machine collaborative path planning and dynamic task allocation based on the real-time traffic situation, the electric quantity state and the task priority; the loading and unloading module comprises a visual guidance landing platform and a mechanical arm grabbing system and is used for realizing automatic loading and unloading of materials; the charging module supports autonomous wired / wireless charging of the unmanned aerial vehicle; the forced landing module triggers nearby emergency landing according to the abnormal condition; the system realizes whole-process unmanned operation from task issuing to task completion through closed-loop control. According to the system, intelligent upgrading of the material carrying link is achieved by innovating the unmanned aerial vehicle system design.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of industrial automation and UAV applications, specifically relating to an intelligent logistics scheduling system and method based on a backpack-mounted UAV swarm, applicable to scenarios requiring three-dimensional material transportation such as intelligent manufacturing and warehousing logistics. Background Technology

[0002] Current industrial logistics systems face multi-dimensional technological challenges during their intelligent transformation. While traditional ground-based automated guided vehicles (AGVs) transportation solutions have achieved basic automation, their planar movement characteristics result in low space utilization and frequent path congestion and efficiency degradation when multiple devices are operating collaboratively. This two-dimensional movement mode not only limits the throughput capacity of the logistics system but also makes factory layout planning inflexible, making it difficult to adapt to the flexible production requirements of modern intelligent manufacturing.

[0003] While existing drone-based logistics solutions overcome the limitations of ground space, the commonly used sling-mounted cargo loading method has inherent drawbacks. The swaying of goods during transport severely affects positioning accuracy, making it difficult for the system to meet the stringent stability requirements of precision industrial scenarios. Furthermore, these systems often employ simple static task allocation mechanisms, lacking effective management of three-dimensional airspace resources, which can easily lead to low collaborative efficiency and safety hazards in complex operating environments.

[0004] Analysis of existing patented technologies reveals that current solutions often only optimize specific aspects and fail to systematically address the multi-dimensional coupling issues in industrial logistics scenarios. While some improvements optimize single dimensions such as cargo stability or route planning, they introduce new limitations, such as reduced payload or significantly increased system complexity. More importantly, existing technologies generally neglect deep integration of logistics data with manufacturing execution systems, making it difficult to support the end-to-end material traceability requirements of digital factories.

[0005] This current technological state severely restricts the development of industrial logistics systems towards intelligence and flexibility. Especially in the high-end manufacturing sector, traditional solutions can no longer meet the comprehensive requirements of stability, timeliness, and traceability for the transportation of precision components. The market urgently needs a new generation of logistics systems that can balance space utilization efficiency, transportation stability, and intelligent collaboration; this is precisely the core technical problem that this invention aims to solve. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide an industrial logistics material handling and scheduling system and method, which aims to solve the pain points of low efficiency, poor flexibility and high cost of traditional transportation methods in the industrial logistics field, and realize the intelligent upgrade of the material handling process through innovative drone system design.

[0007] This invention is achieved through the following scheme: an industrial logistics material handling and scheduling system, characterized in that it includes:

[0008] The Central Dispatch System (CSS) acts as the core hub, communicating bidirectionally with the following modules in real time.

[0009] The monitoring module is used to collect environmental data and drone status information;

[0010] The task / route planning module enables multi-machine collaborative route planning and dynamic task allocation based on real-time traffic conditions, battery status, and task priorities.

[0011] The loading and unloading module includes a vision-guided landing platform and a robotic arm gripping system to achieve automatic loading and unloading of materials.

[0012] The charging module supports autonomous wired / wireless charging for drones.

[0013] The emergency landing module triggers an emergency landing at the nearest location based on abnormal conditions.

[0014] The system achieves fully unmanned operation from task issuance to completion through closed-loop control.

[0015] The drone adopts a back-mounted cargo design, with the cargo area fixed to the back of the fuselage and equipped with a standardized docking interface.

[0016] The task / path planning module further includes:

[0017] Dynamic environment response unit, integrating obstacle detection data and airspace control instructions;

[0018] The rolling temporal optimization unit continuously replans the path and constructs no-fly zones;

[0019] The charging path embedding unit incorporates the location of charging stations into the transportation path planning based on the remaining battery capacity.

[0020] The loading and unloading module specifically includes:

[0021] The high-precision positioning unit uses visual or laser positioning technology to achieve centimeter-level positioning under complex lighting conditions;

[0022] The robotic arm unit is equipped with an electromagnetic / vacuum / gripper end effector;

[0023] The data interaction unit automatically uploads material verification information to CSS after loading and unloading are completed.

[0024] The charging task triggering condition for the charging module is as follows:

[0025] Triggered immediately when battery level drops to the first threshold in standby mode;

[0026] When the battery level drops to the second threshold during task execution, the trigger will be delayed until the task is completed.

[0027] An industrial logistics material handling and scheduling method includes the following steps:

[0028] S1. The central dispatch system receives multi-source task requests and generates an initial dispatch plan by combining airspace capacity and UAV status.

[0029] S2. Based on real-time traffic data and battery status, a dynamic allocation algorithm is used to update task priorities and paths;

[0030] S3. The drone is guided by vision / laser to precisely dock with the loading and unloading platform to complete the automatic loading and unloading.

[0031] S4. Continuously monitor abnormal conditions during flight and trigger an emergency landing or route replanning;

[0032] S5. Dynamically insert charging paths based on battery remaining capacity to maintain the fleet's continuous operational capability.

[0033] The dynamic allocation algorithm described in step S2 includes:

[0034] A height-layer allocation strategy avoids collisions by layering vertical space.

[0035] An emergency detection mechanism is in place to dispatch drones to detect sudden obstacles in order to assess the conditions for lifting airspace restrictions.

[0036] The data interaction in step S3 during the loading and unloading process includes:

[0037] During loading, the material ID is associated with the target equipment information;

[0038] During unloading, the system automatically verifies the compatibility between the destination and the materials, and triggers an alarm if the error exceeds the limit.

[0039] Abnormal states in step S4 include:

[0040] Conflict in airspace control instructions;

[0041] An unexpected obstacle appeared on the planned path;

[0042] The drone's battery level is below the safe flight threshold.

[0043] An industrial logistics smart factory deploys the above system to use a swarm of backpack drones to transport materials in three-dimensional space, replacing traditional ground AGV transportation.

[0044] The beneficial effects of this invention are as follows:

[0045] 1. Revolutionary improvement in space utilization: Through the three-dimensional airspace operation capabilities of the backpack-mounted drone swarm, the inherent limitations of traditional AGV systems confined to a two-dimensional plane are completely overcome. The system can autonomously construct a three-dimensional logistics network, realizing the intelligent utilization of vertical space in the factory, and achieving a qualitative leap in the logistics carrying capacity of a factory area of ​​the same size.

[0046] 2. Significantly enhanced transport stability: The innovative back-mounted cargo design, combined with an adaptive center of gravity adjustment algorithm, fundamentally solves the inherent swaying problem of sling-mounted drones. The cargo platform is rigidly connected to the drone body, and combined with active shock absorption control, it ensures the stability of the material's position and posture during transport, meeting the positioning accuracy requirements of ±1cm in precision industrial scenarios.

[0047] 3. Advantages of dynamic intelligent scheduling: Based on multi-dimensional constraint optimization algorithms and reinforcement learning, the dynamic scheduling system achieves real-time optimal matching of airspace resources, equipment status and task requirements.

[0048] 4. Full-process automated integration: From task assignment and automatic loading / unloading to status feedback, the entire process is unmanned. Combined with standardized data interfaces, it achieves seamless integration with industrial systems such as MES and WMS. Material flow information is uploaded in real time, enabling full-process visual traceability from raw materials to finished products.

[0049] 5. The system has high reliability and security. The three-level emergency response mechanism covers multiple abnormal scenarios, from communication interruption to hardware failure. Combined with three-dimensional obstacle avoidance algorithm and emergency landing strategy, it ensures the reliable operation of the system in complex industrial environments.

[0050] 6. Compared with traditional solutions, this system can reduce the investment in ground facility renovation by more than 60%, greatly reduce labor costs, and extend the overall service life of the equipment through intelligent charging and discharging management and load balancing algorithms, thereby achieving cost optimization throughout the entire life cycle. Attached Figure Description

[0051] Figure 1 The core business process diagram of an industrial logistics material handling and scheduling system and method of the present invention;

[0052] Figure 2 This invention provides a task management flowchart for an industrial logistics material handling and scheduling system and method.

[0053] Figure 3 This invention provides a flowchart of the scheduling process for unmanned aerial vehicles (UAVs) in an industrial logistics material handling and scheduling system and method.

[0054] Figure 4 This invention relates to some data collection content of an industrial logistics material handling and scheduling system and method;

[0055] Figure 5This invention provides an industrial logistics material handling and scheduling system and method for automatically completing the loading / unloading process of materials;

[0056] Figure 6 This invention relates to a drone charging process within an industrial logistics material handling and scheduling system and method.

[0057] Figure 7 This invention relates to an unmanned aerial vehicle (UAV) forced landing process in an industrial logistics material handling and scheduling system and method. Detailed Implementation

[0058] The following is combined with Figure 1-7 The present invention will be further described, but the scope of protection of the present invention is not limited to the contents described herein.

[0059] For clarity, not all features of the actual embodiments will be described. In the following description, well-known functions and structures will not be described in detail, as they would confuse the invention with unnecessary details. It should be understood that in the development of any actual embodiment, a great deal of implementation detail must be made to achieve the developer’s specific goals, such as changing one embodiment to another according to the limitations of the system or business. In addition, it should be understood that such development work may be complex and time-consuming, but is merely routine work for those skilled in the art.

[0060] Example 1

[0061] An industrial logistics material handling scheduling system is presented. The system uses a central scheduling system as its core hub, connecting six modules via real-time data streams: a monitoring module provides environmental and equipment status information; a task / path module optimizes decisions; a loading / unloading / charging module performs physical operations; and an emergency landing module ensures safety in case of anomalies. These modules form a two-way closed loop, supporting a fully unmanned process from task issuance to completion. Driven by the central scheduling hub, the system receives real-time data from the monitoring module (e.g., battery level, site occupancy), dynamically calls the path planning module to generate routes, and triggers the loading / unloading / charging modules to execute operations. The closed-loop task flow is as follows: the task management module generates tasks → the scheduling system allocates drones → the path planning module calculates routes → the loading / unloading module performs end-to-end operations → the status monitoring module provides feedback → the charging module responds to endurance requirements. Anomaly handling is coordinated: the drone cluster monitoring module detects faults → triggers the emergency landing module to take over control → the scheduling system reassigns tasks and updates routes. Data collaboration is achieved through a unified data interface between all modules and the scheduling system (e.g., loading / unloading completion signals are synchronized to the task queue, and charging dock status affects task allocation).

[0062] Example 2

[0063] A material handling scheduling method for industrial logistics scenarios can execute handling tasks according to business needs. For example, if a material handling task is generated at point A and needs to be moved to point B, the CSS (Service Center) assigns this task to a drone. The drone flies to point A according to the optimal path. Upon reaching the designated location at point A (determining if the drone is capable of approaching point A), the drone reports to its superior unit that it has arrived and is in position. The superior unit sends a drone positioning signal to the clamping mechanism, which places the material on the drone's back, retracts to a safe position, and reports back to the superior unit that the unloading action is complete. Upon receiving the unloading completion instruction, or simultaneously detecting that the material has been placed, the drone confirms it is in a loaded state. The drone takes off and moves the material to point B. Upon reaching the designated location at point B, the drone reports to its superior unit that it has arrived and is in position. The superior unit sends a drone positioning signal to the clamping mechanism, which removes the material from the drone's back, retracts to a safe position, and reports back to the superior unit that the retrieval action is complete. Upon receiving the retrieval completion instruction, or simultaneously detecting that the material has been retrieved, the drone confirms it is in an unloaded state. At this point, the drone has completed the task of transporting the materials generated at point A to point B, and is waiting for the next transport task, or to return to its docking position or fly to the charging station.

[0064] CSS allows for task management. Generally, tasks are executed in chronological order of creation. Task priorities can also be set based on material handling type, such as up to 9 levels (adjustable according to customer requirements). Task priority is higher than time priority; tasks of the same priority are assigned based on time. Time-sensitive priorities, such as deadlines, can also be set; for tasks with time-sensitive requirements, the closer to the deadline, the higher the priority. Dynamic time weighting adjusts priorities dynamically based on task creation time and remaining processing time. Manual override allows administrators to manually promote, pause, or delete specific tasks in the queue to handle unforeseen special circumstances.

[0065] The task management priority formula is as follows:

[0066]

[0067] Parameter description:

[0068] P 综合 Task final priority (higher value, higher priority)

[0069] P 物料 Material inherent priority (levels 1-9, preset by the user)

[0070] T 截止 Task deadline

[0071] T 当前 Current system time

[0072] D 最短 The theoretical shortest distance from the starting point to the destination.

[0073] D 实际 Actual planned path distance

[0074] α, β, γ: Weighting coefficients

[0075] λ: Time decay factor (controls the non-linear growth of urgency)

[0076] The above task management strategies can be used individually or in combination, depending on customer needs and the scenario's material priority, deadline urgency, and path optimization gains.

[0077] like Figure 3 As shown, CSS can schedule drones. Generally, tasks are executed sequentially by drone number, resulting in a relatively even distribution of tasks among drones. Alternatively, tasks can be assigned to drones based on their current location, minimizing idle time and improving efficiency. Manual intervention is possible, allowing administrators to add material handling tasks and configure drone statuses, such as offline (not receiving tasks) or stopped (receiving tasks but not executing them), to handle unforeseen circumstances. Based on load balancing, it not only pursues efficiency for individual tasks (e.g., proximity) but also focuses on averaging the workload (flight time, number of tasks, flight distance) of all drones over the long term, preventing some drones from being overused while others are idle. Implementation: Building upon "proximity allocation," it incorporates consideration of historical task load, prioritizing drones with lower recent task loads. Dynamic weight adjustments strike a balance between efficiency and balance. This extends the overall fleet lifespan (reducing individual drone wear), improves resource utilization (reducing idle time), and more fairly distributes equipment wear and tear. It is suitable for the long-term operation and maintenance of large-scale drone fleets.

[0078] The drone allocation formula is as follows:

[0079]

[0080] Parameter description:

[0081] S 分配 The drone ultimately selected to perform the mission

[0082] U i The $i$th drone in the candidate drone set

[0083] D 当前 Distance from the current drone to the mission starting point

[0084] D max Maximum allowed distance in the scene (used for normalization)

[0085] W i UAV i Historical workload (see workload calculation sub-formula)

[0086] W avg Average workload of the fleet

[0087] θ: Distance factor weight (0 < θ < 1, default 0.6)

[0088] W i =k t ·T 飞行 +k n ·N 任务 +k d ·D 累计

[0089] Parameter description:

[0090] T 飞行 Total flight time for the day (hours)

[0091] N 任务 : Total number of tasks completed on the day

[0092] D 累计 Daily cumulative flight distance (km)

[0093] k t ,k n ,k d Weighting coefficient (k) t +k n +k d =1)

[0094] The above drone dispatch strategies can be used individually or in combination, depending on the needs of the customer and the scenario.

[0095] like Figure 4 As shown, CSS collects relevant information. To achieve safe, efficient, reliable, and traceable automated logistics operations, the upper-level control of the UAV system needs to collect multi-dimensional information. This includes: positioning and navigation information, based on indoor positioning technologies such as UWB, visual SLAM, laser SLAM, QR codes, and Bluetooth beacons, providing (X, Y, Z) coordinates; speed, including horizontal and vertical speed; heading, current flight direction; and flight status information, such as flight mode (manual control, autopilot, hovering, return to home, landing, emergency mode, etc.).

[0096] Flight status: takeoff, landing, moving, mission in progress, idle, abnormal, etc. Flight command execution status: progress or result of the current command (e.g., "heading to point A", "arrived at point B", "cargo pickup / release command executed successfully / failed"). Battery status information: remaining battery percentage (SoC), battery voltage, current, temperature, estimated remaining flight time / mission time, charging status (whether charging). Payload status information: whether the drone is currently carrying cargo. Grab / release mechanism status: open / closed status, execution success / failure signal. Mission execution information: including current mission, currently executing mission ID, mission type (pickup, delivery, charging, standby, empty, etc.), mission priority, mission details, starting point / station, target location / station, waypoints / path information, associated cargo / material information, mission start time, estimated completion time, actual completion time. Current mission progress status (assigned, heading to starting point, loading, heading to destination, unloading, completed, canceled, failed). Site status information includes the status of the take-off / landing platform / charging dock (idle, occupied, charging, faulty, closed) and the status of the pickup / unloading point (idle, occupied, ready, abnormal). The above drone and service information is collected based on customer and scenario requirements.

[0097] like Figure 5 As shown, the material loading / unloading is completed automatically. Before issuing a task to the drone, the host computer checks if the pickup point is available. If it is occupied or in an abnormal state, the task is not issued until it becomes available. The drone then proceeds to the pickup point and checks if the unloading point is in normal condition before landing. If it is abnormal, an abnormal signal is sent back to the host computer. If it is available, the drone lands (or hovers). Upon reaching the pickup point, the drone reports its arrival status to the host computer. The gripping mechanism places the material on the drone's back, retracts the gripping mechanism, and reports to the host computer that loading is complete. The host computer then issues the command to the drone. The drone prepares to proceed to the unloading point. If there is only one unloading point, the host computer checks if it is available. If not available, the drone waits until it becomes available and then proceeds to the unloading point. If there are multiple unloading points, proceed directly to the unloading point area. If the unloading point is occupied, hover and wait. If a certain time has passed, change the unloading point and proceed. Upon arrival at the unloading point, report to the higher-level administrator that it is ready. The grabbing mechanism removes the material from the back of the drone, retracts the grabbing mechanism, reports to the higher-level administrator that unloading has been completed, and the drone proceeds to the next task.

[0098] like Figure 6As shown, the drone can automatically charge. When a drone triggers a charging request while in standby mode without a task, a charging task is generated directly. If a drone triggers a charging request while performing a task, the material handling task is completed first, and then a charging task is generated. The charging request threshold can be set. When all charging docks are occupied, charging, faulty, or closed, and no charging is available, the drone waits for the charging dock status to change to idle before executing the charging task. The charging dock status can be manually turned on or off.

[0099] like Figure 7 As shown, this describes an emergency landing for a drone. Emergency landings are triggered when a drone encounters hardware system failures such as power system malfunction, critical sensor failure, or structural damage; software and control anomalies such as flight control system crashes or communication interruptions; or other abnormal situations. The drone follows a pre-set tiered response mechanism: Primary response: hovering awaiting assistance (communication restored / obstacles removed); Secondary response: autonomously returning to home along a pre-remembered path; Final emergency landing: descending slowly to the nearest safe area (avoiding conveyor belts, shelves, etc.).

[0100] Although the technical solutions of the present invention have been described and enumerated in detail, it should be understood that modifications to the above embodiments or the adoption of equivalent alternatives are obvious to those skilled in the art. Such modifications or improvements made without departing from the spirit of the present invention are all within the scope of protection claimed by the present invention.

Claims

1. An industrial logistics material handling and scheduling system, characterized in that, include: The Central Dispatch System (CSS) acts as the core hub, communicating bidirectionally with the following modules in real time. The monitoring module is used to collect environmental data and drone status information; The task / route planning module enables multi-machine collaborative route planning and dynamic task allocation based on real-time traffic conditions, battery status, and task priorities. The loading and unloading module includes a vision-guided landing platform and a robotic arm gripping system to achieve automatic loading and unloading of materials. The charging module supports autonomous wired / wireless charging for drones. The emergency landing module triggers an emergency landing at the nearest location based on abnormal conditions. The system achieves fully unmanned operation from task issuance to completion through closed-loop control.

2. The industrial logistics material handling and scheduling system according to claim 1, characterized in that, The drone adopts a back-mounted cargo design, with the cargo area fixed to the back of the fuselage.

3. The industrial logistics material handling and scheduling system according to claim 1, characterized in that, The task / path planning module further includes: Dynamic environment response unit, integrating obstacle detection data and airspace control instructions; The rolling temporal optimization unit continuously replans the path and constructs no-fly zones; The charging path embedding unit incorporates the location of charging stations into the transportation path planning based on the remaining battery capacity.

4. The industrial logistics material handling and scheduling system according to claim 1, characterized in that, The loading and unloading module specifically includes: The high-precision positioning unit uses visual or laser positioning technology to achieve centimeter-level positioning under complex lighting conditions; The robotic arm unit is equipped with an electromagnetic / vacuum / gripper end effector; The data interaction unit automatically uploads material verification information to CSS after loading and unloading are completed.

5. The industrial logistics material handling and scheduling system according to claim 1, characterized in that, The charging task triggering condition for the charging module is as follows: Triggered immediately when battery level drops to the first threshold in standby mode; When the battery level drops to the second threshold during task execution, the trigger will be delayed until the task is completed.

6. The material handling scheduling method of the system according to any one of claims 1-5, characterized in that, Including the following steps: S1. The central dispatch system receives multi-source task requests and generates an initial dispatch plan by combining airspace capacity and UAV status. S2. Based on real-time traffic data and battery status, a dynamic allocation algorithm is used to update task priorities and paths; S3. The drone is guided by vision / laser to precisely dock with the loading and unloading platform to complete the automatic loading and unloading. S4. Continuously monitor abnormal conditions during flight and trigger an emergency landing or route replanning; S5. Dynamically insert charging paths based on battery remaining capacity to maintain the fleet's continuous operational capability.

7. The material handling scheduling method of the system according to claim 6, characterized in that, The dynamic allocation algorithm described in step S2 includes: A height-layer allocation strategy avoids collisions by layering vertical space. An emergency detection mechanism is in place to dispatch drones to detect sudden obstacles in order to assess the conditions for lifting airspace restrictions.

8. The material handling scheduling method of the system according to claim 6, characterized in that, The data interaction in step S3 during the loading and unloading process includes: During loading, the material ID is associated with the target equipment information; During unloading, the system automatically verifies the compatibility between the destination and the materials, and triggers an alarm if the error exceeds the limit.

9. The material handling scheduling method of the system according to claim 6, characterized in that, Abnormal states in step S4 include: Conflict in airspace control instructions; An unexpected obstacle appeared on the planned path; The drone's battery level is below the safe flight threshold.

10. An intelligent industrial logistics factory, characterized in that, Deploy the system as described in any one of claims 1-5 to realize material handling in three-dimensional space using a cluster of backpack drones, replacing traditional ground AGV transportation.

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