Multi-distribution robot path collaborative optimization system and scheduling method

Abstract

The invention discloses a multi-delivery robot path collaborative optimization system and a scheduling method, and aims to solve the problems of non-uniform scheduling load, unsmooth path, weak conflict avoidance capability and poor dynamic adaptability of existing multi-robot scheduling. The system comprises a central scheduling module, a distribution robot terminal, an environment sensing module and a map management module, and realizes task analysis, state monitoring, path planning and conflict coordination. According to the method, region division and task balanced distribution are completed by improving a DARP algorithm, path coverage efficiency and smoothness are optimized by fusing a BA * algorithm, a cattle farming method and a TEB algorithm, multi-type path conflicts are solved by adopting a time window reservation and distributed negotiation strategy, and dynamic adjustment under new task access, robot faults and environment sudden change is supported. According to the method, the load balance, the path optimization effect and the conflict avoidance capability of multi-robot collaborative distribution can be remarkably improved, the efficient and stable execution of the distribution task is ensured, and the method is suitable for terminal distribution scenes such as communities and office buildings.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics and robot scheduling technology, specifically to a multi-delivery robot path collaborative optimization system and scheduling method. Background Technology

[0002] With the rapid development of smart logistics, delivery robots are increasingly widely used in last-mile delivery scenarios (such as residential communities, office buildings, and industrial parks). While collaborative operation of multiple delivery robots can significantly improve delivery efficiency, current multi-robot scheduling technologies have several shortcomings: First, path planning often uses a single algorithm, failing to fully consider the collaborative optimization of area division and path smoothness, resulting in numerous robot turns and low delivery efficiency. Second, conflict avoidance strategies are simplistic, making it difficult to handle various conflict types in complex scenarios, such as overtaking, intersections, and oncoming traffic, easily leading to traffic congestion or collision risks. Third, dynamic adaptability is poor, with delayed responses to new task access, robot malfunctions, and sudden environmental changes, making real-time dynamic path adjustment impossible.

[0003] For example, existing patent CN114442607A discloses a multi-robot path coordination method and system, which avoids conflicts through spatial grid division and path overlap judgment, but does not involve sub-region boundary optimization and dynamic obstacle prediction. Its scheduling efficiency and safety in complex environments need improvement. Meanwhile, existing delivery robots mostly use a single positioning method, resulting in insufficient positioning accuracy. Furthermore, task allocation does not fully consider robot load and power balance, leading to some robots operating under overload while others are idle, affecting overall operational efficiency. Therefore, there is an urgent need for a multi-delivery robot collaborative scheduling scheme that can achieve balanced task allocation, smooth path optimization, and dynamic conflict avoidance. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a multi-delivery robot path collaborative optimization system and scheduling method to solve the problems that existing delivery robots mostly use a single positioning method, which has insufficient positioning accuracy and does not fully consider the balance of robot load and power in task allocation, resulting in some robots operating under overload while others are idle, thus affecting the overall operation efficiency.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A multi-delivery robot path collaborative optimization system includes a central scheduling module, multiple delivery robot terminals, an environmental perception module, and a map management module. The central scheduling module, as the core control unit, is responsible for task parsing, robot status monitoring, path planning, and conflict coordination. The delivery robot terminals are equipped with positioning, motion control, communication, and task execution modules to achieve autonomous movement and delivery task execution. The environmental perception module collects environmental data through fixed and mobile sensors to ensure comprehensive environmental awareness. The map management module stores and dynamically updates a global grid map, providing basic data support for path planning.

[0006] A collaborative optimization scheduling method for multiple delivery robots includes seven steps: environment modeling and map updating, task reception and parsing, robot state assessment, task allocation and region division, path planning, conflict detection and avoidance, and dynamic adjustment. It achieves balanced region division by improving the DARP algorithm, optimizes path coverage efficiency by combining the BA* algorithm and the ox-plowing method, and enhances path smoothness by employing the TEB algorithm. It resolves multiple types of conflicts through time window reservation and distributed negotiation strategies, and achieves real-time dynamic adjustment of the path by combining dynamic obstacle prediction and battery monitoring.

[0007] Compared with the prior art, the beneficial effects of the present invention are: 1. Good task allocation balance: The delivery area is divided by an improved DARP algorithm. Smooth sub-regions are generated by distance cost calculation and boundary curvature penalty, which effectively balances the task load of each robot, reduces the risk of single robot overload, and improves the overall operation efficiency of the system. 2. Excellent path optimization effect: The BA* algorithm and the ox-plowing method are combined to solve the dead zone coverage problem of traditional path planning. The TEB algorithm is combined to optimize the smoothness of the path, constrain the robot's kinematic parameters, reduce the number of turns and energy consumption, and improve delivery efficiency. 3. Strong conflict avoidance capability: It adopts differentiated resolution strategies for various conflict types, combined with dynamic obstacle prediction and time window reservation mechanism, which significantly reduces the conflict rate and improves the safety of collaborative operations; 4. High dynamic adaptability: Supports real-time response to new task access, robot malfunctions, and sudden environmental changes. By dynamically adjusting paths and task allocation, it ensures the continuity and stability of delivery tasks. The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0008] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0010] Figure 1 : A schematic diagram of the module structure of the multi-delivery robot path collaborative optimization system of the present invention; Figure 2 : A flowchart illustrating the multi-delivery robot path collaborative optimization scheduling method of the present invention; Figure 3 : Structural diagram of the multi-delivery robot of this invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0012] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0013] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0014] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0015] Please see Figure 1-3 This invention provides a technical solution for a multi-delivery robot path collaborative optimization system and scheduling method: Example 1: Multi-delivery robot path collaborative optimization system The multi-delivery robot path collaborative optimization system of this embodiment specifically includes: 1. Central Scheduling Module: Built on an industrial-grade server, configured with an Intel Xeon E5 processor and 128GB of memory, running a Linux operating system and the ROS (Robot Operating System) navigation framework. The task parsing unit supports parsing XML-formatted task instructions, extracting cargo weight, volume, pickup and delivery coordinates, priority (levels 1-5, with level 1 being the highest), and latest delivery time. The robot status monitoring unit receives robot status data in real time via the MQTT communication protocol, with a sampling frequency of 10Hz. The path planning unit integrates improved DARP, BA*, and TEB algorithms, using the move_base_flex framework to implement algorithm calls and path generation. The conflict coordination unit constructs a spatiotemporal conflict detection model and uses a time window reservation table to manage access permissions for key nodes such as elevators and intersections.

[0016] 2. Delivery Robot Terminal: Utilizing a six-wheel differential drive chassis, measuring 0.68m × 0.62m × 0.74m, weighing 30kg, with a payload capacity of 40kg, equipped with a 24V 60AH lithium-ion battery providing 12 hours of runtime, and supporting wireless charging. The positioning module employs a fusion of LiDAR (model: Velodyne VLP-16) and GPS, achieving a positioning accuracy of 1-3cm. The motion control module uses an STM32F407 microcontroller as the core controller, allowing for adjustable robot speed within the 2-10km / h range and a climbing ability of 35°. The communication module supports dual-mode communication with WiFi 6 and 5G, with data transmission latency ≤80ms. The task execution module features two independent compartments, equipped with ultraviolet disinfection capabilities, and supports automatic door opening and closing and cargo status detection.

[0017] 3. Environmental Perception Module: The fixed sensor network includes LiDAR and high-definition cameras deployed at key nodes in the delivery area to collect global environmental data; the robot's mobile sensors include LiDAR, ultrasonic sensors (detection distance 0.1-5m), and vision cameras to achieve 3D environmental perception. The DBSCAN algorithm is used to cluster the LiDAR point cloud data to identify dynamic obstacles (such as pedestrians and vehicles), and a uniform motion model is used to predict the robot's position within the next 3 seconds, with a prediction error ≤0.5m.

[0018] 4. Map Management Module: This module uses the Cartographer algorithm to construct a global raster map with a grid resolution of 0.1m x 0.1m. It stores information on passageways, elevators, charging points, obstacles, and other features within the delivery area. Real-time data collected by the environmental perception module is published to the map management module via ROS topics, updating the map every 0.5 seconds to achieve real-time labeling of dynamic obstacles.

[0019] Example 2: A method for collaborative optimization and scheduling of multiple delivery robots' paths The multi-delivery robot path collaborative optimization scheduling method of this embodiment is applied to the above system, and the specific steps are as follows: S1: Environment Modeling and Map Update: The map management module uses the Cartographer algorithm to construct a global grid map of the delivery area (such as a high-end office building with an area of ​​20,000㎡, including 10 elevators and 50 office delivery points). Fixed sensors and robot motion sensors collect environmental data in real time, and after removing outliers, the obstacle information in the map is updated, and the position and movement status of dynamic obstacles are marked.

[0020] S2: Task Reception and Parsing: The central dispatch module receives takeout delivery tasks in the office building and parses the task information: pickup point (convenience store on the first floor of the office building, coordinates: (10.2, 5.3)), delivery point (office 1502 on the 15th floor, coordinates: (85.6, 42.8)), task priority level 3, and latest delivery time 15 minutes.

[0021] S3: Robot Status Assessment: Obtain status data of 5 schedulable robots, and filter out 2 robots (R1 and R2) with remaining power ≥30% and in an idle state. R1's current position is (12.5, 6.1) with 85% remaining power; R2's current position is (20.3, 15.7) with 72% remaining power.

[0022] S4: Task Allocation and Area Division: Based on the improved DARP algorithm, starting from the initial positions of R1 and R2, calculate the distance cost of each grid (the cost of obstacle grids is set to infinity), add a boundary curvature penalty term (weight factor 0.3), and divide the office building delivery area into two sub-areas. R1 is responsible for floors 1-10 and the area around the convenience store on the first floor, and R2 is responsible for floors 11-20. This task is assigned to R1 for execution.

[0023] S5: Path Planning: For task R1, the initial path is planned along a parallel line using the "ox-plowing" method, from the current position to the pickup point, and then to the elevator entrance. When encountering obstacles, an obstacle avoidance path is planned using the BA* algorithm, connecting to the ox-plowing path. Finally, the path is optimized using the TEB algorithm. The robot's maximum linear speed is set to 10km / h, maximum acceleration to 0.5m / s², and minimum turning radius to 0.6m, generating a smooth path with a total length of 120m and an estimated travel time of 8 minutes.

[0024] S6: Conflict Detection and Avoidance: The central dispatch module detects that R1's planned path requires the use of elevator No. 3, which is currently occupied by R3 (performing a delivery task on the 10th floor). The module allocates elevator usage time to R1 through a time window reservation mechanism (2 seconds after R3 leaves the elevator). When planning a path in the corridor on the 15th floor, a dynamic obstacle (pedestrian, moving at a speed of 1.2 m / s, facing the elevator entrance) is detected. A distributed negotiation strategy is adopted, and R1 decelerates to 2 km / h, moves to the side of the corridor, and resumes its original speed after the pedestrian has passed.

[0025] S7: Dynamic Adjustment: During the delivery task performed by R1, a new level 4 priority task is added (pickup point is the same as before, delivery point is office 1508 on the 15th floor). The central dispatch module re-evaluates the status of R1 (remaining power 78%, current location: (80.1, 40.5)), determines that R1 can perform the task along the way, replans the route, adds a route length of 50m, and is expected to take an additional 3 minutes, still meeting the latest delivery time requirement, and sends a route adjustment instruction to R1.

[0026] In this embodiment, through the synergistic effect of the system and method, the conflict rate of multi-robot delivery is reduced from 12.3% in the traditional method to 0.7%, the average delivery time per task is shortened by 15%, and the task completion rate reaches 99.2%, which significantly improves delivery efficiency and safety.

[0027] The above description is merely a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter alterations to these embodiments within the spirit and principles of the present invention, achieved through conventional substitutions or by achieving the same function without departing from the principles and spirit of the present invention, fall within the scope of protection of the present invention.

Claims

1. A multi-delivery robot path collaborative optimization system, characterized in that, include: The central scheduling module (001) is used to receive delivery task information, robot status information and environmental perception information, execute task allocation and path planning algorithms, and send scheduling instructions to each delivery robot. Multiple delivery robot terminals (002), each of which is equipped with a positioning module (201), a motion control module (202), a communication module (203) and a task execution module (204). The positioning module (203) is used to obtain the robot's real-time location information, the motion control module (202) is used to respond to scheduling instructions to achieve autonomous movement, the communication module (203) is used to interact with the central scheduling module, and the task execution module (204) is used to complete the loading, delivery and unloading of goods. The environmental perception module (003) includes a network of fixed sensors (301) deployed in the delivery area and a mobile sensor (302) carried by the robot, used to collect static obstacle information, dynamic obstacle information and traffic status information in the delivery area, and transmit the information to the central scheduling module (001). The map management module (004) is used to store a global grid map of the delivery area and dynamically update the map information based on the real-time data from the environmental perception module. The global grid map includes area markers that the robot can pass through, obstacle area markers, and key node markers such as elevators and passageways.

2. The system according to claim 1, characterized in that, The central scheduling module includes: The task parsing unit (101) is used to extract cargo information, pickup point coordinates, delivery point coordinates, task priority and time requirements from the delivery task; The robot status monitoring unit (102) is used to monitor the remaining battery power, load status, current position and operating status of each delivery robot in real time; The path planning unit (103) uses the improved DARP algorithm to divide the region, combines the BA* algorithm to generate the initial path, and then optimizes the path smoothness through the Time Elastic Band (TEB) algorithm. The conflict coordination unit (104) is used to detect spatiotemporal conflicts in multi-robot paths and resolves conflicts using a time window-based reservation and distributed negotiation strategy.

3. The system according to claim 1, characterized in that, The positioning module (203) adopts a fusion positioning method of lidar and GPS, and the positioning accuracy reaches 1-3cm; the motion sensor (302) includes lidar (3021), ultrasonic sensor (3022) and vision camera (3023), which are used to realize 3D environmental perception and dynamic obstacle recognition.

4. The system according to claim 1, characterized in that, The communication module supports WiFi, 5G and LAN multi-mode communication, ensuring data transmission latency of less than 100ms.

5. A method for collaborative optimization scheduling of multiple delivery robot paths, characterized in that, Includes the following steps: S1: Environmental modeling and map updating. A global grid map of the delivery area is constructed through the map management module (004), and the environmental perception module (003) collects environmental data in real time and dynamically updates the obstacle and traffic status information in the map. S2: Task reception and parsing. The central scheduling module (001) receives the delivery task and parses it to obtain key task information, including the pickup point, delivery point, priority and time constraints. S3: Robot status assessment, obtain the real-time status of each delivery robot, including remaining battery power, load capacity, current location and idle status, and filter out the set of robots that can be scheduled; S4: Task allocation and area division. Based on the improved DARP algorithm, the delivery area is divided into multiple sub-areas according to the initial position of the schedulable robots and the task distribution, so as to achieve balanced task load distribution. S5: Path planning. For the task in each sub-region, the BA* algorithm is used to generate an initial path covering the pickup and delivery points. The ox-plowing method is combined to optimize the path coverage efficiency. The TEB algorithm is then used to optimize the path smoothness and constrain the robot's speed and acceleration. S6: Conflict detection and avoidance. The central scheduling module (001) detects the spatiotemporal overlap of multiple robot paths and uses a time window reservation strategy to allocate the passage time of key nodes. For overtaking, intersection and oncoming conflicts, avoidance zone parking, priority passage and side rail avoidance strategies are adopted respectively. S7: Dynamic adjustment. During the delivery process, if a new task, robot malfunction, or sudden environmental change is received, the central scheduling module (001) will re-execute steps S4-S6 to achieve dynamic optimization of the path and task.

6. The method according to claim 5, characterized in that, The improved DARP algorithm described in step S4 calculates the distance cost of each grid using the wavefront algorithm, adds a boundary curvature penalty term, generates smooth sub-region boundaries, and reduces the number of robot turns.

7. The method according to claim 5, characterized in that, The dynamic obstacle processing described in step S6 includes: clustering the lidar point cloud data using the DBSCAN algorithm to identify dynamic obstacles, predicting the future position of obstacles using a uniform motion model, and optimizing the TEB obstacle avoidance function by fusing distance, time, and velocity direction constraints.

8. The method according to claim 5, characterized in that, The dynamic adjustment in step S7 also includes charging planning based on the robot's remaining battery power. When the robot's battery power is below a threshold, a path to the nearest charging point is planned first.

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