Robot Cargo Sorting With Dynamic Route and Container Handover
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Solution Overview
Problem
Current goods sorting systems, such as cross-belt and robot steel platform systems, face inefficiencies in sorting accuracy and labor costs due to inflexible design, high construction costs, and manual handling, leading to reduced productivity and increased errors.
Innovation Solution
A goods sorting system comprising a control server, delivery robots, and carrying robots that communicate and coordinate to optimize routes and container management, using QR codes or barcodes for route identification and infrared sensors for volume detection, allowing for automated sorting and reduced manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If cross-belt sorting system or robot steel platform sorting system is used, then goods sorting automation is achieved, but construction cost increases and system complexity increases
Solution Approach 1:
The sorting system is divided into independent functional modules: delivery robots for goods delivery, carrying robots for container transport, and collection containers for goods accumulation. Each module operates independently but coordinates through the control server, reducing overall system complexity while maintaining automation.
Solution Approach 2:
The robots are designed with multi-functionality, capable of performing both delivery and carrying tasks. The system can adapt to different sorting scenarios by reconfiguring robot assignments and routes, reducing the need for specialized equipment and lowering construction costs.
2Extent of automation
If cross-belt sorting system is used, then goods sorting is automated, but the number of delivery ports is fixed and extending flexibility is poor
Solution Approach 1:
The system dynamically adjusts delivery routes and container assignments based on real-time conditions. The control server reconfigures robot paths and container allocations automatically, allowing the system to adapt to changing demands without physical modifications to the infrastructure.
Solution Approach 2:
The system changes operational parameters such as delivery routes, container positions, and robot assignments dynamically. This allows the same physical infrastructure to serve multiple configurations and scaling scenarios, improving extending flexibility without increasing construction complexity.
3Device complexity
If manual collection and buffering of goods is performed, then labor cost is reduced, but sorting efficiency decreases and sorting accuracy decreases
Solution Approach 1:
The system implements self-service through automated robot operations. Delivery robots autonomously navigate to containers, deposit goods, and return for new tasks without human intervention. The control server manages coordination automatically, maintaining high sorting efficiency while eliminating manual labor.
Solution Approach 2:
The system uses feedback mechanisms where sensors detect goods delivery status, container fill levels, and robot positions. This real-time feedback enables the control server to optimize routes, reassign tasks, and maintain high sorting accuracy and efficiency automatically without manual monitoring.
4Extent of automation
If robot steel platform sorting system is used, then automation is improved, but robot density is high causing long waiting time and stopping
Solution Approach 1:
The system transitions from a two-dimensional platform-based approach to a three-dimensional space utilization model. Robots operate in vertical and horizontal dimensions simultaneously, with containers arranged in arrays that allow multiple delivery routes. This dimensional expansion reduces robot congestion and waiting time while maintaining high automation levels.
Solution Approach 2:
The control server performs preliminary route planning and container allocation before robots begin delivery tasks. By pre-configuring optimal paths and anticipating bottlenecks, the system minimizes waiting time and stopping during delivery operations, maintaining high automation efficiency.
Data Source
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AI summary
Disclosed are a goods sorting system and method. The system includes a control server (101), a delivery robot (102), and a first carrying robot (103). The control server (101) is configured to determine a delivery port according to a road direction of goods to be delivered, allocate the delivery robot (102), plan a traveling route for the delivery robot (102), generate a delivery instruction and send the delivery instruction to the delivery robot (102). The delivery robot (102) is configured to travel to the delivery port, deliver the goods to be delivered to the delivery port. The control server (101) is further configured to: when the number of goods collected in a target goods collection container below the delivery port is greater than or equal to a preset threshold, allocated the first carrying robot (103), plan a traveling route for the first carrying robot (103), generate a carrying instruction and send the carrying instruction to the first carrying robot (103). The first carrying robot (103) is configured to travel to the target goods collection container according to the traveling route, carry the target goods collection container to a goods collection station. Further disclosed are a server and a storage medium.