AGV Task Scheduling and Wireless Charging for Due-Time Delivery
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Solution Overview
Problem
Existing AGV management systems are inefficient in task assignment and battery recharge management, often wasting resources by assigning tasks too early and lacking flexibility in dynamic charging policies, which can lead to insufficient battery charge and increased transit times under environmental uncertainty.
Innovation Solution
An AGV management system with a battery recharge management module, task management module, and AGV path planning module that prioritizes idle AGVs with higher battery charge for task assignment, employs dynamic charging policies, and uses A* algorithm with hybrid receding horizon/incremental scheduling for path planning to ensure timely and efficient task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If immediate task assignment is performed whenever new tasks are generated, then task completion speed is improved, but AGV resource waste increases due to premature assignment
Solution Approach 1:
The system performs preliminary evaluation of task urgency and AGV readiness before assignment. By assessing whether tasks are truly ready for assignment and whether AGVs are ready to execute them, the system avoids premature assignments that would waste AGV resources while still maintaining rapid response to genuine task needs.
Solution Approach 2:
The system continuously monitors task status, AGV battery levels, and delivery deadlines, using this feedback to dynamically adjust assignment timing. This feedback mechanism enables the system to assign tasks at the optimal moment - neither too early (wasting resources) nor too late (delaying completion).
2Device complexity
If static charging policy is used with constant battery threshold, then charging management is simplified, but operational flexibility is reduced under varying task demands
Solution Approach 1:
The system dynamically adjusts charging thresholds and policies based on real-time operational conditions such as task urgency, AGV availability, and delivery deadlines. Instead of using a fixed battery threshold, the system adapts charging decisions to current operational needs, maintaining simplicity while significantly improving flexibility and responsiveness.
Solution Approach 2:
The system changes charging parameters (thresholds, timing, priority) based on operational context. When tasks are urgent or AGVs are scarce, the system adjusts charging parameters to ensure AGVs remain available, while relaxing parameters during low-demand periods to optimize battery management.
3Productivity
If AGVs are assigned tasks without sufficient battery charge, then task assignment efficiency is improved, but transit time increases due to mid-task recharging
Solution Approach 1:
The system ensures AGVs have sufficient battery charge before task assignment by performing preliminary battery checks and arranging charging in advance. This preliminary action prevents mid-task recharging interruptions that would extend transit time, while maintaining efficient assignment by having AGVs ready to execute tasks immediately when assigned.
Solution Approach 2:
The system builds a battery charge cushion before task assignment, ensuring AGVs have enough charge to complete assigned tasks without interruption. This cushioning approach absorbs the time cost of charging in advance rather than during task execution, maintaining assignment efficiency while preventing transit time extensions.
4Productivity
If path planning does not account for environmental uncertainty, then planning speed is improved, but bottleneck reduction and transit time optimization are compromised
Solution Approach 1:
The path planning system dynamically adjusts routes based on real-time environmental conditions such as AGV positions, task priorities, and delivery deadlines. This dynamic planning accounts for environmental uncertainty by adapting to changing conditions, reducing bottlenecks and optimizing transit times while maintaining planning speed through efficient algorithms.
Solution Approach 2:
The system continuously receives feedback on AGV positions, task progress, and environmental conditions, using this information to adjust path planning in real-time. This feedback-driven approach enables the system to account for environmental uncertainty and optimize routes dynamically, reducing transit times and bottlenecks without sacrificing planning speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system ensures just-in-time task assignment, maintains sufficient battery charge for AGVs, reduces transit times, and optimizes path coordination under environmental uncertainty, thereby improving operational efficiency and reducing resource wastage.
Implementation Method 1
managing a plurality of AGVs to be recharged by at least one wireless charging unit in a parking area
Data Source
AI summary
An automated guided vehicle (AGV) management system including a battery recharge management module, a task management module, and an AGV path planning module is provided. The battery recharge management module manages the AGVs to be recharged by at least one wireless charging unit in a parking area. The AGV leaving the parking area has a battery charge higher than a charge threshold. The task management module receives tasks and assigns the tasks to the AGVs. The task includes information including at least one pick-up location, at least one drop-off location, and a due time. The AGV path planning module plans paths for the AGVs, respectively, according to the information of the assigned tasks. The task management module delays assigning the task to the AGV if the AGV is expected to complete the task earlier than the due time of the task.