AMR Task Self-Selection to Reduce Warehouse Idle Time
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Solution Overview
Problem
Traditional warehouse automation systems relying on centralized fleet managers for task assignment to autonomous mobile robots (AMRs) lead to wait times and decreased utilization, resulting in increased order fulfillment cycle times and operational delays due to the need for AMRs to remain idle until computing resources are available.
Innovation Solution
Implementing a dynamic tasking system where AMRs can self-select tasks based on information provided by the Warehouse Execution System (WES), allowing them to operate independently and optimize task assignments without relying on a centralized control system, thereby reducing the computational burden on the WES and enhancing throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized fleet manager is used to assign tasks to AMRs, then task assignment control is centralized and coordinated, but AMRs must wait idle until the fleet manager is available, decreasing utilization and increasing cycle times
Solution Approach 1:
The patent segments the centralized fleet manager into distributed autonomous AMR units, each capable of independent task selection and execution. This distributes the computational burden from a single central system to multiple autonomous units, eliminating the bottleneck where AMRs must wait for centralized processing.
Solution Approach 2:
Each AMR is equipped with onboard computing resources that enable it to autonomously select and execute tasks without continuous centralized control. The AMRs serve themselves by making independent decisions about task selection based on their current state and warehouse conditions, eliminating idle wait time.
2Reliability
If a centralized fleet manager processes all work requests, then comprehensive coordination is achieved, but computational resources are bottlenecked, decreasing throughput
Solution Approach 1:
The centralized processing architecture is segmented into distributed autonomous units. Each AMR maintains local knowledge of warehouse layout and task priorities, performing computational work locally rather than requiring all processing to occur at a central fleet manager, thereby increasing overall system throughput.
Solution Approach 2:
The system transitions from a single-dimensional centralized control model to a multi-dimensional distributed architecture where task assignment occurs across multiple levels (centralized WES, regional task queues, and individual AMR decisions), adding dimensional complexity that resolves the throughput bottleneck.
3Reliability
If AMRs rely on centralized fleet manager for next task assignment, then centralized control is maintained, but operational delays increase due to linear two-phase approach
Solution Approach 1:
Instead of the traditional approach where the centralized system waits for AMRs to report completion and then assigns new tasks (linear two-phase approach), the patent inverts the workflow: AMRs proactively select and begin tasks autonomously, with the centralized WES providing oversight and reassignment capabilities when needed.
Solution Approach 2:
AMRs perform preliminary task selection and preparation actions autonomously before centralized confirmation is required. This preliminary autonomous action eliminates the waiting period inherent in the traditional two-phase approach, allowing AMRs to begin productive work immediately.
Data Source
AI summary
A method and system are provided for task allocation for a material handling system with autonomous mobile robots (AMR) operating within a material handling facility. The AMRs are configured to self-select tasks to perform utilizing a computer based warehouse execution system (WES) utilizing a pending workflow list of tasks to be performed within the material handling facility. The AMRs include onboard computers for communicating with the WES and to self-select tasks from the pending workflow list. The AMR may be directed to a prioritized task or task queue, and the WES may lock an AMR to a task or task queue until the task or tasks are performed, or until the AMR determines that the AMR should be reassigned or otherwise relieved of the task or task queue. The AMRs are thus adapted to independently self-select tasks, where the AMR and/or WES may enable the AMR to self-select tasks.