Cloud-Based Cumulative Learning for Robot Execution Plans
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Solution Overview
Problem
Autonomous mobile robots (AMRs) face challenges in processing and comparing execution plans due to limited computing capabilities, leading to inefficiencies and delays in determining the most efficient course of action, especially in real-time scenarios.
Innovation Solution
A cloud server-connected system that generates and simulates multiple execution plans across edge node devices, allowing for parallel processing and comparison of metrics to determine an optimal execution plan, which is then updated in real-time across robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots process and compare execution plans locally, then real-time response is achieved, but computing capabilities are insufficient to handle complex comparisons
Solution Approach 1:
A cloud server acts as an intermediary between robots and execution plan comparison. The cloud server receives execution plans from robots, performs complex comparisons using machine learning models, and returns optimized plans. This mediator handles the computational burden that individual robots cannot process alone.
Solution Approach 2:
The system transitions from local 2D robot processing to a 3D hierarchical architecture involving edge devices and cloud servers. Execution plans are processed at multiple levels: locally for simple tasks, at edge nodes for regional coordination, and in the cloud for comprehensive comparison across multiple robots and scenarios.
2Reliability
If robots analyze multiple execution plan options in detail, then optimal plan selection is achieved, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing execution plans in the cloud before they are needed. Machine learning models are pre-trained on historical data to predict outcomes. When a robot needs a plan, the cloud server can quickly retrieve pre-computed plans or generate them using pre-trained models, significantly reducing real-time analysis time.
Solution Approach 2:
Instead of robots physically trying out different execution plans, the system creates virtual copies and simulations of plans in the cloud. Multiple execution plans are simulated in parallel using virtual environments, and their outcomes are compared without physical trial-and-error, dramatically reducing the time needed to find optimal plans.
3Productivity
If robots operate independently without cumulative learning, then operational independence is maintained, but quality improvement is limited
Solution Approach 1:
The system merges individual robot experiences into a collective knowledge base in the cloud. Execution plans and metrics from multiple robots are aggregated, and machine learning models analyze this combined data to identify patterns and optimizations. This merged knowledge is then shared back with individual robots, enabling them to learn from each other's successes and failures.
Solution Approach 2:
The system implements continuous feedback loops where execution metrics from robots are sent to the cloud server, which uses machine learning to analyze performance and generate improved execution plans. These optimized plans are fed back to robots for implementation, creating a closed-loop system that continuously improves operational quality through cumulative learning.
Data Source
AI summary
System and techniques for cumulative learning robot execution plan generation are described herein. A first execution plan report based on execution of a first execution plan by a first robot may be received. Here, the first execution plan report includes a first metric for a first operation of the first execution plan. A second execution plan report based on execution of a second execution plan by a second robot may also be received that includes a second metric for a second operation of the second execution plan. Here, the second operation corresponds to the first operation. The first metric and the second metric are analyzed to determine that the second operation is an improvement to the first operation. Then, a modified first execution plan that replaces the first operation with the second operation may be transmitted to the first robot.