Distributed Robot Fleet Management for Remote Training and Configuration
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Solution Overview
Problem
Existing systems face challenges in efficiently configuring and managing fleets of dynamic mechanical systems, such as robots, particularly in unstructured and semi-structured environments, due to difficulties in data management, training, and resource allocation.
Innovation Solution
A distributed robot management system with a robot management server system that provides configuration information, maintains robot state representation, receives and stores data, and offers training resources, utilizing modules like experience collection, configuration, and training to manage and configure robot fleets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a distributed robot management system is implemented to manage fleets of robots, then the adaptability and performance of robot fleets are improved, but the device complexity and data management burden increase
Solution Approach 1:
The system segments robot management into distinct functional modules: experience collection module for data acquisition, configuration module for parameter management, and training module for model development. This modular segmentation allows each component to handle specific tasks independently, improving adaptability while managing complexity through functional decomposition.
Solution Approach 2:
The server system acts as an intermediary between individual robots and external control systems. It collects experiences from multiple robots, processes this data through training modules, and distributes optimized configurations back to the fleet. This intermediary approach centralizes complex data management while allowing individual robots to remain relatively simple.
2Productivity
If centralized data handling is used to manage robot fleets, then the productivity and training efficiency are improved, but the loss of time for data transmission and processing increases
Solution Approach 1:
The system performs preliminary actions by collecting experiences and training models in advance before deploying updated configurations to the robot fleet. The training module pre-processes data and generates optimized models, which are then distributed to robots. This preliminary processing reduces real-time computational requirements and minimizes operational downtime.
Solution Approach 2:
The experience collection module continuously gathers data from robots during normal operations, and the training module continuously processes this data to improve models. This continuous cycle of data collection, processing, and deployment ensures that the fleet constantly improves without interrupting operational productivity.
Data Source
AI summary
Provided is a distributed robot management system, including: a first fleet of robots at a first facility; and a robot management server system remote from the first facility and communicatively coupled with the first fleet of robots via a network, wherein the robot management server system is configured to: provide configuration information to the first fleet of robots, maintain a remote representation of state of robots in the first fleet of robots, receive and store data from the first fleet of robots, and provide computing resources by which robots in the first fleet of robots are trained.