Cluster Management for Robot Applications Using Virtual Containers
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Solution Overview
Problem
Current robot systems with distributed computation systems face challenges in managing and controlling multiple computers, leading to inefficient resource utilization and complexity, particularly due to the lack of defined management mechanisms in Robot Operating System (ROS), which results in difficulties in continuous development and optimization.
Innovation Solution
A robot application management device that executes robot applications by utilizing virtual containers, allowing for the management of robotic devices and computer devices as a cluster, enabling the placement and activation of virtual containers across the cluster, thereby optimizing resource utilization and simplifying management and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a distributed computation system is used to compose the robot system from multiple computers, then the system's computational power and functionality are improved, but the complexity of managing and controlling multiple computers increases significantly
Solution Approach 1:
The patent merges multiple independent computer devices into a unified cluster managed by a single management device. The management device consolidates control functions for all cluster members, including process execution, monitoring, and resource allocation, thereby reducing the effective management complexity while preserving the distributed computational power.
Solution Approach 2:
The management device performs multiple functions simultaneously: it acts as a control center for process management, a monitoring station for system status, a resource allocator for computation tasks, and a coordination hub for inter-device communication. This multi-functionality reduces the need for separate specialized management systems for each function.
2Ease of operation
If manual login to each computer is performed for individual process execution, then precise control over each device is achieved, but the time and effort required for system operation increases
Solution Approach 1:
The management device enables automated self-service operations for the cluster. It automatically executes processes on appropriate cluster members, monitors their status, handles errors, and manages resource allocation without requiring manual intervention. The system serves itself by making deployment decisions based on device capabilities and current load conditions.
Solution Approach 2:
The management device acts as an intermediary between the operator and the cluster devices. Instead of the operator directly logging into each device, all control operations are routed through the management device, which translates high-level commands into device-specific actions and aggregates status information from multiple sources into a unified view.
3Adaptability or versatility
If different developers handle applications with physical restrictions in different ways, then flexibility in adapting to specific hardware is achieved, but consistency and reproducibility of development processes are reduced
Solution Approach 1:
The system manages hardware adaptability through parameterized device profiles that describe the capabilities and restrictions of each cluster member. Instead of hardcoding different handling approaches for different hardware, the management device dynamically adjusts execution parameters based on device profiles, maintaining consistent development processes while adapting to various hardware configurations.
Solution Approach 2:
The patent applies local quality by assigning specific roles and capabilities to different cluster members based on their hardware characteristics. Devices with specific physical restrictions are designated for particular types of tasks, while the management device routes applications to appropriate devices based on their capabilities, thereby maintaining development consistency through systematic resource allocation rather than ad-hoc handling.
4Productivity
If virtualization techniques are used to optimize development and operation monitoring, then the efficiency of system management is improved, but hardware recognition issues arise in hypervisor-based environments
Solution Approach 1:
The system segments the virtualization approach by using container-based virtualization instead of full hypervisor-based virtualization. Containers provide process-level isolation and resource management without the hardware abstraction layer that causes recognition issues, thereby maintaining hardware accessibility while still providing virtualization benefits for development and monitoring efficiency.
Data Source
AI summary
A robot application is executed by executing a plurality of kinds of virtual containers in cooperation with each other. To this end, a robot application management device (100), at least one robot device (300) and at least one computer device (400) are connected to each other via a local area network (600). A group of devices including these devices are managed as a cluster for executing the robot application, and each virtual container is placed and activated in any of the group of devices composing the cluster.