Robot Fleet Management via Universal Service Platform
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Solution Overview
Problem
The integration of robot fleets in industrial facilities is complex due to the lack of common protocols and communication methods among robots of varying types and capabilities, making it difficult to efficiently assign and manage missions effectively.
Innovation Solution
A system comprising a robot service platform with data and control adapters that transform feedback and control information into a common format, allowing a mission manager to select and assign robots based on their capabilities and mission requirements, enabling efficient task management across different robot types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robots of varying types and capabilities from different manufacturers are integrated into industrial facilities, then the range of tasks that can be performed increases, but the complexity of integrating robot fleets increases due to lack of common protocols and communication methods
Solution Approach 1:
The patent implements a universal service platform that can manage multiple types of robots from different manufacturers through a common interface. The platform uses standardized service descriptions and task definitions that are independent of specific robot types, allowing the same platform to orchestrate diverse robot fleets without requiring manufacturer-specific integration logic for each robot type.
Solution Approach 2:
The service platform acts as an intermediary layer between the facility management system and the diverse robot fleet. It translates high-level task requirements into robot-specific commands and aggregates robot feedback into standardized status reports, mediating between the need for task diversity and the challenge of integration complexity.
2Productivity
If the number of robots and robot-assigned tasks increase, then the capability to handle complex facility operations improves, but the complexity of integrating robot fleets increases
Solution Approach 1:
The patent segments the robot management system into modular components: task definition modules, service description modules, robot assignment modules, and feedback processing modules. Each component handles specific aspects of robot management independently, allowing the system to scale to handle increasing numbers of robots and tasks without proportionally increasing overall integration complexity.
Solution Approach 2:
The service platform provides universal functions that work across all robot types and task categories, including standardized task definition, robot capability matching, and performance monitoring. This universal approach allows the system to manage large numbers of diverse robots through consistent processes rather than requiring separate integration logic for each robot-task combination.
3Ease of operation
If common protocols and communication methods are implemented across robot fleets, then the ease of managing robot missions improves, but the adaptability to different robot types decreases
Solution Approach 1:
The service platform implements universal service descriptions and task definitions that work across all robot types. The platform maintains robot-type-agnostic task specifications while accommodating diverse robot capabilities through flexible assignment logic that matches tasks to appropriate robot types based on their specific capabilities.
Solution Approach 2:
The system allows task definitions and service descriptions to have local quality adaptations for different robot types. While the overall framework and communication protocols are standardized, the platform can apply robot-specific parameters, capabilities, and constraints at the local level when assigning and executing tasks, ensuring both ease of management and robot type compatibility.
Data Source
AI summary
A system or a method includes defining missions based on factors associated with the missions or environmental data associated with the system, assigning the missions to the fleet of robots based on capabilities of the robots, generating a schedule of the missions and the robots, and managing the fleet of robots using feedback.