Robot Service Platform for Multi-Robot Task Matching
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Solution Overview
Problem
Current social collaboration networks lack an efficient mechanism for robots and users to collaborate and share information to perform complex tasks, as they are not designed to handle the diverse capabilities and scheduling of multiple robots effectively.
Innovation Solution
A robot service platform that registers and publishes robot profiles, provides an API for robots, and offers a user interface for service requests, allowing qualified robots to be selected and assigned tasks based on their capabilities and availability, enabling decentralized collaboration among robots and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot service platform registers and publishes robot profiles to enable discovery, then robots and users can collaborate on service requests, but the system complexity increases due to profile management and matching mechanisms
Solution Approach 1:
The robot service platform serves multiple functions: it registers robots, publishes profiles, receives service requests, matches robots to tasks, and manages scheduling. This multi-functional approach enables diverse collaboration scenarios while consolidating complexity into a single unified system rather than requiring separate systems for each function.
Solution Approach 2:
The platform acts as an intermediary between robots and users, managing the complexity of profile publication, discovery, and matching. Instead of robots directly interacting with users in complex ways, the platform mediates these interactions through standardized interfaces and automated matching algorithms.
2Productivity
If the platform divides tasks among multiple robots based on capabilities and availability, then service requests can be completed more efficiently, but the task assignment and scheduling complexity increases
Solution Approach 1:
The platform segments service requests into discrete tasks that can be assigned to individual robots based on their capabilities. By breaking down complex service requests into smaller, manageable task units, the system can efficiently match tasks to suitable robots while reducing the overall complexity of task assignment through modular processing.
Solution Approach 2:
The platform uses parameter-based matching, where robot profiles contain parameters (capabilities, availability, skills) and service requests specify required parameters. The matching algorithm automatically compares these parameters to assign tasks, transforming the complex task assignment problem into a parameter-matching process that is more manageable and scalable.
3Reliability
If the system collects and evaluates operational data from multiple robots, then collective intelligence can be aggregated for decision-making, but the data processing and storage requirements increase
Solution Approach 1:
The platform extracts relevant operational data from robots' activities and isolates the most valuable information for collective intelligence aggregation. Instead of processing all raw data from multiple robots, the system extracts and focuses on key performance indicators, task completion data, and operational metrics that are most useful for improving decision-making quality.
Solution Approach 2:
The platform performs preliminary data processing and filtering at the source, preparing and validating operational data before it is aggregated. By preprocessing data at the robot level and only transmitting essential information to the platform, the system reduces the volume of data that needs to be stored and processed centrally while maintaining the quality needed for reliable decision-making.
4Ease of operation
If the platform provides API and user interface for service requests, then accessibility and ease of use improve, but the interface and communication overhead increases
Solution Approach 1:
The platform provides multiple access interfaces (API for programmatic access, user interface for direct interaction) that serve different user needs and preferences. This universal access approach allows the same core service request functionality to be accessed through different channels, improving ease of operation while consolidating the complexity into a single backend system that handles all interface types.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to share information in a community of robots and users to perform tasks. In one aspect, a method includes registering a plurality of robots in a system including creating for each robot a robot profile; publishing the robot profile; collecting operational data related to performance of tasks, the operational data including situational awareness information from at least a first of the plurality of registered robots; evaluating the collected operational data including performing statistical analysis, modeling, and extrapolation using the collected operational data; and in response to a request to transfer relevant data to at least a second of the plurality of registered robots, determining relevant data from the evaluated collected operational data, the relevant data including at least a portion of the situational awareness information; and sending the relevant data to at least the second registered robot.