Robot Collaboration System with Dynamic Task Grouping
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Solution Overview
Problem
Current systems lack mechanisms for robots and drones to effectively collaborate, communicate, and optimize task execution, leading to inefficiencies and resource wastage, as they require predefined tasks and lack adaptive workflow generation and conflict avoidance protocols.
Innovation Solution
A method and system that assigns color tags based on functional capabilities, dynamically creates task groups, elects chief and prime robots, and uses a neural network to generate optimal task sequences, ensuring seamless execution and conflict prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots are assigned predefined tasks with fixed sequences, then task execution reliability is improved, but system adaptability deteriorates
Solution Approach 1:
The patent implements dynamic task sequence generation where robots adapt their execution order based on real-time conditions. The system transitions from static predefined sequences to dynamic adaptive sequencing, allowing robots to reorganize task execution based on current system state, resource availability, and task priorities while maintaining reliable completion.
Solution Approach 2:
The patent enables robots to autonomously determine their own task execution sequences through self-organization mechanisms. Each robot can independently assess its capabilities, current workload, and task requirements to self-determine the optimal execution order, reducing dependency on centralized predefined instructions while maintaining system reliability.
2Manufacturing precision
If domain experts define detailed task sequences, then task completion accuracy is improved, but system complexity and maintenance resources increase
Solution Approach 1:
The patent transfers task sequencing intelligence from external domain experts to the robot system itself. Robots use embedded algorithms to automatically generate and adjust task sequences based on their capabilities and task requirements, eliminating the need for complex expert-defined sequences while maintaining accurate task completion.
Solution Approach 2:
The patent introduces an intermediary task management layer that translates high-level task objectives into executable sequences automatically. This intermediary system handles the complexity of sequence generation, allowing domain experts to define only task goals rather than detailed execution steps, thereby reducing system complexity while maintaining accuracy.
3Device complexity
If robots work independently without collaboration protocols, then system simplicity is improved, but task execution efficiency deteriorates
Solution Approach 1:
The patent merges individual robot capabilities into coordinated collaborative teams. By combining multiple robots with complementary skills to work on tasks simultaneously, the system achieves higher productivity than individual robots could accomplish alone, while maintaining relatively simple individual robot designs.
Solution Approach 2:
The patent creates universal collaboration protocols that enable robots with different functions to work together seamlessly. The communication framework is designed to be function-agnostic, allowing robots with varied capabilities to coordinate efficiently without requiring complex specialized protocols for each robot type.
4Reliability
If comprehensive communication protocols are implemented, then collaboration effectiveness is improved, but communication overhead and system complexity increase
Solution Approach 1:
The patent extracts and standardizes only the essential communication elements needed for robot collaboration. By identifying and implementing only the critical communication protocols required for task coordination, the system reduces communication overhead while maintaining effective collaboration, eliminating unnecessary communication complexity.
Data Source
AI summary
A method, device and system for managing collaboration amongst robots is disclosed. The method may include assigning a color tag from a set of predefined color tags to each of a plurality of robots, based on associated functional capabilities. The method may further include dynamically creating a plurality of groups for a plurality of tasks based on at least one attribute associated with each of the plurality of tasks and functional capabilities associated with the plurality of robots. The method may include electing a plurality of chief robots for the plurality of groups based on a first predefined logic. The method may include selecting a prime robot from the plurality of chief robots based on a second predefined logic and the selected prime robot may be configured to monitor activity of each of the plurality of groups and each robot in each of the plurality of groups.