Hybrid Robot Plan Execution Engine
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Solution Overview
Problem
Current systems face challenges in controlling complex plan execution by multiple autonomous robots, including task assignment, synchronization, and handling dynamic environments, due to limited processing power and the need for quick decision-making in collaborative tasks.
Innovation Solution
A system and method that utilize a plan execution engine to manage the lifecycle of plan execution, allowing for centralized or decentralized control, with the engine determining task allocation and constraint solutions in real-time, and enabling flexible control mechanisms through a graphical user interface that models plans as sequences of tasks and transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple robots collaborate to execute complex plans, then task completion capability is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the complex plan execution control into distributed components across multiple robots and a central server. Each robot executes local tasks autonomously while the server coordinates overall plan management, dividing the computational burden and enabling parallel processing that reduces total execution time.
Solution Approach 2:
The patent introduces a central server as an intermediary that mediates between multiple robots executing complex plans. The server handles centralized coordination and computation-intensive tasks, while robots perform localized execution, creating a distributed architecture that balances processing loads and reduces overall computation time.
2Productivity
If centralized control is used for plan execution, then coordination efficiency is improved, but processing power requirements increase
Solution Approach 1:
The patent segments control authority between a central server and individual robots. The server handles high-level plan coordination and task assignment, while robots independently manage local task execution and basic decision-making, distributing processing power requirements across the system.
Solution Approach 2:
The patent transitions from a purely centralized or purely decentralized control model to a hybrid hierarchical architecture. Control is distributed across multiple dimensions - centralized for strategic coordination and decentralized for tactical execution - allowing the system to leverage both approaches without requiring all processing power at one location.
3Power
If decentralized control is used for plan execution, then processing power requirements are reduced, but coordination complexity increases
Solution Approach 1:
The patent segments the control architecture into hierarchical layers, with the central server managing high-level coordination logic and individual robots handling local execution decisions. This segmentation reduces the coordination complexity at each node while maintaining overall system coherence through the server's centralized oversight.
4Device complexity
If a single robot controls plan execution, then device complexity is reduced, but task execution capability deteriorates
Solution Approach 1:
The patent creates a universal control architecture where the central server provides multi-functional capabilities for coordinating complex plans across multiple robots, while each robot maintains its own execution capabilities. This multi-functional system can handle both simple and complex tasks, scaling from single-robot to multi-robot scenarios without requiring completely different control architectures.
Data Source
AI summary
A system and method to control execution of a centralized and decentralized controlled plan, has been described. A plan execution engine executing at a cloud node, receives sensor data captured by one or more sensors at a plurality of autonomous robots and a plan execution status of the centralized controlled plan. The plan execution engine executing at the cloud node, determines whether the plurality of autonomous robots satisfy a transition condition. Next a determination is made one or more activated constraints and task allocation for one or more autonomous robots in the next state. Next the plan execution engine executing at the cloud node and autonomous robots collaboratively determine a constraint solution for the activated one or more plan constraints. Finally based on the determined constraint solution, the one or more plan execution engines sends instructions to an actuator for executing the task included in the centralized controlled plan.