Robotic Task Re-Planning for Autonomous Execution Under Change
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Solution Overview
Problem
Conventional robotic task planning systems rely on centralized management, requiring continuous network connectivity and user intervention, and fail to adapt to changes in the environment, leading to potential plan failures.
Innovation Solution
A processor-implemented method and system for task planning in a robotic environment that generates and executes task plans, detects deviations through observations, and invokes a re-planning mechanism to update plans based on changes in objects or actions, using perception queries and granularity approaches to ensure continuous operation without external control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized system manages task planning, then task coordination is improved, but system reliability deteriorates due to network dependency
Solution Approach 1:
The patent divides the centralized task planning system into distributed autonomous agents, each capable of independent decision-making. This segmentation eliminates single-point failure risks associated with centralized control while maintaining coordination through peer-to-peer communication protocols.
Solution Approach 2:
The patent implements self-service mechanisms where autonomous agents independently perform task planning, execution monitoring, and adaptive re-planning without requiring continuous external intervention. This self-sufficiency improves reliability by reducing dependency on external network connectivity.
2Ease of operation
If robots operate on a fixed plan, then execution simplicity is improved, but adaptability deteriorates when environmental changes occur
Solution Approach 1:
The patent transforms static fixed plans into dynamic adaptive plans that can be modified in real-time. The system continuously monitors environmental parameters and automatically adjusts task execution sequences, allowing robots to respond to unexpected changes while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The patent implements feedback loops where execution observations are continuously compared with planned actions. When deviations are detected, the system automatically triggers re-planning mechanisms that generate updated task sequences based on current environmental conditions, ensuring both simplicity and adaptability.
3Productivity
If design time planning is performed, then task preparation is improved, but response time to environmental changes deteriorates
Solution Approach 1:
The patent performs preliminary design-time planning to generate initial task plans, but augments this with run-time adaptive capabilities. The system prepares contingency plans and decision frameworks in advance, enabling rapid response to environmental changes without requiring complete re-planning from scratch.
Solution Approach 2:
The patent implements partial re-planning mechanisms that only regenerate affected portions of task plans when environmental changes occur, rather than performing complete re-planning. This selective approach minimizes response time while maintaining overall task coherence and preparation efficiency.
Data Source
AI summary
Robots are deployed for handling different tasks in various field of applications. For the robots to function, task planning is required to be done. During the task planning, goal setting is done, as well as actions to be executed for corresponding to each goal are decided. Traditionally, this is carried out first and then the robots start executing the task plan, thereby failing to capture any change in the environment the robots operate, post the task plan generation. Disclosed herein is a method and system for robotic task planning in which a task plan is generated and is executed. However if the task execution fails due to change in any of the parameters/factors, then the system dynamically invokes an adaptation and re-planning mechanism which either updates the already generated task plan (by capturing the change) or generates a new task plan, which the robot can execute to achieve the goal.