Robotic Task Planning With Dynamic Re-Planning for Environmental Changes
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Solution Overview
Problem
Existing robotic task planning systems rely on centralized systems that require continuous communication and fail to adapt to changes in the environment, leading to plan failures and the need for user intervention.
Innovation Solution
A decentralized system that uses hardware processors to generate and execute task plans, detect deviations, and invoke re-planning mechanisms to adapt to changes in the environment, such as object state changes or new objects, by updating or generating new task plans using perception queries and selecting appropriate granularity approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized system manages task planning, then task planning can be coordinated, but the system requires continuous communication and fails when connection is interrupted
Solution Approach 1:
The patent segments the centralized task planning system into distributed autonomous units. Each robot is equipped with local planning capabilities that allow it to independently generate and execute task plans without requiring continuous communication with a central system. This segmentation eliminates the single point of failure inherent in centralized systems while maintaining coordinated task execution through peer-to-peer communication only when necessary.
Solution Approach 2:
The patent implements self-service by enabling robots to autonomously perform task planning and adaptation without external intervention. The robotic system includes local sensors, processors, and actuators that work together to detect environmental changes, re-plan tasks, and execute adjustments independently. This self-service capability ensures continuous operation even when communication infrastructure fails.
2Ease of operation
If robots operate on a fixed plan, then task execution is straightforward, but they fail to capture changes in environment parameters
Solution Approach 1:
The patent applies dynamics by making the task plan adaptive rather than static. The robotic system continuously monitors environmental parameters using local sensors and dynamically adjusts the task plan when changes are detected. The plan transitions from a fixed sequence of operations to a flexible framework that can accommodate environmental variations while maintaining the overall task objective.
Solution Approach 2:
The patent implements feedback mechanisms where the robotic system continuously observes environmental parameters and compares them against the current task plan. When deviations are detected, the system triggers re-planning processes that generate updated plans based on the new environmental state. This closed-loop feedback ensures the robot adapts to changes while maintaining straightforward execution of the updated plan.
3Reliability
If centralized systems manage task planning, then coordination is possible, but user intervention is required during planning
Solution Approach 1:
The patent enables the robotic system to perform task planning autonomously without requiring user intervention. Each robot is equipped with local planning algorithms that automatically generate task plans based on environmental observations and task objectives. The system handles all planning decisions, environmental adaptations, and execution adjustments independently, achieving full automation while maintaining reliable task completion.
Data Source
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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.