Robotic Task Scoring for Speed-Safety Trade-offs
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Solution Overview
Problem
Robotic systems face challenges in efficiently selecting and executing tasks due to the limitations of their individual components, such as speed, efficiency, and safety, which are not effectively balanced in existing technologies.
Innovation Solution
A robotic system that determines an external environment state and calculates task group scores based on expected performance capabilities of its actuators to select the most optimal task group for achieving a goal, considering factors like speed, safety, and efficiency, and then executes the selected task group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robotic system prioritizes speed in task execution, then productivity is improved, but safety and reliability deteriorate
Solution Approach 1:
The robotic system dynamically adjusts task selection and execution parameters based on real-time environmental states and actuator performance capabilities. The system evaluates multiple possible task groups and selects the optimal one by balancing competing objectives (speed, safety, efficiency) through dynamic scoring rather than static prioritization, allowing the system to adapt its behavior to current conditions.
Solution Approach 2:
The system changes operational parameters by calculating task group scores that incorporate multiple factors including expected performance capability, environmental state, and actuator attributes. This multi-parameter evaluation approach allows the system to optimize task selection by considering trade-offs between speed, safety, and efficiency simultaneously rather than optimizing for a single parameter.
2Adaptability or versatility
If the robotic system uses multiple actuators for complex tasks, then versatility is improved, but device complexity worsens
Solution Approach 1:
The robotic system segments complex tasks into distinct task groups, where each task group represents a coherent set of actions that can be executed by specific actuators. This segmentation allows the system to manage complexity by breaking down versatile task requirements into manageable, scoreable units rather than treating the entire system as a monolithic complex entity.
Solution Approach 2:
The task scoring and selection mechanism serves multiple functions simultaneously: it evaluates task feasibility, optimizes for performance criteria, manages actuator coordination, and adapts to environmental conditions. This universal evaluation framework handles diverse task requirements without requiring separate control mechanisms for each function, thereby managing complexity while maintaining versatility.
Data Source
AI summary
The present disclosure relates to methods and systems for improving robotic task selection and execution. An example method includes determining an external environment state based on receiving information indicative of at least one aspect of an environment proximate to a robotic system having at least one actuator. The method also includes optionally determining a goal to be accomplished by a robotic system and determining a plurality of possible tasks each including at least one task involving the at least one actuator that may be carried out by the robotic system in pursuance of the goal. For each possible task group, the method includes determining a respective task group score based on an expected performance capability. The expected performance capability is based, at least in part, on the external environment state. The method includes selecting a task group based on the respective task group scores.