Decider Networks for Reactive Robot State-Based Control
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Solution Overview
Problem
Conventional robotic systems face challenges in reacting to changes in their environment due to complex control algorithms and limited ability to adapt to unforeseen events, often requiring physical or virtual barriers to interact with humans or objects safely.
Innovation Solution
The implementation of a decider network that uses an acyclic graph structure with nodes to evaluate environmental states and determine actions, allowing for reactive decision-making by identifying logical states and updating them periodically to adapt to changes, enabling task-aware automation and improved reactivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional control algorithms are used to operate robotic systems, then the robot can perform basic operations, but the robot cannot react effectively to changes in the environment and requires highly complex control algorithms or exception-based approaches
Solution Approach 1:
The control algorithm is segmented into multiple discrete states representing different environmental conditions and robot configurations. Each state contains specific actions that can be executed when certain conditions are met, allowing the robot to react to environmental changes without requiring complex continuous control algorithms. The states are organized in a structured format that enables efficient evaluation and transition between different behavioral modes.
2Adaptability or versatility
If exception-based approaches are used to account for environmental changes, then the robot can handle unforeseen events, but the control system becomes highly complex
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple states that anticipate potential environmental changes and robot needs. Each state contains predetermined actions that can be executed when specific conditions occur, eliminating the need for complex real-time exception handling. The structured state evaluation process proactively manages potential exceptions before they become problems, simplifying the overall control system architecture.
3Productivity
If the robot uses a structured approach to evaluate environmental states, then the robot can prioritize and react to changes effectively, but requires a complex network of nodes and state evaluation processes
Solution Approach 1:
The robot performs periodic evaluation of environmental states at discrete time intervals rather than continuously monitoring all possible conditions. The structured approach evaluates aĉé set of predefined states in each cycle, allowing the robot to react quickly to changes while avoiding the complexity of continuous comprehensive monitoring. This periodic evaluation mechanism maintains responsiveness without requiring an overly complex node network.
Data Source
AI summary
In various examples, systems and methods are disclosed relating to decider networks for reactive decision-making, including for control of robotic systems. The decider networks can allow robotic systems to operate more collaboratively, such as by allowing the robotic systems to more frequently process and react to dynamic states of the environment and objects in the environment, such as to change decisions and/or paths of decision execution responsive to dynamic changes in logical states. The decider network can include a plurality of nodes having functions to process the logical states in sequence to determine actions for the robotic systems to perform.