Robot Behavior Tree Control for Modular Mission Execution
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Solution Overview
Problem
Existing autonomy systems in robotics are limited in extensibility and adaptability, as they are typically designed to address only one narrow mission set, making it difficult to add new modules or adapt to new platforms quickly.
Innovation Solution
A computer-implemented method and apparatus that utilize a behavior tree architecture to model and execute missions for robots, allowing for the identification of tasks, determination of behavior trees, and execution of missions using a leaf node library, while also handling contingency events through alternate task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing autonomy systems are designed to address only one narrow mission set, then the system design can be simplified and focused, but the extensibility and adaptability to new missions or platforms are limited
Solution Approach 1:
The autonomy system is segmented into modular components including a behavior tree architecture with hierarchical nodes, a task library, and configurable parameters. This segmentation allows individual mission components to be independently developed, tested, and reused across different mission types, thereby improving extensibility without proportionally increasing overall system complexity.
Solution Approach 2:
The behavior tree architecture provides a universal framework that can accommodate multiple mission types and platforms through configurable nodes and tasks. The same core architecture supports diverse missions by swapping task libraries and adjusting parameters, enabling one system design to serve multiple functions across different applications and platforms.
2Device complexity
If existing autonomy systems lack modular structure, then the overall system architecture can be simpler, but the ability to rapidly adapt to new platforms through parameterization is reduced
Solution Approach 1:
The system employs dynamic parameterization where behavior tree nodes and tasks can be configured with platform-specific parameters without changing the core architecture. This dynamic configurability allows rapid adaptation to new platforms by simply adjusting parameters and loading appropriate task libraries, maintaining architectural simplicity while achieving platform versatility.
Solution Approach 2:
The autonomy system utilizes parameter changes as the primary mechanism for platform adaptation. By defining platform characteristics as configurable parameters within the behavior tree framework, the system can rapidly adapt to new platforms through parameter adjustment rather than structural redesign, preserving architectural simplicity while achieving broad platform compatibility.
3Adaptability or versatility
If new modules are added to existing autonomy systems, then the mission capabilities are enhanced, but the integration difficulty and system complexity increase
Solution Approach 1:
New mission capabilities are implemented as separate, self-contained task modules that can be independently developed and validated. These modular tasks interface with the core behavior tree architecture through standardized connections, allowing capability enhancement without requiring integration of entire subsystems, thereby reducing integration difficulty while expanding mission capabilities.
Solution Approach 2:
The behavior tree architecture serves as an intermediary layer between new mission modules and the core autonomy system. This intermediary framework provides standardized interfaces and coordination mechanisms, allowing new modules to be integrated through the established behavior tree structure rather than requiring direct integration with core systems, thus reducing integration complexity.
4Loss of time
If the autonomy system uses a narrow mission set focus, then the initial development time and resources are reduced, but the long-term productivity for diverse missions is limited
Solution Approach 1:
The system performs preliminary action by establishing a flexible behavior tree architecture and task library framework during initial development, rather than hardcoding specific mission logic. This upfront investment in modular infrastructure enables rapid development of new missions through configuration and task assembly, reducing long-term development time while maintaining low initial development overhead through reusable components.
Data Source
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AI summary
A method is provided for causing one or more robots to execute a mission. The method includes determining a behavior tree in which the mission is modeled, and causing the one or more robots to execute the mission using the behavior tree and a leaf node library. The behavior tree is expressed as a directed tree of nodes including a switch node, a trigger node representing a selected task, and action nodes representing others of the tasks. The switch node is connected to the trigger node and the action nodes in a parent-child relationship in which the trigger node and the action nodes are children of the switch node. The trigger node is a first of the children that, when ticked by the switch node, returns an identifier of one of the action nodes to trigger the switch node to next tick the one of the action nodes.