Behavior-Based Mission Templates for Multi-Agent Autonomous Coordination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional robotic systems face challenges in implementing contingency actions and navigating complex dynamic environments, as they are limited by pre-planned incremental instructions and lack scalability, especially when interacting with multiple autonomous agents.
Innovation Solution
The development of a mission behavior model logic framework that generates synchronized task sequences for multiple task agents, allowing for behavior-based dynamic mission task sequence assembly, which includes synchronization points to coordinate the behaviors of individual agents and accounts for actions and interactions between them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional pre-planned incremental instructions are used to control autonomous machines, then the control system is simple and easy to implement, but the system lacks scalability and cannot effectively handle complex dynamic environments or implement contingency actions
Solution Approach 1:
The patent segments the mission into multiple hierarchical levels: mission-level goals, task-level components, and behavior-level actions. Each level operates with its own control logic, allowing the system to handle complexity at appropriate abstraction levels while maintaining overall simplicity. The behavior model library provides pre-segmented, reusable action templates that reduce the need for complex custom programming.
Solution Approach 2:
The control system transitions from static pre-planned instructions to dynamic behavior-based control. The system can adaptively select and combine behaviors from the behavior model library based on real-time environmental conditions, enabling contingency actions and dynamic response to changing situations while maintaining a structured framework.
2Productivity
If multiple autonomous agents are coordinated using traditional methods, then individual agent control is straightforward, but synchronization and coordination between multiple agents becomes difficult and inefficient
Solution Approach 1:
The patent merges individual agent behaviors into a coordinated multi-agent system through shared mission frameworks and synchronized task execution. Multiple agents operate under a unified mission model that defines their interrelationships, allowing them to work together efficiently on complex missions while maintaining individual autonomy and simplicity.
Solution Approach 2:
The behavior model library provides universal, reusable behavior templates that can be applied across multiple agents and mission types. This universality reduces coordination complexity by providing standardized interfaces and synchronized execution patterns that work consistently across different agents and mission scenarios.
3Reliability
If behavior-based mission templates are used to control multiple task agents, then the system can effectively complete complex missions and handle unexpected challenges, but the mission framework and behavior coordination become more complex
Solution Approach 1:
The system performs preliminary action by pre-defining behavior models and mission templates in the behavior model library. These pre-configured elements encapsulate complex coordination logic and contingency handling, allowing the system to reliably handle unexpected challenges during mission execution without requiring complex real-time decision-making or framework modifications.
Data Source
AI summary
In various examples, behavior-based mission task management for mobile autonomous machine systems and applications are provided. A mission controller may generate a mission behavior model logic framework for a mission that accounts for the actions of a set of multiple task agents that play a role in completing the mission. The mission controller may assemble a framework starting from a mission template that defines a baseline task sequence, correlate tasks defined by the baseline task sequence with pre-defined behavior models from a task library, and customize those behavior models based on mission task customization parameters. The mission controller may provide the mission behavior model logic framework to a mission dispatch function. Individual autonomous mobile task agents may then proceed to execute their assigned portions of local task sequences in accordance with the customized behavior models distributed to them by the mission dispatch function.