Adaptive Vehicle Autonomy Architecture for Modular Task Planning
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Solution Overview
Problem
Existing autonomy systems for vehicles, such as UAVs and UASs, are limited in extensibility and adaptability, as they are typically designed to address only one aspect of autonomy and lack the ability to rapidly adapt to new platforms or domains, restricting their ability to support the addition of new modules and parameterization.
Innovation Solution
The Adaptable Autonomy Architecture (A3) system, which includes a processor, sensors, a situational awareness module, a task planning module, a task consensus module, and a task execution module, enables extensible autonomy by using a parameterized software framework that supports generic autonomy algorithms, allowing for rapid extension and reconfiguration across various vehicle types and domains, including aircraft, ground, sea, and surface vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing autonomy systems are designed to address only one aspect of autonomy activities, then the design can focus on a narrow mission set with simplified algorithms and software architecture, but the extensibility is limited and the system cannot support the addition of new modules
Solution Approach 1:
The autonomy system is divided into discrete, independently developable modules including situational awareness module, task planning module, task consensus module, and task execution module. Each module can be developed, tested, and updated independently while maintaining standardized interfaces, enabling the system to address multiple aspects of autonomy without increasing overall complexity.
Solution Approach 2:
The system employs a universal software architecture with standardized interfaces and communication protocols that allow the same framework to support multiple autonomy aspects and mission sets. This enables new modules to be integrated without redesigning the entire system, thereby improving extensibility while maintaining manageable complexity.
2Device complexity
If existing autonomy systems are not structured for rapid adaptation through parameterization, then the system structure remains simple and fixed, but the ability to rapidly adapt to new platforms is compromised
Solution Approach 1:
The system incorporates parameterized algorithms and configurable parameters that allow rapid adaptation to new platforms without structural redesign. By separating platform-specific parameters from core algorithms, the system can be quickly reconfigured for different vehicle types and mission requirements while maintaining a simple underlying structure.
Solution Approach 2:
The autonomy system employs dynamic configuration capabilities where modules and parameters can be adjusted in real-time based on operational requirements. This dynamic adaptability allows the system to respond to new platforms and mission sets without requiring complex predetermined structures for every possible scenario.
3Device complexity
If autonomy systems lack a standardized interface control document framework, then implementation is simpler for single-purpose systems, but integration with new systems and domains requires significant redevelopment
Solution Approach 1:
The system introduces standardized interface control documents as intermediary layers between different autonomy modules and external systems. These standardized interfaces act as mediators that enable integration with new systems and domains without requiring redevelopment of core functionality, thereby improving integration capability while maintaining simple implementation through clear interface definitions.
Data Source
AI summary
An autonomy system for use with a vehicle in an environment. The autonomy system comprising a processor operatively coupled with a memory device, a plurality of sensors operatively coupled with the processor; a vehicle controller, a situational awareness module, a task planning module, and a task execution module. The situational awareness module being configured to determine a state of the environment based at least in part on sensor data from at least one of the plurality of sensors. The task planning module being configured to identify, via the processor, a plurality of tasks to be performed by the vehicle and to generate a task assignment list from the plurality of tasks that is based at least in part on predetermined optimization criteria. The task execution module being configured to instruct the vehicle controller to execute the plurality of tasks in accordance with the task assignment list. The task execution module may be configured to monitor the vehicle or the vehicle controller during execution of the task assignment list to identify any errors.