Adaptive Vehicle Autonomy Architecture for Dynamic Task Reallocation
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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 rapid 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, allows for the generation of task assignment lists based on optimization criteria and enables dynamic task reallocation and reordering across multiple vehicles, facilitating collaborative objectives and rapid adaptation to different vehicle types and domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
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 specialized algorithms, but the system lacks extensibility and cannot support addition of new modules
Solution Approach 1:
The patent implements a universal autonomy system architecture that can perform multiple functions across different mission types. The system uses a common software framework with parameterized algorithms that can be configured for various autonomy aspects including flight control, path planning, and task management, allowing the same hardware platform to support diverse missions without requiring dedicated specialized systems for each function
Solution Approach 2:
The autonomy system is divided into modular functional components including situational awareness module, task planning module, task execution module, and vehicle controller. Each module can be independently developed, tested, and configured, allowing new modules to be added to the system without redesigning the entire architecture. The modular design enables flexible composition of autonomy capabilities for different mission requirements
2Device complexity
If existing autonomy systems are not structured for rapid adaptation, then the system architecture remains simple, but the system cannot rapidly adapt to new platforms through parameterization
Solution Approach 1:
The system employs parameterized algorithms where mission-specific behavior is achieved by changing parameters rather than rewriting code. The software framework accepts configuration parameters that define mission objectives, constraints, and performance criteria, allowing the same algorithmic structure to adapt to different platforms and missions by simply adjusting parameters without increasing architectural complexity
Solution Approach 2:
The autonomy system implements dynamic reconfiguration capabilities where the task planning module can generate and update task assignment lists in real-time based on changing mission requirements. The system dynamically adjusts task priorities, allocates resources, and reorders task execution sequences during operation, enabling rapid adaptation to new platforms and missions while maintaining a stable core architecture
3Ease of manufacture
If the system uses a fixed task assignment approach, then the implementation is straightforward, but the system cannot perform dynamic task reallocation and optimization across multiple vehicles
Solution Approach 1:
The task execution module continuously monitors vehicle performance and task completion status, feeding this information back to the task planning module. This feedback loop enables the system to identify errors, assess mission progress, and dynamically adjust task assignments to optimize collaborative efficiency. The feedback mechanism allows the system to maintain implementation simplicity while achieving high productivity through automated task reallocation based on real-time performance data
Solution Approach 2:
The task planning module pre-generates task assignment lists based on optimization criteria before mission execution. This preliminary task allocation provides a structured starting point that simplifies implementation, while the system retains the capability to dynamically reorder and reallocate tasks during execution based on changing conditions, thus combining the benefits of pre-planned simplicity with adaptive optimization
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.