Adaptive Resource Reallocation for Agricultural Robots
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Solution Overview
Problem
Resource-constrained devices used in agricultural fields, such as modular computing devices and robots, face limitations in battery power, memory, and processing power, making it challenging to perform computationally expensive tasks over large areas without frequent recharging or servicing.
Innovation Solution
Implementing a method to adaptively reallocate computing resources between tasks using a policy model that monitors resource usage and phenotypic output, allowing devices to dynamically adjust processor cycles, memory, and bandwidth to prioritize tasks based on environmental conditions and goals, ensuring extended operation without recharging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are allocated to perform computationally expensive tasks in agricultural fields, then task completion capability is improved, but battery power consumption increases
Solution Approach 1:
The patent implements dynamic resource allocation where the computing device continuously monitors resource usage and phenotypic output to adjust processor cycles, memory, and bandwidth allocation in real-time. The policy model enables the system to adaptively shift resources between tasks based on current operational conditions, allowing the device to maintain task completion capability while optimizing energy consumption patterns throughout the agricultural operation.
Solution Approach 2:
The system changes operational parameters by adjusting processor cycle allocation, memory allocation, and bandwidth allocation based on monitored resource usage and phenotypic output. The policy model processes state information to generate probability distributions over candidate reallocation actions, enabling parameter optimization that balances computational task performance with energy consumption constraints.
2Duration of action of moving object
If computing resources are increased to cover larger agricultural areas, then operational duration is improved, but resource constraints are worsened
Solution Approach 1:
The patent employs dynamic resource reallocation that adjusts computing resource distribution based on operational phase and phenotypic output requirements. By continuously monitoring resource usage and using the policy model to determine optimal allocation, the system extends operational duration through adaptive resource management rather than through static resource increases, thereby operating within existing resource constraints.
Solution Approach 2:
The computing device performs self-service through autonomous resource allocation decisions. The policy model enables the device to automatically monitor its own resource usage, assess phenotypic output, and reallocate resources without external intervention, allowing the system to optimize its operational duration within its inherent resource constraints.
3Productivity
If computing resources are reallocated dynamically between tasks, then resource utilization efficiency is improved, but system complexity is worsened
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors computing resource usage and phenotypic output, processes this information through the policy model, and uses the resulting probability distributions to guide resource reallocation decisions. This closed-loop feedback system enables efficient resource utilization while managing complexity through structured decision-making processes.
Solution Approach 2:
The policy model serves as an intermediary between resource monitoring and resource allocation decisions. It processes monitored state information and generates probability distributions over candidate reallocation actions, mediating the complex relationship between resource usage patterns and allocation strategies, thereby simplifying the overall system architecture.
Data Source
AI summary
Implementations are disclosed for adaptively reallocating computing resources of resource-constrained devices between tasks performed in situ by those resource-constrained devices. In various implementations, while the resource-constrained device is transported through an agricultural area, computing resource usage of the resource-constrained device ma may be monitored. Additionally, phenotypic output generated by one or more phenotypic tasks performed onboard the resource-constrained device may be monitored. Based on the monitored computing resource usage and the monitored phenotypic output, a state may be generated and processed based on a policy model to generate a probability distribution over a plurality of candidate reallocation actions. Based on the probability distribution, candidate reallocation action(s) may be selected and performed to reallocate at least some computing resources between a first phenotypic task of the one or more phenotypic tasks and a different task while the resource-constrained device is transported through the agricultural area.