Autonomous Agent Resource Allocation for Collaborative Sensing
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Solution Overview
Problem
Autonomous machines, such as drones and vehicles, face limitations in navigation and obstacle detection due to limited on-board storage, sensors, power, and computing capabilities, especially in environments with poor lighting or uncharted territories, where network connectivity may not provide sufficient real-time data.
Innovation Solution
The implementation of a collaborative network using blockchain technology and dynamic resource allocation, where autonomous agents can share sensory data and computing resources with each other, allowing for improved perception and decision-making by leveraging heterogeneous agents and offloading processing tasks to more capable agents when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous machines use only on-board sensors and storage, then device complexity is reduced, but measurement precision and information availability deteriorate in challenging environments
Solution Approach 1:
The patent merges the sensing capabilities of multiple autonomous machines into a collaborative network. Individual machines share their sensor data (LIDAR, cameras, radar) through blockchain-based communication, creating a virtual expansion of each machine's perceptual range and precision without adding physical sensors to each unit.
Solution Approach 2:
The blockchain network serves multiple functions simultaneously: it acts as a communication medium for data sharing, a distributed ledger for verifying data authenticity, and a coordination mechanism for resource allocation. This universal platform enables machines to perform both individual navigation and collaborative sensing without requiring separate specialized systems.
2Reliability
If autonomous machines operate independently without collaboration, then device complexity is minimized, but reliability and adaptability deteriorate in uncharted territories
Solution Approach 1:
The autonomous machines self-organize into a collaborative network using blockchain smart contracts that automatically execute data sharing agreements. Each machine independently contributes its sensor data and receives needed information without requiring centralized coordination, making the system self-sufficient while improving reliability.
Solution Approach 2:
The blockchain network acts as a trustless intermediary that enables reliable data exchange between machines. It verifies data authenticity through cryptographic signatures and ensures consistent state across all participants, allowing machines to collaborate reliably without direct peer-to-peer trust relationships.
3Measurement precision
If autonomous machines allocate maximum resources to sensing and processing, then measurement precision improves, but use of energy increases beyond available power
Solution Approach 1:
Instead of every machine continuously running maximum-power sensing and processing, the system uses partial action by having each machine perform only the sensing needed for its immediate navigation while relying on others' data for broader environmental awareness. This reduces individual power consumption while maintaining collective perception accuracy.
Solution Approach 2:
The computational processing load is merged across the network. Individual machines with limited processing power can offload complex environmental modeling and obstacle prediction tasks to other machines with available computational resources, reducing each machine's energy expenditure while maintaining high measurement precision.
4Adaptability or versatility
If autonomous machines use fixed resource allocation, then device complexity is reduced, but adaptability deteriorates when operating conditions change
Solution Approach 1:
The resource allocation system dynamically adjusts each machine's sensing and processing responsibilities based on real-time environmental conditions, machine capabilities, and network state. When conditions deteriorate (e.g., poor lighting, obstacles), the system automatically reallocates resources to enhance relevant sensing modalities without requiring manual reconfiguration.
Solution Approach 2:
The system changes operational parameters such as sensor sampling rates, processing intensity, and data sharing frequency based on environmental conditions. In challenging environments, machines increase their sensing and sharing activity; in benign environments, they reduce consumption, allowing adaptability without fixed complex resource management hardware.
Data Source
AI summary
Methods, systems, articles of manufacture and apparatus are disclosed to improve autonomous machine capabilities. An example disclosed apparatus includes an agent task manager to retrieve native sensor input data from a sensor of the agent, an agent characteristics engine to identify environmental characteristics based on the retrieved native sensor input data, and a resource allocation modifier to allocate a first quantity of resources of the agent based on a likelihood score associated with the environmental characteristics.