Task-Specific Map Hierarchy for Autonomous Agent Retrieval Latency
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Solution Overview
Problem
Existing map representation systems for autonomous agents face challenges in efficiently storing and retrieving task-specific virtual representations of space with varying granularities, leading to compute, storage, and latency overheads.
Innovation Solution
A unified and scalable map representation system that provides hierarchical access to representations, ranging from highly granular surfaces to fine granular point clouds, enabling intelligent data storage and retrieval based on semantic requirements and task context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a unified map representation system stores all virtual representations with high granularity, then measurement precision is improved, but use of energy and device complexity increase
Solution Approach 1:
The map representation system segments virtual representations into multiple granularity levels (coarse and fine). The processor selectively retrieves only the required granularity level based on the autonomous agent's current task, avoiding the energy cost of storing and processing all high-granularity data uniformly.
Solution Approach 2:
The system dynamically adjusts the granularity of retrieved map data based on task requirements. For navigation tasks, coarse-granularity representations are sufficient and energy-efficient, while for manipulation tasks, fine-granularity representations are retrieved only when needed, optimizing energy consumption adaptively.
2Adaptability or versatility
If the system retrieves all virtual representations with varying granularities, then adaptability is improved, but loss of time increases due to retrieval overhead
Solution Approach 1:
The system pre-organizes virtual representations into a hierarchical structure with multiple granularity levels stored in an efficient data structure. This preliminary organization enables O(1) or O(log n) retrieval time for any granularity level, eliminating the need to process or filter through all representations during task execution.
Solution Approach 2:
Different regions of the map data structure are optimized for different retrieval patterns. Frequently accessed coarse-granularity navigation data is stored for rapid access, while fine-granularity manipulation data is stored efficiently but accessed only when locally required by specific tasks, minimizing overall retrieval latency.
3Adaptability or versatility
If the system stores multiple distinct virtual representations for different tasks, then adaptability is improved, but device complexity increases
Solution Approach 1:
A single unified data structure serves multiple functions by storing virtual representations at multiple granularity levels. The same data structure supports navigation tasks using coarse-granularity data, manipulation tasks using fine-granularity data, and intermediate tasks using appropriate intermediate granularity levels, eliminating the need for separate storage systems for each task type.
Solution Approach 2:
The data structure implements a nested hierarchy where coarse-granularity representations contain references to or are composed of finer-granularity representations. This nested organization allows the system to maintain a compact unified structure that can dynamically expand or contract the level of detail retrieved, reducing overall system complexity while maintaining adaptability.
Data Source
AI summary
Various aspects are related to a map representation system including one or more processors configured to: obtain operation task data, the operation task data indicating one or more operation tasks associated with an operation of one or more autonomous agents within a corresponding operating space; and obtain operation task specific map data as a function of the operation task data, the operation task specific map data representing the operating space corresponding to the one or more operation tasks.