Neural Implicit Function Path Planning for Dynamic Environments
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Solution Overview
Problem
Existing methods for calculating paths through dynamic environments are inefficient and require significant computing resources, especially when environments and goals change frequently, as they struggle to balance geometric and semantic feasibility.
Innovation Solution
A neural implicit function is used to represent environments through an environment field, allowing for the calculation of reaching distances from any position to a goal position, enabling efficient trajectory prediction and navigation while ensuring geometric and semantic plausibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional path planning methods are used to calculate paths through dynamic environments, then path feasibility can be ensured, but computing resources required increase significantly and efficiency decreases
Solution Approach 1:
The system pre-calculates and stores reaching distance maps for multiple possible goal positions before actual navigation is needed. When the goal changes, the pre-computed maps are readily available for quick path planning without requiring full recalculation, thus maintaining path feasibility while significantly reducing real-time computing requirements
Solution Approach 2:
The reaching distance maps are computed and stored locally for different regions and goal positions. Instead of calculating entire path plans globally, the system uses local distance information from pre-computed maps to quickly determine navigation paths, reducing the computational burden while ensuring local path feasibility
2Measurement precision
If traditional path planning methods recalculate paths when environments or goals change, then path accuracy can be maintained, but time required for recalculation increases
Solution Approach 1:
The system pre-computes reaching distance maps for multiple potential goal positions and stores them for future use. When the goal position changes, the system retrieves pre-computed maps rather than recalculating from scratch, maintaining path accuracy through pre-validated distance information while dramatically reducing recalculation time
Solution Approach 2:
The pre-computed reaching distance maps serve multiple purposes: they can be used for different goal positions within the same environment and for various path planning scenarios. This multi-functionality allows the system to maintain accurate paths for different goals without repeated full recalculations, saving time while preserving precision
Data Source
AI summary
Apparatuses, systems, and techniques to calculate a plurality of paths, through which an autonomous device is to traverse. In at least one embodiment, a plurality of paths are calculated using one or more neural networks based, at least in part, on one or more distance values output by the one or more neural networks.