Topometric Map Navigation for Inventory Drones With Low Compute Load
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Solution Overview
Problem
Generating and updating a detailed map of an environment for autonomous navigation in unmanned aerial vehicles (UAVs) is computationally expensive and memory-intensive, making it difficult for devices with limited resources to incorporate complex maps effectively.
Innovation Solution
A topometric map is generated using vertices and edges representing locations and paths in a storage site, allowing an inventory robot to navigate efficiently by updating the map in real-time, determining actions based on the generated path, and using a perception engine to enhance navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detailed map is generated and updated for autonomous navigation, then navigation accuracy is improved, but computational cost and memory requirements increase
Solution Approach 1:
The environment map is segmented into a graph structure consisting of vertices (key locations) and edges (paths between locations). This segmentation allows the system to represent complex environments using only essential navigational elements rather than detailed continuous maps, reducing computational complexity while maintaining navigation accuracy.
Solution Approach 2:
The patent extracts only the essential navigational information from the environment - specifically vertices representing key locations (pallet positions, intersections) and edges representing traversable paths. Non-essential detailed information is discarded, achieving efficient representation with reduced memory and computational requirements while preserving navigation functionality.
2Measurement precision
If a detailed map is generated and updated for autonomous navigation, then navigation accuracy is improved, but memory requirements increase
Solution Approach 1:
The map is segmented into discrete vertices and edges, where vertices represent key locations (pallet positions, intersections) and edges represent paths. This segmentation compresses the environment representation to only essential navigational points and connections, dramatically reducing memory requirements compared to storing detailed continuous maps while preserving navigation accuracy.
Solution Approach 2:
The system extracts and stores only the minimal necessary information for navigation - vertex coordinates, edge connections, and associated metrics. All other detailed environmental information is excluded, achieving efficient memory utilization while maintaining sufficient navigation precision.
3Adaptability or versatility
If real-time map updating is performed during navigation, then adaptability to environment changes is improved, but computational load increases
Solution Approach 1:
The system implements real-time feedback through the perception engine that continuously monitors the environment during navigation. When changes are detected (new obstacles, modified paths), the graph structure is updated by adding, removing, or modifying vertices and edges. This feedback mechanism enables adaptability to environment changes while maintaining computational efficiency through incremental updates rather than complete remapping.
Solution Approach 2:
The map representation is designed to be dynamic, allowing vertices and edges to be added or removed based on real-time environmental changes. The graph structure can adapt its topology as the inventory robot navigates, enabling the system to respond to dynamic warehouse environments with modified layouts or obstacles while keeping computational load manageable through localized updates.
Data Source
AI summary
A topometric map that enables autonomous navigation of an inventory robot. The topometric map is generated using the layout of a storage site and is made up of vertices and edges. The vertices are generated at pallet locations and other structural locations, and edges are generated between neighboring vertices. Vertices and edges have associated metrics that aid in the routing of the robot. The metrics of the vertices and edges may be updated as the robot navigates through the storage site by using a perception engine and state estimator. The metrics of the edges can be used to calculate an energy cost. The robot determines a shortest path between a source and destination vertex based on the energy cost associated with each edge.