Autonomous Mobile Device Navigation Using Composite Maps
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Solution Overview
Problem
Conventional autonomous mobile devices cannot effectively avoid zones that should be excluded from entry, even if no obstacles exist, as they rely solely on obstacle detection and lack the capability to identify no-entry zones without obstacles.
Innovation Solution
An autonomous mobile device equipped with an obstacle sensor, storage for environment and no-entry zone maps, an estimation device for self-location, and a controller that uses these maps to plan paths and avoid both obstacle and no-entry zones, including the use of a synthesizing device to generate a composite map and a calculation device to provide avoidance information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous mobile device uses only obstacle sensor and environment map for navigation, then the device can avoid detected obstacles, but it cannot avoid no-entry zones where no obstacles exist (such as treatment rooms or stepped zones)
Solution Approach 1:
The patent segments the navigation problem by separating obstacle detection (environment map) from no-entry zone identification (no-entry zone map). The no-entry zone map is divided into multiple zones with different restriction levels, allowing the system to handle different types of restricted areas independently. This segmentation enables the device to avoid no-entry zones without requiring complex integration of multiple sensing systems.
Solution Approach 2:
The patent introduces a map synthesis unit as an intermediary that combines the environment map and no-entry zone map into a unified navigation framework. This intermediary component processes both maps and generates composite navigation information, allowing the autonomous device to simultaneously consider obstacles and no-entry zones without directly complexifying the core navigation algorithm.
2Measurement precision
If the autonomous mobile device estimates self-location using only obstacle information from the obstacle sensor, then the estimation can be simple and fast, but it cannot accurately determine position when no obstacles are present or when obstacles are ambiguous
Solution Approach 1:
The patent merges obstacle information from the environment map with no-entry zone information from the no-entry zone map to enhance self-location estimation. By combining these two information sources, the system achieves more accurate position determination, especially in areas where obstacles alone provide insufficient reference points. The merging occurs in the map synthesis unit that creates a unified spatial understanding.
Solution Approach 2:
The patent performs preliminary action by pre-mapping no-entry zones and storing them in the no-entry zone map before the autonomous device needs to navigate. This advance preparation of no-entry zone information allows the device to use this data during self-location estimation without real-time processing delays, improving both accuracy and speed of position determination.
3Adaptability or versatility
If the autonomous mobile device treats all zones uniformly based on obstacle detection, then the control algorithm remains simple, but it cannot differentiate between zones that should be avoided and zones that are safe to enter
Solution Approach 1:
The patent applies local quality by assigning different properties to different zones through the no-entry zone map. Each zone is tagged with specific attributes (e.g., treatment room, stepped zone, corridor) that define its characteristics and navigation rules. This allows the control system to adapt its behavior locally based on zone type without requiring a completely complex control architecture, as the zone-specific information is embedded in the map data.
Solution Approach 2:
The patent creates a universal navigation framework that handles multiple zone types through a single integrated approach. The map synthesis unit and control unit are designed to process both obstacle information and no-entry zone information using the same basic algorithms, making the system multi-functional. This universality allows the device to navigate various environments (hospitals, offices, public buildings) without requiring specialized control algorithms for each zone type.
Data Source
AI summary
An autonomous mobile device that moves while autonomously avoiding zones into which entry should be avoided even if no obstacle exists therein includes a laser range finder that acquires peripheral obstacle information, a storage unit that stores an environment map that shows an obstacle zone where an obstacle exists, and a no-entry zone map which shows a no-entry zone into which entry is prohibited, a self-location estimation unit that estimates the self-location of a host device by using the obstacle information acquired by the laser range finder and the environment map, and a travel control unit that controls the host device to autonomously travel to the destination by avoiding the obstacle zone and the no-entry zone based on the estimated self-location, the environment map, and the no-entry zone map.