Autonomous traveler and travel control method thereof
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Solution Overview
Problem
Autonomous vacuum cleaners face inefficiencies and inaccuracies in navigation due to incomplete mapping of room layouts and dynamic changes in obstacles, leading to potential getting stuck or deviating from optimal routes.
Innovation Solution
An autonomous traveler equipped with a self-position estimator, obstacle detector, map generator, and controller that dynamically adjusts travel routes based on real-time obstacle detection and self-positioning data, allowing for continuous map updates and optimized route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a map is generated based on basic obstacle detection without considering interior or material information, then the mapping process is simple and fast, but the autonomous traveler may get stuck or fail to travel along expected routes due to insufficient environmental understanding
Solution Approach 1:
The system performs preliminary detailed mapping during initial exploration phases, collecting comprehensive information about furniture interiors, materials, and obstacle characteristics before actual cleaning operations begin. This preliminary action ensures that the map contains sufficient detail to prevent getting stuck during subsequent traveling operations.
Solution Approach 2:
The system continuously monitors traveling operations and detects when obstacles are encountered that were not properly characterized in the original map. This feedback triggers map updates to incorporate interior and material information, improving future traveling reliability without significantly impacting overall productivity.
2Adaptability or versatility
If the map is updated whenever new obstacles are detected during traveling, then the system adapts to changing environments, but the traveling route for next time may deviate from the actually optimum route when temporary obstacles like shopping bags or pets are detected
Solution Approach 1:
The system dynamically adjusts map update behavior based on obstacle characteristics. For permanent obstacles like furniture, the map is updated to reflect their positions. For temporary obstacles like shopping bags or pets, the system uses detection frequency analysis to determine whether to update the map, allowing flexible adaptation to changing environments while maintaining traveling efficiency.
Solution Approach 2:
The system changes the parameter of map update frequency based on obstacle detection patterns. When an obstacle is detected multiple times at the same location, the system increases the likelihood of updating the map for that location. When obstacles are detected only once or sporadically, the system maintains the original route, assuming they are temporary objects.
3Reliability
If the autonomous traveler repeats obstacle avoidance operations to ensure safe traveling, then traveling safety is improved, but the time required for cleaning operations increases due to repeated avoidance maneuvers
Solution Approach 1:
The system performs preliminary characterization of obstacles during mapping, recording their interiors, materials, and spatial relationships. This preliminary action enables the autonomous traveler to plan optimal avoidance routes in advance, reducing the need for repeated obstacle avoidance maneuvers during actual cleaning operations and thereby minimizing time loss.
Data Source
AI summary
A vacuum cleaner that can achieve efficient and accurate autonomous traveling. An obstacle detection part detects an object corresponding to an obstacle outside a main casing. A map generation part generates a map indicating information on an area having been traveled by the main casing, based on detection of the object by the obstacle detection part and a self-position estimated by a self-position estimation part during traveling of the main casing. A controller controls an operation of a driving wheel to make the main casing autonomously travel. The controller includes a traveling mode for controlling the operation of the driving wheel so as to make the main casing autonomously travel along a traveling route set based on the map. The controller determines whether or not to change the traveling route for next time based on the obstacle detected by the obstacle detection part during the traveling mode.