Mobile Robot Route Localization Using Multi-Sensor Node Integration
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Solution Overview
Problem
Existing technologies face challenges in maintaining accurate position and posture estimation of mobile robots during autonomous travel, particularly when GNSS positioning accuracy is reduced, leading to map distortion and subsequent inaccuracies.
Innovation Solution
A mobile apparatus that performs teaching travel to store positions and postures manually, generates calculation information for deviations using multiple external sensors, and integrates these deviations to ensure accurate autonomous travel even in environments with reduced GNSS accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If an environment map is generated using SLAM technology with distance measurement data, then autonomous travel capability is enabled, but map distortion occurs when GNSS positioning accuracy is reduced, leading to decreased position and posture estimation accuracy
Solution Approach 1:
The patent combines multiple external sensors (GNSS, LiDAR, cameras) to generate multiple environment maps simultaneously. By merging the information from these different sensors, the system creates a more robust representation of the environment that is less susceptible to distortion from any single sensor's inaccuracies, particularly when GNSS positioning accuracy is reduced.
Solution Approach 2:
The system performs map matching between the generated environment maps and distance data at each time point during autonomous travel. This continuous feedback mechanism allows the mobile robot to detect and correct deviations from the travel route, maintaining position and posture estimation accuracy even when the map contains some distortion.
2Measurement precision
If multiple external sensors are used to generate environment maps, then position estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent employs multiple external sensors that serve dual purposes: they generate environment maps for autonomous navigation and simultaneously provide data for map matching and position estimation. This multi-functionality allows the system to improve position estimation accuracy without proportionally increasing overall system complexity, as the same hardware resources are utilized for multiple critical functions.
3Measurement precision
If map matching is performed continuously during autonomous travel, then position estimation accuracy is maintained, but processing time and computational load increase
Solution Approach 1:
The patent performs map matching for each node on the travel route independently for each external sensor. This segmentation of the matching process into discrete, manageable units allows the system to process information efficiently at each location without the computational burden of continuous full-map matching, thereby maintaining accuracy while reducing processing time.
Data Source
Figure 1A~1B
Figure 2A~2B
Figure 3
AI summary
A mobile apparatus performs teaching travel and autonomous travel on a travel route. In the teaching, the mobile apparatus stores in a storage unit a position and posture of the mobile apparatus traveling on the travel route under control by a manual operation. In, the autonomous travel, the mobile apparatus autonomously travels on the travel route. The mobile apparatus includes an information generation unit (46) to generate, for each of nodes on the travel route independently for each of multiple external sensors, calculation information used for calculating a deviation between a node passed in the teaching travel and a point passed in the autonomous travel, and store in the storage unit the calculation information in association with the node and the external sensor. The mobile apparatus further includes an autonomous travel unit (43) including a deviation calculation unit (43b) to calculate, for each node independently for each of the multiple external sensors, the deviation based on the calculation information and a sensor value of the external sensor obtained in the autonomous travel; a position estimation unit (43c) to determine, for each node independently for each of the multiple external sensors, the calculated deviation as a position and posture of the node on the travel route with reference to the position and posture of the mobile apparatus; and an integration unit (43d) to integrate, for each node, the positions and postures of the node determined independently for each of the multiple external sensors. The autonomous travel unit (43) controls the mobile apparatus to autonomously travel on the travel route based on the integrated position and posture of each node on the travel route integrated by the integration unit (43d).