Autonomous Mobile Robot Localization With Multi-Level Obstacle Sensing
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Solution Overview
Problem
Existing autonomous guided vehicles face challenges in accurately navigating and transporting objects in dynamic environments with varying obstacles and changing ambient conditions, such as distribution centers and shop backyards, where movable objects and humans are present, due to limitations in sensor positioning and data accuracy.
Innovation Solution
An autonomous mobile robot equipped with a first detector positioned higher than surrounding objects, a second detector for scanning in front, and a third detector for wheel position data, along with a control system that generates routes avoiding obstacles and calculates precise positions using SLAM, enables the robot to autonomously move and transport objects while adapting to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single detector is used for localization and navigation, then the device complexity is reduced, but the measurement precision and reliability of position detection deteriorate in dynamic environments with moving obstacles
Solution Approach 1:
The detection system is segmented into multiple specialized detectors: a first detector for obtaining scanning data of the environment, a second detector for obtaining image data, and a third detector for detecting wheel position. Each detector is positioned at different heights and orientations to capture different aspects of the environment, enabling accurate localization and obstacle detection in dynamic settings.
Solution Approach 2:
The first detector is positioned at a height higher than surrounding objects, adding a vertical dimension to the detection capability. This elevated position allows the detector to scan over moving obstacles and obtain a broader view of the environment, improving measurement precision without significantly increasing device complexity.
2Measurement precision
If the first detector is positioned at a higher location to scan over obstacles, then the measurement precision of environment scanning is improved, but the device complexity and difficulty of installation increase
Solution Approach 1:
The system uses multiple detectors positioned at different heights (first detector elevated, second detector at robot level) to create a complementary detection network. This multi-level positioning allows each detector to operate in its optimal range, with the elevated first detector scanning over obstacles while the second detector captures detailed images, thereby improving environment scanning accuracy without excessive positioning difficulty.
3Reliability
If multiple detectors are used to improve localization and navigation accuracy, then the reliability of autonomous operation is improved, but the device complexity and cost increase
Solution Approach 1:
The detection system is designed with multi-functionality: the first detector serves both for localization (by scanning environment features) and obstacle detection; the second detector provides both navigation data and verification of the first detector's scanning results; the third detector integrates wheel position data with the other sensors. This universal approach improves autonomous navigation reliability while managing device complexity through shared processing logic.
Solution Approach 2:
The system implements feedback mechanisms where the localization estimation unit continuously compares detected features with the environment map, and the route generating unit adjusts paths based on real-time obstacle detection. The control unit receives feedback from all detectors and dynamically adjusts navigation decisions, thereby improving reliability through continuous verification and correction.
4Productivity
If SLAM and route generation are performed in real-time with multiple sensors, then the productivity of autonomous navigation is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system performs preliminary actions by pre-generating multiple candidate routes before execution and pre-processing environment maps during periods of low activity. The route generating unit creates a set of possible paths in advance, and the control unit selects the optimal route based on current conditions, thereby improving navigation productivity while reducing real-time computational energy consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution allows for accurate localization, route generation, and obstacle avoidance, enabling efficient and safe transportation of objects in dynamic environments, even with varying ambient conditions and moving obstacles, by utilizing multiple detectors and a sophisticated control system.
Implementation Method 1
a first detector (10) provided at a position higher than a position in height of an object present around the autonomous mobile robot, the first detector being configured to obtain first data by scanning the object at a first region around the autonomous mobile robot
Implementation Method 2
a second detector (11) configured to obtain second data by scanning the object at a second region around the autonomous mobile robot
Data Source
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AI summary
According to one embodiment, an autonomous mobile robot has a driver, a first detector, a second detector, a localization estimation part, a route-generating part, and a control part. The localization estimation part is configured to calculate an estimated position of the autonomous mobile robot in a predetermined region in accordance with first data. The route-generating part is configured to calculate a position of an object present around the autonomous mobile robot in accordance with second data. The route-generating part is configured to calculate a route to a target position in accordance with the estimated position and the position of the object. The control part is configured to control the driver in accordance with the route. The control part is configured to cause the autonomous mobile robot to travel to the target position.