Self-Mobile Device 3D Obstacle Detection for Adaptive Travel Control
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Solution Overview
Problem
Existing floor sweeping robots face challenges in accurately judging obstacles due to limitations in their obstacle avoidance systems, such as incomplete mapping and inability to detect vertical directions, leading to potential collisions with narrow gaps or inaccurate navigation.
Innovation Solution
A self-mobile device equipped with an area array solid-state laser radar collects three-dimensional environment information, identifies obstacle types, and performs targeted travel control to improve obstacle avoidance by determining whether to climb over, step down, or traverse obstacles based on their characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional distance sensors (infrared, laser, ultrasonic) are used for obstacle detection, then the device can detect obstacles at a distance, but the measurement precision and accuracy of obstacle judgment deteriorates due to incomplete mapping and inability to detect vertical directions
Solution Approach 1:
The patent transitions from traditional 2D plane mapping to 3D spatial mapping by introducing vertical direction detection capability. The distance sensor is configured to detect not only horizontal obstacles but also vertical structures like door frames and steps, adding a new dimension to the mapping data. This enables the robot to understand the three-dimensional environment and make more accurate obstacle judgments.
2Reliability
If the robot returns or bypasses upon detecting an obstacle, then obstacle avoidance is achieved, but the productivity and cleaning efficiency deteriorates due to unnecessary actions in passable areas
Solution Approach 1:
The patent applies different judgment criteria and avoidance strategies for different types of obstacles based on their spatial characteristics. For detectable vertical obstacles like door frames, the robot performs localized avoidance maneuvers. For passable areas like narrow gaps under furniture, the robot continues navigation without unnecessary returns. This localized, differentiated response optimizes both obstacle avoidance reliability and cleaning productivity.
Solution Approach 2:
The system changes the decision-making parameters for obstacle avoidance based on the type and characteristics of the detected obstacle. By analyzing the three-dimensional features (height, width, position) of obstacles, the robot adjusts its behavior parameters - sometimes returning, sometimes bypassing, sometimes continuing straight - to optimize the balance between safe avoidance and efficient cleaning.
3Productivity
If the robot continues straight without adequate detection, then productivity is maintained, but the reliability of obstacle avoidance deteriorates leading to potential collisions
Solution Approach 1:
The patent implements preliminary detection and judgment actions before the robot reaches potential obstacle areas. The distance sensor continuously scans the environment ahead, and the processor pre-judges whether upcoming areas are passable (like narrow gaps) or require avoidance (like door frames). This preliminary action allows the robot to maintain efficient navigation while ensuring reliable collision avoidance through advance awareness.
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
Enhances the accuracy and effectiveness of obstacle avoidance, reducing the likelihood of collisions and improving navigation by enabling the device to adapt its path according to the specific nature of obstacles encountered.
Implementation Method 1
The area array solid-state laser radar is configured to collect three-dimensional environment information on a travel path of the self-mobile device
Data Source
AI summary
The embodiments of the present disclosure provide a method of travel control, a device and a storage medium. In some exemplary embodiments of the present disclosure, a self-mobile device collects three-dimensional environment information on a travel path of itself in the travel process, identifies an obstacle area and a type thereof existing on the travel path of the self-mobile device based on the three-dimensional environment information, and the self-mobile device adopts different travel controls in a targeted manner for different types of the area, such that the obstacle avoidance performance of the self-mobile device is improved by adopting the method of the travel control in the present disclosure.