UAV Multi-Sensor Mapping for Accurate Navigation and Obstacle Detection
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Solution Overview
Problem
Existing approaches for obtaining environmental data by unmanned aerial vehicles (UAVs) are often less than optimal, leading to inaccurate data that can negatively impact UAV functionality, particularly in diverse environments and operating conditions.
Innovation Solution
The use of a plurality of sensors, including GPS, vision, and proximity sensors like lidar and ultrasonic sensors, to collect and combine data for generating detailed environmental maps, facilitating navigation and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single sensor type is used to collect environmental data, then the device complexity is reduced, but the measurement precision and reliability of environmental data deteriorate
Solution Approach 1:
The patent combines multiple sensor types (GPS receiver, vision sensor, and proximity sensor) into a unified sensor system. The GPS receiver provides location data, the vision sensor captures visual information, and the proximity sensor detects distance to obstacles. By merging these different sensor types, the system achieves comprehensive environmental perception with high measurement precision while maintaining manageable device complexity through integrated processing.
2Reliability
If multiple sensor types are used to collect environmental data, then the measurement precision and reliability improve, but the device complexity increases
Solution Approach 1:
The patent merges multiple sensor types (GPS, vision, proximity) into a single integrated environmental data collection system. This combination improves reliability by cross-validating data from different sensors and providing redundant measurement capabilities, while the unified processing architecture keeps device complexity manageable.
Solution Approach 2:
The sensor system is designed with multi-functionality, where each sensor type serves multiple purposes. For example, the vision sensor not only captures visual data for environmental mapping but also assists in obstacle detection and navigation. This universal approach allows the system to achieve high reliability across various functions without proportionally increasing complexity.
3Measurement precision
If environmental data is collected with high accuracy requirements, then the navigation and obstacle avoidance performance improve, but the loss of time for data processing increases
Solution Approach 1:
The patent implements partial processing by prioritizing critical environmental data for immediate navigation decisions. The system processes sensor data selectively, focusing on obstacles and environmental features that directly impact safety and navigation, rather than processing all collected data with equal depth. This approach maintains high measurement precision for critical parameters while reducing overall processing time.
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
This approach enhances the accuracy and robustness of environmental mapping, improving UAV functionality by providing precise location information and obstacle detection, thereby enhancing navigation and obstacle avoidance capabilities in various environments.
Implementation Method 1
determining, using at least one of a plurality of sensors carried by the movable object, an initial location of the movable object
Implementation Method 2
The proximity sensor can comprise at least one of the following: a lidar sensor
Implementation Method 3
The proximity sensor can comprise at least one of the following: an ultrasonic sensor
Implementation Method 4
The proximity sensor can comprise at least one of the following: a time-of-flight camera sensor
Data Source
AI summary
A method for controlling a UAV in an environment includes receiving first and second sensing signals from a vision sensor and a proximity sensor, respectively, coupled to the UAV. The first and second sensing signals include first and second depth information of the environment, respectively. The method further includes selecting the first and second sensing signals for generating first and second portions of an environmental map, respectively, based on a suitable criterion associated with distinct characteristics of various portions of the environment or distinct capabilities of the vision sensor and the proximity sensor, generating first and second sets of depth images for the first and second portions of the environmental map, respectively, based on the first and second sensing signals, respectively, combining the first and second sets of depth images to generate the environmental map; and effecting the UAV to navigate in the environment using the environmental map.