UAV Multi-Sensor Mapping for Accurate Obstacle Occupancy Grids
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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, vision sensors, proximity sensors including LIDAR and ultrasonic sensors) into an integrated sensor system. This merging of different sensing modalities allows the system to overcome the limitations of individual sensors and achieve higher measurement precision and reliability in environmental data collection, directly resolving the technical contradiction between device complexity and measurement precision.
Solution Approach 2:
The sensor system is designed with multi-functionality, where each sensor type serves multiple purposes. For example, the system can switch between different sensor types depending on the operating conditions and environment type, making the system universally applicable across diverse environments while maintaining high measurement precision without proportionally increasing complexity.
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 system employs self-service mechanisms through automated sensor selection and data fusion algorithms. The processor automatically determines which sensor types to activate based on current operating conditions, environment type, and required measurement precision, eliminating the need for manual configuration and reducing operational complexity despite having multiple sensor types available.
Solution Approach 2:
The sensor system is dynamic in its operation, selectively activating different sensor types based on real-time conditions. The system can adapt its sensor configuration dynamically, using only the necessary sensors for current tasks, which maintains reliability while managing complexity through conditional activation rather than constant operation of all sensors.
3Adaptability or versatility
If comprehensive environmental mapping is performed using multiple sensors, then the adaptability to diverse environments improves, but the use of energy increases
Solution Approach 1:
The system applies partial action by selectively activating only the necessary subset of sensors required for the current environment and task, rather than continuously operating all sensors. This approach maintains adaptability to diverse environments while significantly reducing energy consumption by avoiding unnecessary sensor operation.
Solution Approach 2:
The environmental mapping is performed periodically rather than continuously, with the system updating its environmental model at appropriate intervals based on movement and environmental changes. This periodic action maintains adaptability while reducing overall energy consumption compared to continuous sensing and mapping.
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 multi-sensor approach enhances the accuracy and robustness of environmental mapping, improving UAV functionality by providing precise environmental representations for navigation and obstacle avoidance in various environments.
Implementation Method 1
The proximity sensor can comprise at least one of the following: a lidar sensor
Implementation Method 2
The proximity sensor can comprise at least one of the following: an ultrasonic sensor
Implementation Method 3
The plurality of sensors can comprise a global positioning system (GPS) sensor
Data Source
AI summary
A method for controlling an unmanned aerial vehicle (UAV) includes receiving first sensor data relative to a first coordinate system and second sensor data relative to a second coordinate system from a first sensor and a second sensor, respectively. The first and second sensor data includes first and second obstacle occupancy information indicative of relative locations of a first and a second sets of obstacles in reference to the UAV in the first and second coordinate systems, respectively. The first and second sets of obstacles have at least a subset of obstacles in common. The method further includes converting the first and second sensor data into a single coordinate system using sensor calibration data to generate an obstacle occupancy grid map based on the first and second obstacle occupancy information, and effecting the UAV to navigate using the obstacle occupancy grid map to perform obstacle avoidance.