Aerial Baggage Lowering Control for Stable Automated Descent
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Solution Overview
Problem
Current technologies face challenges in achieving stable and automatic piloting of aerial vehicles when lowering fragile baggage, as existing methods do not effectively account for the behavior of the baggage and external conditions during descent.
Innovation Solution
An information processing apparatus that acquires piloting and behavior histories of the aerial vehicle and connected baggage, using machine learning to establish a relationship between piloting actions and baggage behavior, considering conditions like connector stiffness, wind direction, and environmental factors to enable stable automatic piloting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual piloting is used to lower baggage, then the operator can adjust to baggage behavior, but automation cannot be achieved and stability cannot be guaranteed for fragile items
Solution Approach 1:
The system records piloting operations and baggage behavior history, then creates a learning model that copies the relationship between pilot actions and baggage responses. This allows the automated system to replicate skilled operator techniques without requiring actual human intervention during delivery.
Solution Approach 2:
The system continuously monitors baggage behavior (swinging, shaking) during lowering and uses this feedback to adjust piloting operations in real-time. The learning model is also continuously improved by incorporating new piloting history and behavior history data, creating a closed-loop system that enhances both immediate performance and long-term capability.
2Extent of automation
If piloting operations are recorded and analyzed, then learning of stable lowering techniques can be achieved, but system complexity increases
Solution Approach 1:
The information processing apparatus performs multiple functions: it records piloting operations, captures baggage behavior data, processes this information, and generates control commands. By consolidating these functions into a single multi-functional system, the patent avoids the complexity of separate systems for each function while achieving comprehensive automation.
Solution Approach 2:
The system automatically processes its own operational data without requiring external intervention. The learning unit autonomously analyzes piloting history and behavior history, and the control unit self-adjusts based on learned patterns, enabling the system to improve its own performance without additional complexity from external training mechanisms.
3Manufacturing precision
If external conditions like wind and connector properties are considered, then lowering accuracy improves, but the amount of data to process increases
Solution Approach 1:
The system pre-acquires information about external conditions such as wind direction, connector stiffness, and baggage properties before the lowering operation begins. This preliminary data collection allows the learning model to account for these factors during processing without requiring real-time data acquisition, reducing the data processing burden during critical operations.
Solution Approach 2:
The system combines multiple data sources including piloting operations, baggage behavior, and external conditions into a unified learning model. By merging these related data streams into a single integrated processing framework, the system avoids the complexity of handling separate data volumes for each factor and achieves synergistic improvements in lowering precision.
Data Source
AI summary
Baggage is connected to an aerial vehicle in a state in which the baggage is suspended by a connector such as rope. A learning unit of a server device performs machine learning on the relationship between the piloting of an aerial vehicle and the behavior of baggage on the basis of an aerial vehicle behavior history and a piloting history acquired by a first acquisition unit and a baggage behavior history acquired by a second acquisition unit. With this arrangement, the automatic piloting of the aerial vehicle at the time of lowering baggage is achieved.