Multi-Drone Flight Path Allocation by Vehicle Attributes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining flight paths for multiple aerial vehicles in a predetermined region are inefficient, requiring manual setting of flight regions and failing to consider attributes like starting position, work efficiency, and battery capacity, leading to ineffective workload distribution and potential overlaps or omissions.
Innovation Solution
A method that automatically divides a predetermined region into sub-regions based on the attributes of multiple aerial vehicles, including determining optimal starting positions and workload distribution to minimize flight paths and ensure efficient work, using an information processing device to communicate and display the divided regions and flight paths to the vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple aerial vehicles work simultaneously in a predetermined region, then work efficiency is improved, but manual setting of flight regions becomes more complicated and burdensome
Solution Approach 1:
The system performs preliminary automatic division of the work region into sub-regions based on aerial vehicle attributes before the vehicles start working. This preliminary action eliminates the need for manual flight region setting during operation, reducing user burden while enabling multiple vehicles to work efficiently.
Solution Approach 2:
The system enables self-service by automatically determining flight regions and flight paths based on the aerial vehicles' own attributes (starting position, work efficiency, battery capacity). Each vehicle receives a customized flight region and path without requiring manual configuration, allowing the system to serve itself rather than requiring continuous user intervention.
2Ease of operation
If manual operation is used to set flight regions, then user control is maintained, but work regions cannot be accurately divided leading to omitted or overlapping work
Solution Approach 1:
The system incorporates feedback by considering the actual attributes of aerial vehicles (starting positions, work efficiency, battery capacity) when automatically dividing work regions. This feedback mechanism ensures that the automatic division accurately reflects the capabilities and positions of the vehicles, preventing overlapping or omitted work while maintaining operational effectiveness.
Solution Approach 2:
The system changes the parameters used for region division from simple geometric splitting to attribute-based allocation. By using parameters such as starting position, work efficiency, and battery capacity, the system achieves accurate work region division that adapts to the specific characteristics of each aerial vehicle, eliminating the imprecision of manual operation.
3Ease of manufacture
If the predetermined region is mechanically and evenly divided, then division is simple, but attributes of each aerial vehicle are not considered leading to ineffective flight paths
Solution Approach 1:
The system applies local quality by allocating different work region sizes and characteristics to different aerial vehicles based on their individual attributes. Vehicles with higher work efficiency or better battery capacity receive larger or more demanding sub-regions, while others receive appropriately sized regions. This ensures that each vehicle operates at optimal effectiveness rather than receiving uniform, potentially suboptimal allocations.
Solution Approach 2:
The system introduces asymmetry by abandoning even mechanical division in favor of asymmetric region allocation based on vehicle attributes. The work regions are divided unevenly according to each vehicle's starting position, work efficiency, and battery capacity, creating an asymmetric distribution that optimizes overall work effectiveness rather than maintaining simple symmetry.
4Device complexity
If uniform region division is used, then calculation is straightforward, but workload is not appropriately distributed among aerial vehicles
Solution Approach 1:
The system implements dynamics by making the region division adaptive to the specific attributes of the aerial vehicles involved. Rather than using a fixed uniform division method, the system dynamically calculates and allocates work regions based on the actual starting positions, work efficiencies, and battery capacities of the vehicles. This dynamic approach ensures appropriate workload distribution while maintaining manageable calculation complexity through algorithmic automation.
Data Source
AI summary
A flight path determination method includes obtaining first information of a predetermined region, obtaining second information of multiple aerial vehicles, dividing the predetermined region into a plurality of sub-regions where the multiple aerial vehicles respectively work based on the second information, and determining a flight path for each of the plurality of sub-regions.