Multi-Drone Gas Detection Route Planning for Battery Limits
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Solution Overview
Problem
Current gas detection systems using aerial vehicles face challenges such as limited battery capacity, requiring frequent battery exchanges, power-consuming computation processing, and vulnerability to communication environment deterioration, leading to prolonged detection times and potential vehicle crashes. Additionally, detecting gases outside the atmospheric window band is difficult due to changing background spectra, especially in humid environments.
Innovation Solution
A gas detection system employing multiple aerial vehicles with a light-emitting unit and light-receiving units, where the receiving aerial vehicles transmit gas data to a computing unit, and a photographing-route computing unit optimizes routes based on energy remaining amounts to minimize battery exchanges and enable precise, short-time gas detection, even outside the atmospheric window band.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a high-capacity battery is installed in a drone to expand search range, then detection range is improved, but device weight and cost increase
Solution Approach 1:
The system divides the detection task among multiple drones, each with standard battery capacity. Instead of one drone with large battery, multiple drones with smaller batteries work together to cover the same area, reducing individual weight while maintaining total search range
Solution Approach 2:
Multiple drones are combined into a coordinated system where their collective detection capabilities cover the required area. The ground controller synchronizes their operations and coordinates battery exchange timing, achieving the effect of expanded range without individual drones carrying heavy batteries
2Measurement precision
If spectral dispersion processing is performed on the drone to improve gas detection precision, then measurement precision is improved, but power consumption increases requiring frequent battery exchange
Solution Approach 1:
The computationally intensive spectral dispersion processing is extracted from the mobile drone and performed on the stationary ground controller. The drone only needs to collect and transmit raw spectral data, while the heavy computation is done on the ground where power is abundant, reducing on-drone power consumption
Solution Approach 2:
A communication system acts as an intermediary between the drone's sensor and the ground controller's processing unit. Raw data is transmitted via wireless communication, allowing the drone to minimize onboard computation while still enabling precise spectral analysis on the ground
3Reliability
If autonomous control is implemented to prevent crashes when communication deteriorates, then reliability is improved, but device complexity increases
Solution Approach 1:
The drone is pre-equipped with autonomous return-to-base functionality and obstacle detection sensors before deployment. When communication is still available, the system pre-plans fallback routes and sets safe zones, so that when communication deteriorates, the drone can automatically execute pre-programmed safe return procedures without complex real-time decision-making
4Productivity
If multiple aerial vehicles are used to perform gas detection in a three-dimensional structure, then productivity is improved, but coordination complexity and time for battery exchange increase
Solution Approach 1:
The ground controller receives real-time status information from all drones including position, battery level, and detection progress. Based on this feedback, the controller dynamically adjusts drone routes and synchronizes battery exchange timing to ensure all drones are replaced simultaneously, minimizing system downtime and coordination complexity
Solution Approach 2:
The system uses dynamic route planning that adapts to real-time conditions. Drone paths are continuously optimized based on gas detection priorities, battery status, and spatial constraints, allowing flexible coordination without fixed rigid schedules that would increase complexity
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 system allows for efficient, precise, and rapid gas detection by synchronizing battery exchanges and optimizing routes, reducing waste and enabling detection of gases outside typical wavelength bands, while reducing the number of costly light-emitting units and improving data accuracy through spectral dispersion and correction techniques.
Implementation Method 1
Most of the gases targeted for detection have emissivity or transmittance inherent in the respective gas on an infrared band. By measuring the emissivity or transmittance, remote gas detection becomes possible.
Implementation Method 2
a method (passive type) of receiving radiant infrared light from gas, or a method (active type) of receiving reflected infrared light or transmitted infrared light of infrared light applied to gas
Data Source
AI summary
A gas detect ion system includes: a sending aerial vehicle in which a light-emitting unit is installed; a small unmanned aerial vehicle including a receiving aerial vehicle in which a light-receiving unit is installed; a gas computing and displaying unit that computes and displays gas information; and a photographing-route computing unit that computes a photographing route for the small unmanned aerial vehicle. The receiving aerial vehicle receives light from the light-emitting unit of the sending aerial vehicle by using the light-receiving unit thereof and sends the result as gas data to the gas computing and displaying unit. The gas computing and displaying unit computes the gas information from the gas data. The photographing-route computing unit computes the photographing route from the position of the small unmanned aerial vehicle and the amount of energy remaining in the small unmanned aerial vehicle.