Leading Underwater Drone Path Prediction for Obstacle Sensing
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Solution Overview
Problem
Current drone technologies lack the capability to efficiently navigate and provide real-time sensor data ahead of a moving base station, limiting their ability to anticipate and adapt to changing environments and obstacles.
Innovation Solution
A leading drone system that predicts the future location of a base station and moves accordingly, collecting and processing sensor data to provide real-time information and perform tasks such as obstacle avoidance and path adjustment, while also interacting with sensor drones to enhance data collection and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the drone follows the base station's current location, then the drone can maintain communication and coordination with the base station, but the drone cannot anticipate future obstacles or environmental changes ahead of the base station's path
Solution Approach 1:
The drone performs preliminary actions by predicting the base station's future location and moving ahead to that predicted location before the base station arrives. This allows the drone to collect sensor data and identify obstacles in advance, improving response time while maintaining navigation reliability through continuous communication with the base station.
2Loss of information
If the drone moves ahead of the base station to collect sensor data, then the drone can provide real-time environmental information, but the drone may lose communication contact with the base station
Solution Approach 1:
The system implements feedback mechanisms where the drone continuously reports its position and collected sensor data to the base station, and the base station provides guidance signals back to the drone. This feedback loop maintains communication reliability while enabling the drone to move ahead and collect valuable environmental information.
3Adaptability or versatility
If the drone predicts future location based on base station movement, then the drone can proactively navigate to anticipate obstacles, but the prediction accuracy may be insufficient in dynamic environments
Solution Approach 1:
The prediction system is designed to be dynamic and adaptive, continuously adjusting prediction parameters based on the base station's actual movement patterns and environmental conditions. The drone can recalculate predicted locations in real-time as new data becomes available, improving prediction accuracy in dynamic environments while maintaining adaptive navigation capabilities.
Data Source
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AI summary
Systems and methods are provided for least one leading drone configured to move to a leading drone future location based on a future location of a base station. A set of base station future locations may form a base station path for the base station to traverse. Also, a set of leading drone future locations may form a leading drone path for the leading drone to traverse. The base station's future location may be anticipated from a prediction or a predetermination. The leading drone, navigating along the leading drone path, may collect sensor data and/or perform tasks. The leading drone may interact with sensor drones while traversing the leading drone path. Accordingly, the leading drone may move ahead of the base station in motion, as opposed to following or remaining with the base station.