Flight Path Planning Using Reinforcement Learning and Visual Data
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Solution Overview
Problem
Current air traffic control systems rely heavily on human decision-making, which becomes inefficient and unsafe as air traffic increases, as they fail to consider global airspace conditions and visual images, limiting airspace capacity and flight safety.
Innovation Solution
A method and device for flight path planning that integrates reinforcement learning-based decision-making with both flight trajectory and visual image data, using a route planning module, action selection module, and fusion module to generate fused features for efficient and safe flight path decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human air traffic controllers make real-time decisions based on flight trajectories only, then aircraft interval maintenance is ensured, but global airspace utilization is inefficient and airspace capacity is limited
Solution Approach 1:
The patent combines flight trajectory data with visual image data from ATC systems to create a comprehensive decision-making input. The reinforcement learning model processes both structured trajectory information and unstructured visual information simultaneously, enabling controllers to maintain safety intervals while optimizing global airspace utilization through enhanced situational awareness.
Solution Approach 2:
The patent adds a new dimension of visual image data to the traditional one-dimensional flight trajectory data. By integrating spatial visual information from ATC displays with temporal trajectory data, the system creates a multi-dimensional representation of airspace状况, enabling more efficient path planning and higher airspace capacity while maintaining safety.
2Reliability
If existing reinforcement learning methods use only flight trajectory data as input, then local airspace interval maintenance is achieved, but global traffic situation awareness is insufficient for efficient path planning
Solution Approach 1:
The patent merges flight trajectory data with visual image data from ATC systems to create a comprehensive decision-making input. The reinforcement learning model processes both structured trajectory information and unstructured visual information simultaneously, enabling controllers to maintain safety intervals while optimizing global airspace utilization through enhanced situational awareness.
3Adaptability or versatility
If air traffic control systems rely entirely on human decision-making, then flexibility in complex situations is maintained, but decision-making efficiency decreases and safety is compromised under increasing air traffic flow
Solution Approach 1:
The patent implements a reinforcement learning-based automated decision-making system that processes flight trajectory and visual image data to generate path planning recommendations independently. The system learns from historical data and continuously improves its decision-making capabilities, providing efficient and safe recommendations to air traffic controllers while reducing their workload and decision-making pressure under increasing air traffic flow.
Data Source
AI summary
The invention discloses a method to support the trajectory planning considering both the flight trajectory and the visual images (VI) from air traffic control (ATC) system for air traffic controllers (ATCOs) (VI from ATC system for ATCOs), comprising the following steps: Step 1: acquire the VI and the flight trajectory to serve as the method inputs, and extract features of the VI and the relative position of the aircraft; Step 2: construct reinforcement learning-based methods to support the decision-making for flight path planning and conduct the training procedures of the models in the proposed method; Step 3: based on the optimized reinforcement learning-based methods, predict the required operation sequence to guide the flight to the target waypoint. The method of the invention can support the flight path planning for air traffic operation in a safe and efficient manner and is able to reduce the workload of air traffic controllers.