Interdependent Motion Planning for Dissimilar Airborne Vehicles
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Solution Overview
Problem
Current air traffic control processes are manual and inefficient, struggling to manage the increasing demand for flights, especially with the integration of autonomous vehicles, and fail to account for diverse flight characteristics of crewed and uncrewed aircraft, leading to a need for higher-fidelity automation in air traffic management.
Innovation Solution
A ground-based motion planning system that routes aircraft through airspace by determining starting and goal states, connecting them via incremental projections, generating candidate next states, culling unsafe states, and selecting optimal paths based on system cost and safety, while considering flight characteristics, hazards, and priority levels, to create efficient and safe flight plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual air traffic control processes are used, then operators can verify aircraft safety and routing, but the system cannot handle the increasing demand for flights and integration of autonomous vehicles
Solution Approach 1:
The patent replaces manual mechanical control processes with an automated motion planning system that uses computational algorithms to generate and evaluate flight paths. The system substitutes human operators' manual verification with automated safety checks and conflict detection algorithms, enabling the air traffic control system to handle increasing flight volumes without proportional increases in human staff.
Solution Approach 2:
The motion planning system enables aircraft to effectively plan their own paths through the airspace by evaluating candidate states and selecting optimal routes that avoid conflicts with other aircraft and hazards. This self-service capability allows the system to autonomously manage air traffic without requiring constant human intervention, thereby increasing productivity while maintaining safety.
2Reliability
If diverse flight characteristics of crewed and uncrewed aircraft are not accounted for, then routing processes remain simple, but the system fails to provide safe and efficient air traffic management
Solution Approach 1:
The patent applies local quality by tailoring the motion planning process to each aircraft's specific characteristics. The system evaluates candidate next states based on individual aircraft performance parameters, flight dynamics, and operational constraints. This allows the routing system to account for diverse flight characteristics of different aircraft types while maintaining a unified planning framework, thereby improving safety without requiring completely separate systems for each aircraft type.
3Reliability
If the system generates all possible candidate next states for multiple aircraft, then complete path exploration is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the complex multi-aircraft routing problem into individual aircraft path planning subproblems. The system generates candidate next states for each aircraft separately based on its current state and constraints, then evaluates combinations of these individual candidates to form system-wide candidate states. This segmentation reduces the computational burden compared to generating all possible joint states simultaneously, while still ensuring complete safety verification through systematic evaluation of aircraft interactions.
Solution Approach 2:
The system generates candidate next states incrementally and evaluates them in order of promise, using heuristics to prune the search space. Rather than exhaustively generating all possible candidate states before evaluation, the system performs partial action by stopping the generation process once a sufficient number of promising candidates are evaluated and an optimal path is found. This approach balances computational efficiency with thorough safety verification.
Data Source
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AI summary
A motion planning system (100) and method receives requests (108) for routing a set of dissimilar aircraft through a defined airspace. Each aircraft (110) has a starting and destination position relative to the airspace. A start state is based on the earliest start, and a goal state on the destination. The system connects start and goal states via a sequence of next states by projecting each state incrementally forward in time. Candidate next states include all possible next paths to a next position of each aircraft combined with next paths and positions of its neighbors, with unsafe next states culled from the set. Remaining next states are prioritized on the basis of aggregate system cost for all next paths and an estimated system cost to achieve the goal state, and an optimal next state is added to the sequence. When the goal state is achieved, optimal flight plans are generated for each aircraft.