Manned VTOL Collision Avoidance Using Repulsion Field Control
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Solution Overview
Problem
Manned vertical take-off and landing (VTOL) aerial vehicles face challenges in collision avoidance due to factors like high-speed collisions, poor visibility, and pilot error, especially in high-density airspace where multiple vehicles occupy similar flight paths, increasing the risk of accidents with objects or other vehicles.
Innovation Solution
The implementation of a control system in VTOL aerial vehicles that includes a sensing system, processor, and memory to determine state estimates, generate repulsion potential field models, and calculate control vectors to avoid collisions by adjusting propulsion based on collision avoidance velocity vectors and input vectors from pilot-operable controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a control system with sensing system and processor is implemented to determine state estimates and generate control vectors, then collision avoidance capability is improved, but device complexity increases
Solution Approach 1:
The control system is segmented into distinct functional modules: sensing system for data acquisition, processor for state estimation and collision avoidance velocity vector calculation, and actuation system for propulsion control. This modular segmentation improves reliability by isolating functions while managing complexity through structured organization of system components.
Solution Approach 2:
The system performs preliminary determination of state estimates and collision avoidance velocity vectors before actual collision occurs. The processor continuously calculates potential collision scenarios and prepares control vectors in advance, enabling proactive collision avoidance rather than reactive response, thereby improving safety without requiring overly complex real-time intervention systems.
2Measurement precision
If collision avoidance velocity vector calculation is performed based on state estimates, then navigation precision is improved, but computation time increases
Solution Approach 1:
The system calculates collision avoidance velocity vectors based on partial state estimates rather than complete exhaustive analysis. By focusing computation on critical state parameters directly related to collision risk (position, velocity, object proximity) rather than all possible state variables, the system achieves sufficient navigation precision while reducing computation time to acceptable levels for real-time operation.
Solution Approach 2:
The system implements continuous feedback loops where state estimates are updated based on sensor measurements and previous control actions. This feedback mechanism allows the processor to refine collision avoidance velocity vector calculations iteratively, improving accuracy over time while maintaining efficient computation through incremental updates rather than complete recalculation.
3Measurement precision
If repulsion potential field model is generated from sensor data, then obstacle detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The repulsion potential field model serves as an intermediary representation between raw sensor data and collision avoidance control. Instead of directly processing complex sensor data for obstacle detection, the system transforms data into a potential field model where obstacles generate repulsion forces. This intermediary model simplifies subsequent collision avoidance calculations while maintaining high detection accuracy, as the field model encapsulates obstacle information in a computationally efficient format.
Data Source
AI summary
A manned vertical take-off and landing (VTOL) aerial vehicle comprises: a body comprising a cockpit having pilot-operable controls; a propulsion system carried by the body to propel the body during flight; a control system comprising a sensing system, a processor, and memory storing program instructions configured to cause the processor to determine a state estimate of the aerial vehicle within a region, a repulsion vector based on a repulsion potential field model of the region and the state estimate, and a collision avoidance velocity vector based on the repulsion vector and the state estimate; determine an input vector indicative of an intended angular velocity and an intended thrust of the vehicle based on pilot-operable control inputs; determine a control vector based on the collision avoidance velocity vector and the input vector; and control the propulsion system such that the manned VTOL aerial vehicle avoids an object in the region.