Augmented Manual Flight Control for Obstacle-Proximity Navigation
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Solution Overview
Problem
Pilots face challenges in safely navigating aerial vehicles close to obstacles due to the high workload of managing vehicle position and avoiding collisions, which can be hazardous and unmanageable.
Innovation Solution
An augmented manual control mode is implemented for aerial vehicles, using existing sensors and world models to detect obstacles and dynamically set speed and attitude constraints, allowing pilots to freely navigate while preventing collisions with obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the pilot manually controls the aerial vehicle to navigate close to obstacles, then the aerial vehicle can complete aerial tasks requiring proximity to obstacles, but the pilot workload increases and collision risk increases making control unmanageable
Solution Approach 1:
The system introduces an intermediary automated control layer between the pilot and the aerial vehicle. This intermediary uses sensor data and world models to detect obstacles and automatically imposes speed and attitude constraints on manual inputs, reducing the pilot's direct control burden while maintaining the ability to navigate close to obstacles. The intermediary processes pilot commands through constraint checking and modification, effectively mediating between human intent and safety requirements.
Solution Approach 2:
The system enables the aerial vehicle to partially serve itself by automatically monitoring its own position relative to obstacles using onboard sensors and world models. The vehicle autonomously determines when to impose constraints based on detected proximity to obstacles, without requiring continuous pilot attention. This self-service capability reduces the pilot workload while maintaining safe navigation near obstacles.
2Reliability
If the pilot manually controls the aerial vehicle to avoid collisions, then collision prevention is attempted, but the control becomes unmanageable due to the complexity of simultaneously managing position and obstacle avoidance
Solution Approach 1:
The automated constraint imposition system acts as an intermediary that handles the complex collision avoidance calculations, freeing the pilot from managing both position and obstacle avoidance simultaneously. The intermediary automatically processes sensor data, determines constraint parameters, and modifies pilot inputs to prevent collisions, making control manageable while maintaining reliability.
Solution Approach 2:
The system performs preliminary obstacle detection and constraint determination before the pilot's manual input could result in a collision. By proactively identifying hazardous situations and pre-imposing appropriate constraints on speed and attitude, the system prevents collisions before they occur, maintaining reliability without requiring the pilot to manage complex real-time avoidance maneuvers.
3Reliability
If automated obstacle detection and constraint imposition is implemented, then collision prevention is improved, but the device complexity increases
Solution Approach 1:
The system achieves improved collision prevention by making existing multi-functional components serve additional purposes. The world model, originally designed for general navigation, is also used for obstacle detection. The speed and attitude control systems, already present for basic flight management, are enhanced to automatically impose constraints. This multi-functionality approach reduces the need for entirely new dedicated systems, thereby limiting the increase in device complexity.
4Reliability
If speed constraints are dynamically imposed when close to obstacles, then collision risk is reduced, but the pilot's ability to freely navigate is restricted
Solution Approach 1:
The system dynamically adjusts speed and attitude constraints based on real-time detection of obstacle proximity. When obstacles are detected, constraints are automatically imposed; when obstacles are not present, constraints are relaxed or removed. This dynamic adaptation allows the pilot to navigate freely in safe conditions while maintaining safety through automated constraints only when necessary, balancing collision avoidance with navigation freedom.
Data Source
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Figure 3A~3B
AI summary
Systems (100, 110) and methods for controlling an aerial vehicle to avoid obstacles are disclosed. A system (100,110) can detect, based on a world model (145) generated from sensor data captured by one or more sensors (125) positioned on the aerial vehicle (105) during flight, an obstacle (140) for the aerial vehicle, and trigger an augmented manual control mode responsive to a speed of the aerial vehicle (105) being less than a predetermined threshold and detecting the obstacle (140). The system (100, 110) can set, responsive to triggering the augmented manual control mode, a speed constraint for the aerial vehicle in a direction of the obstacle based on a distance between the aerial vehicle and the obstacle. The system (100, 110) can receive an instruction to navigate the aerial vehicle (105) in the direction at a second speed, and adjust the instruction to replace the second speed with the speed constraint, causing the aerial vehicle (105) to navigate at the speed constraint.