Slung Load Volume Prediction for Helicopter Obstacle Avoidance
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Solution Overview
Problem
Aerial vehicles, such as helicopters, face challenges in avoiding obstacles while carrying slung loads, particularly in predicting the volume occupied by the load and interacting with the environment.
Innovation Solution
A data processing system associated with the aerial vehicle predicts the volume occupied by the slung load based on parameters such as altitude, environmental data, and sensor inputs, and uses this prediction to define a route that avoids collisions with obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the aerial vehicle carries a slung load, then the load can be transported to the destination, but the volume occupied by the load is difficult to predict and obstacle avoidance becomes challenging
Solution Approach 1:
The system performs preliminary actions by receiving load parameters (dimensions, cable length) before flight, pre-calculating the conical volume envelope that the slung load will occupy. This advance preparation allows the obstacle avoidance system to plan routes that account for the load's spatial requirements before the aerial vehicle even deploys the load.
Solution Approach 2:
The patent introduces an intermediary computational model that translates physical load parameters into a predicted volumetric envelope. This intermediary representation (the conical volume) serves as a mediator between the tangible slung load and the digital terrain map, enabling the obstacle avoidance algorithm to reason about load-terrain interactions without directly measuring the load's actual position and orientation in real-time.
2Reliability
If the route is defined to avoid obstacles considering load volume, then collision avoidance is improved, but the route may require increased altitude or longer transit time
Solution Approach 1:
The system dynamically adjusts the route planning based on the predicted load volume and terrain data. Rather than using a fixed altitude or path, the obstacle avoidance algorithm continuously evaluates multiple potential routes, selecting those that maintain safe clearance between the load's conical envelope and terrain obstacles. This dynamic optimization balances collision avoidance with efficient transit by finding the optimal trade-off between altitude, distance, and time.
3Measurement precision
If real-time sensor data is used to update load volume predictions, then obstacle avoidance accuracy is improved, but the system complexity increases
Solution Approach 1:
The system implements feedback by using real-time sensor data (accelerometers, gyroscopes, GPS) to monitor the aerial vehicle's motion and updating the load volume prediction accordingly. As the vehicle accelerates, turns, or changes altitude, the feedback from sensors allows the system to recalculate the conical envelope's orientation and position, maintaining accurate obstacle avoidance predictions throughout the flight without requiring complex additional hardware.
Data Source
AI summary
Load volume predication for slung loads of aerial vehicles are disclosed. A system can identify parameters of a load. The system can determine a volume occupied by a superposition of locations of the cone. The system can receive from sensors or models, characteristics of an environment associated with the aerial vehicle or the load. The system can detect an obstacle in the environment. The system can take a navigational action in response to detecting the obstacle.