UAV Collision Avoidance Using LiDAR and Velocity Potential Fields
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Solution Overview
Problem
Existing collision avoidance algorithms for unmanned aerial vehicles (UAVs) are inadequate in densely packed urban areas, particularly when dealing with moving obstacles and unpredictable situations, as they often rely solely on distance-based potential fields, which fail to account for relative velocities and omnidirectional obstacle recognition.
Innovation Solution
A method using LiDAR as an obstacle detection sensor to calculate two potential fields, compute attractive and repulsive forces, and adjust the UAV's direction to avoid collisions, considering both stationary and moving obstacles, by incorporating relative velocity and omnidirectional sensing, and employing a Kalman filter for velocity estimation of obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a general potential field algorithm is used that considers only distance to obstacles, then the algorithm is simple to implement, but the collision avoidance performance with moving obstacles is poor
Solution Approach 1:
The patent modifies the potential field algorithm by changing the parameters considered from only distance to include relative velocity of obstacles. The repulsive force is recalculated using both distance and relative velocity parameters, allowing the UAV to respond more effectively to moving obstacles while maintaining the overall structure of the potential field approach.
2Adaptability or versatility
If non-collaborative sensors are used to recognize obstacles, then the system can detect both terrain features and expected obstacles, but the system complexity increases compared to collaborative sensors
Solution Approach 1:
The patent employs non-collaborative sensors that serve multiple functions: detecting terrain features, identifying expected obstacles, and providing distance measurements. This multi-functional approach allows a single sensor system to handle various detection tasks without requiring separate specialized sensors for each function.
3Ease of operation
If bearing angle algorithms are used to keep obstacles at a safe position in the field of view, then collision avoidance is achieved in simple scenarios, but the algorithm becomes difficult to utilize when various obstacles are recognized by the image sensor
Solution Approach 1:
The patent transitions from a static bearing angle approach to a dynamic potential field approach where the repulsive force continuously adapts based on real-time obstacle position and relative velocity. This dynamic adjustment allows the algorithm to handle multiple obstacles in various configurations while maintaining effective collision avoidance.
Data Source
AI summary
Provided is a method of avoiding collision of an unmanned aerial vehicle with an obstacle, the method including: calculating two potential fields using ament positional information of the unmanned aerial vehicle, a target point that is set, and positional information of the obstacle measured by a sensor, computing an attractive force and a repulsive force by differentiating the computed potential fields, respectively; computing a direction of a potential force that results from adding up the computed attractive force and repulsive force; and performing control that brings about a change from the computed direction of the potential force to a direction in which the unmanned aerial vehicle moves.