Cooperative UAV Path Planning With Velocity Obstacles
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Solution Overview
Problem
As the use of unmanned aerial vehicles (UAVs) increases, especially in congested airspace, there is a need for efficient autonomous flight path planning systems that can dynamically adjust flight paths to avoid obstacles and ensure safe navigation, particularly for groups of aircraft with varying configurations and capabilities.
Innovation Solution
The implementation of a control system on each aircraft, equipped with sensors like GPS, LiDAR, and communication components, which uses optimization algorithms to calculate and adjust flight paths, allowing aircraft to dynamically avoid obstacles and maintain formation or operate independently, using velocity obstacles calculations to ensure collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple UAVs operate in congested airspace, then aerial photography and delivery services can be provided, but collision risk and navigation complexity increase
Solution Approach 1:
The system segments the navigation decision-making process into individual UAV agents, each independently calculating its own velocity obstacles and planning its path while considering others in the group. This distributed segmentation allows parallel operation without centralized coordination bottlenecks.
Solution Approach 2:
Each UAV pre-calculates velocity obstacles for all other UAVs before finalizing its flight path. This preliminary calculation of potential collision vectors allows the navigation algorithm to proactively plan collision-free trajectories rather than reacting to conflicts in real-time.
2Reliability
If dynamic path adjustment is implemented to avoid obstacles, then collision avoidance is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent replaces complex mechanical collision avoidance maneuvers with a computational velocity obstacle field model. Instead of relying on reactive mechanical responses, the system uses mathematical calculations of velocity vectors and obstacle zones to determine optimal navigation paths.
Solution Approach 2:
The system dynamically changes flight path parameters by calculating velocity obstacles as mathematical constructs rather than fixed physical barriers. The velocity obstacle parameters are continuously updated based on relative positions and velocities, allowing adaptive path planning without increasing hardware complexity.
3Measurement precision
If velocity obstacles calculations are performed for all UAVs, then collision avoidance accuracy is improved, but computational load and processing time increase
Solution Approach 1:
Each UAV performs velocity obstacle calculations for all other UAVs in the group, which may seem excessive, but this comprehensive approach ensures complete collision coverage. The computational load is distributed across all UAVs rather than concentrated in a single controller.
Solution Approach 2:
Each UAV independently performs its own velocity obstacle calculations and path planning without requiring external computational assistance. This self-service approach distributes the computational energy burden across all UAVs in the group, with each unit serving its own navigation needs.
Data Source
AI summary
A method of autonomous flight path planning for a group of cooperating aircraft operating in formation includes: receiving information related to obstructions that interfere with an aircraft of the group continuing on a flight path; calculating a velocity obstacle for each obstruction; calculating a plurality of candidate velocities outside of the velocity obstacles; selecting a first velocity from the candidate velocities and operating a leader of the group at the first velocity; calculating, based upon keeping a follower aircraft in formation, a second velocity for the follower; determining whether the second velocity is inside one of the velocity obstacles; operating the follower at the second velocity when the second velocity is outside the velocity obstacles; and calculating a revised velocity that is outside of the velocity obstacles and operating the follower at the revised velocity when the second velocity is inside one of the velocity obstacles.