MEC Edge-DAA Resolution Advisories for UAV Collision Avoidance
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Solution Overview
Problem
Unmanned Aerial Systems (UAS) face challenges in safely managing UAV traffic in airspace due to the lack of effective collision avoidance systems, particularly in environments where traditional systems may be inadequate.
Innovation Solution
Implementing a Mobile Edge Computing (MEC) node that provides an edge detect and avoid (edge-DAA) function, enabling UAVs to receive and act on resolution advisories to avoid collisions with objects in the airspace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a local DAA function is implemented within the UAV, then the UAV can independently determine resolution advisories to avoid collisions, but the system lacks comprehensive situational awareness and may provide suboptimal collision avoidance decisions
Solution Approach 1:
The MEC node acts as an intermediary between the UAV and the broader airspace environment. It receives situational data from multiple sources, processes this information centrally, and provides enhanced resolution advisories to the UAV, thereby bridging the gap between limited onboard capabilities and comprehensive situational awareness
Solution Approach 2:
The system merges the local DAA function within the UAV with the edge-DAA function at the MEC node. This combination allows the UAV to maintain independent collision avoidance capability while simultaneously benefiting from the MEC node's broader situational awareness and more accurate resolution advisories
2Measurement precision
If an edge-DAA function is provided by the MEC node, then the UAV receives more accurate resolution advisories based on comprehensive situational awareness, but the system complexity and dependency on external infrastructure increase
Solution Approach 1:
The MEC node serves as an intermediary that centralizes complex processing tasks. By offloading situational awareness and resolution advisory generation to the MEC node, the system achieves high measurement precision without requiring each UAV to possess equivalent computational complexity
Solution Approach 2:
The system segments functionality between the UAV and MEC node. The UAV handles basic local DAA operations while the MEC node provides enhanced edge-DAA capabilities. This segmentation allows each component to be optimized independently, managing overall system complexity
3Reliability
If the UAV prioritizes acting on the second RA from the MEC node, then collision avoidance effectiveness is improved, but the response time and latency in critical situations may increase
Solution Approach 1:
The MEC node performs preliminary processing of situational data and generates resolution advisories in advance. By maintaining continuous situational awareness and pre-computing potential resolution strategies, the system reduces the time required to provide accurate collision avoidance guidance when needed
Solution Approach 2:
The system implements feedback mechanisms where the UAV communicates its state and received advisories back to the MEC node. This continuous feedback loop allows the MEC node to refine its situational awareness and optimize resolution advisories in real-time, improving both effectiveness and response time
Data Source
AI summary
An unmanned aerial vehicle (UAV) may detect a risk of collision with one or more objects in an airspace serviced by a mobile edge computing (MEC) node. The MEC node may provide an edge detect and avoid (edge-DAA) function for use in the airspace. The UAV may determine a first resolution advisory (RA) to be acted on in order to avoid the collision with the one or more objects based on a local DAA function within the UAV. The UAV may receive, from the MEC node, a second RA to be acted on in order to avoid the collision with the one or more objects based on the edge-DAA function. If the second RA can be acted onto avoid the collision with the one or more objects, the UAV may act on the second RA and may send a message to the MEC node with an acknowledgement.