Assured Mission Adaptation of UAVs

Assured Mission Adaptation of UAVs


UAV is the acronym of Unmanned Aerial Vehicles, which we commonly refer to as drones in our day-to-day life. We have seen drones make last-mile delivery easy for delivery companies. Firefighters can use it to aid their firefighting efforts and photographers for capturing photographs from breath-taking spots. Local government authorities can also use drones for surveillance to ensure law & order. UAV’s are also widely used by the military for patrolling, search and rescue operations, or even neutralize enemies in warfare.

Image credit: Christopher Michel via Wikimedia (CC BY-SA 2.0)

Most UAVs are programmed to do specific tasks and their application is limited to strongly related goals. For example, a drone by Amazon would fly from their warehouse and deliver a package to their customers, in which case its task would be to pick, fly & drop. In case of package returns, it would be fly, pick & drop. However, the overall architecture of the drone’s function does not change much.

Another application of UAVs could be surveillance. In this case, a camera would be mounted on the drone to capture live images/videos of the area. This UAV could also be remotely controlled, in which case it would be referred to as remote piloted aircrafts, or RPA. 

Given the wide spectrum of applications for UAV’s, it has been a topic of great interest for researchers. 

How can a UAV accommodate applications that were not foreseen at design time?

For example, how can a UAV designed for simple surveillance adjust to locate a missing person in a red jacket in a patrol area? This change in objective would mean a lot of inherent changes for the UAV. The unmanned system could be required to change altitude, speed, camera angle, camera setting (high definition/low definition), speed of flight, use additional optical filters, perform communication with control team, etc. 

The research paper by Sebastian A. Zudaire, Leandro Nahabedian, and Sebastian Uchite explores how an UAV can accommodate a real-time change in objective. In the words of the researchers,

The main contributions of this paper is a system for adapting UAV missions with correctness guarantees that a) builds on discrete event controller update but extends it to liveness properties to support typical mobile robot missions, b) that implements a hybrid controller architecture that incorporates reconfiguration capabilities. We demonstrate, in real and simulated flights, how a UAV running a mission can be adapted at runtime to new missions that may require changes to workspace discretization, software sensors and software actuators. We show how UAV missions and mission adaptations can be specified. This sets the appropriate level of abstraction to present an overview of the architecture and detailed description of the software architecture and main software components when applied to UAVs. We then report on our validation efforts and conclude with a discussion and future work.

The research paper proposes applying MORPH adaptive software architecture in UAVs to transition from old to newer mission goals. It also explores architectural changes to the UAV’s configuration, such as adding new sensors, actuators that may be required to accommodate the newer goal. 

Key factors such as technical feasibility, UAV flexibility, Hybrid Control Layer flexibility & mission variability are also validated in this research work. The paper also refers to robotic literature and asserts how new mission goals can be synthesized and be hot-swapped on the UAV during system’s runtime.  

Future Work

Research by the team can be extrapolated to multiple UAVs as a lot of missions of interest include groups of UAVs used simultaneously.


UAVs do not only have wide applications in our day-to-day life, but they are also actively used by the military and government for execution of critical tasks. The research paper explores how a mobile robot can adapt to unforeseen changes in high-level goals at runtime. The proposed architecture is built on hybrid control, dynamic controller update and adaptive software architecture supporting behavioral and structural adaptation. Experimental results also support the notion of mission adaptation in runtime for real UAV system

Source: Sebastian A. Zudaire, Leandro Nahabedian, and Sebastian Uchitel, “Assured Mission Adaptation of UAVs


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