IntelliDrones has been selected as a finalist for Drones for Good Award 2017.


Drones (also known as unmanned aerial vehicles) are becoming a convenient means of logistics. In particular, the civilian markets for drones are forecast to be booming globally. The notable areas of drone applications include delivery (e.g., for light-weight parcels, medical items, airmail) and remote operations (e.g., farming, environmental surveying, search and rescue operations). Remarkably, oil and gas companies and utility providers, which rely on extensive surveillance, measurements, mapping and surveying, maintenance operations for geographically diverse facilities, will be the primary customers of drones for carrying out remote missions involving minimal transportation of manpower to hazardous environments.

Despite increasingly popular uses of drones in the civilian sectors, most drones are typically controlled by human operators, using remote controllers. Piloting a drone is intrinsically difficult for human. Manual controls not only incur inefficiency and inconvenience, but also are vulnerable to human errors. Often, intensive training for drone piloting is required in advance, which are usually expensive and laborious. On the other hand, improper human controls will cause drone crashes and damages. There are many repetitive routine missions that are more suitably carried out by drones, and hence, it is desirable to dispense with human controls.

In this work, we develop a working prototype, called IntelliDrones, which is a fully automated intelligent management system for drone fleet. It is designed for a sizable fleet of drones, which can be applied in a wide range of applications of drone delivery and remote missions. In summary, IntelliDrones system supports the following features:

  1. Automatic flight path planning and optimization according to users' specified goals.
  2. Real-time flight path tracking and re-computation in dynamic environments.
  3. Robotic inductive charging station to support autonomous recharging for drones.
  4. Recharging scheduling and optimization to maximize battery life.

Sid Chi-Kin Chau, Majid Khonji, Mohammad Al Shehhi, Chien-Ming Tseng, Khaled Elbassioni

Contact us

Email team leader (Sid Chi-Kin Chau) at ckchau(at)