A resource and energy-efficient real-time delivery scheduling framework for a network of autonomous drones

2018 - present
In partnership with

Team Members

Dr. Anwar Haque
Dr. Anwar Haque PI
Roberto Solis Oba
Roberto Solis Oba Co-PI
Gopi Gugan
Gopi Gugan Former MSc Student
Fu Chen
Fu Chen MSc Student
Yifang Liu
Yifang Liu MSc Student
Nicole Chow
Nicole Chow UG Student
Joshua Noble
Joshua Noble UG Student
Caroline Owen
Caroline Owen UG Student


The use of unmanned aerial vehicles (UAV) or drones appears to be a viable, low-cost solution to problems in many applications. However, the limited onboard computing resources and battery capacity make it challenging to deploy drones for long-distance missions. Path planning capabilities are essential for autonomous control systems. An autonomous drone must be able to rapidly compute feasible, energy-efficient paths to avoid collisions. We first evaluate existing sampling-based algorithms' performance and present a hybrid samplingbased algorithm to generate a solution quicker, using less memory. We then introduce the notion of a layered graph, which accurately and efficiently models the search environment. Simulations show that when applying a modified A* algorithm on the layered graph, paths can be generated at least twice as fast, using significantly less memory than the samplingbased algorithm. Finally, we propose a novel cell-based model that uses a network of drones to perform longrange tasks such as last-mile deliveries. Drone charging stations are strategically placed to ensure that drones can replenish their batteries. The genetic algorithm was implemented to solve the scheduling problem for multiple drones using this model. We show that this model can be used to deliver many packages within a short amount of time.


  1. MSc Thesis: A resource and energy-efficient real-time delivery scheduling framework for a network of autonomous drones

    Computer Science, Western University

  2. Path Planning for Autonomous Drones: Challenges and Future Directions

    submitted to IEEE Communications Magazine

  3. Developing a Resource and Energy Efficient Real-time Delivery Scheduling Framework for a Network of Autonomous Drones

    submitted to IEEE Transactions on Intelligent Transportation Systems

  4. Simple and Efficient Algorithm for Drone Path Planning

    55th 2021 IEEE International Conference on Communications (ICC 2021)

  5. Towards the Development of a Robust Path Planner for Autonomous Drones

    2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-6

  6. RADR: Routing for Autonomous Drones

    in proceedings of the 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco, 2019, pp. 1445-1450


  1. Interview Topic: Autonomous Drones