Project title: Control for precision autonomous delivery Unmanned Aircraft (UA) in contained environment based on vision data fusion
Eligibility: Indonesian nationals
Duration: Full-Time – between three and four years fixed term
Application deadline:
Interview date: Will be confirmed to shortlisted candidates
Start date: September 2026
For further details contact: Dr. Rianto Adhy Sasongko, S.T., M.Sc., Ph.D. (ITB, radhys@itb.ac.id), Dr. Thomas Statheros (CU, thomas.statheros@coventry.ac.uk)

Introduction

The project research focuses on investigating multirotor (Unmanned Aircrafts) UA safe and accurate flying parcel delivery capability in contained environment/GNSS-denied environment. The UA will rely on a single board processor, camera/vision sensors, IMS (inertial measurement unit), wireless data, and positioning technologies. Data fusion between vision sensors and wireless positioning will be investigated for the development of precision navigation, path planning and control methodologies. Therefore, the practical limitations related to UA real-time path planning, control, and data fusion among vision sensors will be explored. This project will investigate the accuracy and safety of UA within the working environment so that UA can operate safely within the indoor environment to deliver a parcel to specific indoor locations.

Project details

The project aims to investigate the navigation precision for parcel delivery of a UA multirotor in GNSS-denied and contained environments by using custom hardware and software and open-source vision, IMS, and RF positioning technologies, e.g., Ultra-Wideband (UWB).

Positioning and 3D local map design and testing: A 3D local positioning map will be created by combining visual, Inertial Measurement Unit (IUM) and RF sensors. The map will be updated in real-time. Different UA configurations, visual images, and real-time positioning accuracy and performance will be investigated.

UA simulation environment design testing: The indoor environment, the multirotor UAs (multirotor, S500, and D800 x8 heavy lift drone dynamics and control will be simulated and tested on state-of-the-art workstation with NVIDIA 6000 Ada graphics processor in Gazebo or Omniverse. Requirements for path planning and control algorithms will be validated and tested for offline and real-time efficiency and accuracy.

Safety of the Path-planning, Control, visual and RF positioning algorithms: The development of the related path-planning, control, and RF positioning, will be subject to safety evaluation related to the newer and developing international standards (related to distance from obstacle and humans, velocity, and system integrity).

Objectives:
  • Literature Review and state-or-the-art technology investigation: involves a comprehensive study and literature review of state-of-the-art UA precise path planning in GNSS-denied environments, the most effective control methodologies, different vision and RF positioning techniques, and the UA system safety requirements at the design level.
  • Determine the best design of the local world model generated by the UA sensors and the RF wireless technology positioning anchors.
  • Investigate the best real-time path planning algorithm for collision avoidance and navigation in various GNSS-denied environments.
  • Investigate the appropriate control algorithm to work in harmony with real-time path planning.
  • Identify the UA system safety level required when operating in an indoors or GNSS-denied environment with humans.

Funding

Tuition fees and bursary from LPDP or PDDI

Benefits

The successful candidate will receive comprehensive research training including technical, personal, and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Researcher College, which provides support with high-quality training and career development activities.

Entry requirements

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.

PLUS

  • The potential to engage in innovative research and to complete the PhD within 3.5 years.
  • A minimum of English language proficiency (IELTS academic overall minimum score of 6.5 with a minimum of 6.0 in each component).
Additional Requirements

Applicants should hold a good undergraduate or master’s degree in engineering, computer science, or a related discipline. Relevant background may include:

  • Path-planning and Control methodologies
  • Programming experience C++, Python, MATLAB
  • Single board computers/processors, e.g. NVIDIA Processors (e.g., NVIDIA Orin, Pixhawk)

The ideal candidate will be motivated, aerospace/system engineering inclined, interested in UA technology, control, programming, and path-planning.