Research Assistant (Multi-UAV Target Search)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Assistant-%28Multi-UAV-Target-Search%29/33554-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

This project focuses on developing autonomous multi-drone swarm systems for collaborative search and target localization in low-rise urban environments. The target scenarios involve GNSS-denied conditions, low-light areas, cluttered indoor/outdoor spaces, narrow openings, double-storey buildings, limited communication bandwidth, RF communication losses, and potential drone failures during the mission. The candidate will investigate both conventional robotics pipelines and recent AI-based approaches for robust swarm exploration, coverage planning, task allocation, and communication-aware coordination. Each drone will use onboard LiDAR and camera sensors for mapping, navigation, obstacle avoidance, and target localization. The project will also involve designing a Ground Control Station (GCS)-based fusion framework to collect partial observations from multiple drones, merge duplicated target detections, maintain global search progress, and report final target positions. The candidate is expected to develop and test algorithms in simulation environments, integrate them with multi-drone planning and communication modules, and eventually deploy the system on real drone hardware. The work may include frontier-based exploration, multi-agent task allocation, semantic mapping, robust planning under communication loss, and learning-based decision-making for scalable swarm search.

Qualifications

•             Strong coding skills in Python and/or C++, with experience in ROS/ROS 2

•             Experience with robotics simulation platforms, such as Gazebo, Unity-based simulators, Isaac Sim/Lab, or similar environments

•             Knowledge of motion planning, mapping, SLAM, frontier-based exploration, or autonomous navigation

•             Experience with multi-agent systems, swarm robotics, task allocation, or distributed coordination is preferred

•             Experience with deep learning or reinforcement learning methods, such as PyTorch, multi-agent reinforcement learning, Transformers, or learning-based planning, is a plus

•             Familiarity with LiDAR/camera-based perception, occupancy mapping, semantic mapping, or object localization is preferred

•             Experience with aerial robots, PX4, MAVROS/MAVSDK, or real-world drone deployment is highly preferred

•             Ability to design robust systems under limited communication, onboard computation constraints, and possible agent failures

•             Good literature review, technical writing, and communication skills

•             Experience publishing papers and mentoring undergraduate or master’s students is preferred