Robotic systems such as Unmanned Ground Vehicles (UGVs) are often used in hazardous environments. Thus, their perception, localisation and path planning must be robust to the wide range of environmental conditions encountered in the field. This includes adverse weather, ambiguous semi-traversable obstacles and terrain that is dangerous when approached at certain angles and speeds.

This project aims to apply advances in computer vision, machine learning, and motion planning to formulate solutions to these problems that are robust to the inherent uncertainty of our operating environments and yet efficient enough to run in real time.

Requirements

  • Strong programming skills and experience with C++
  • Strong grasp of algorithms and data structures
  • Familiarity with linear algebra
  • Prior experience in robotics a plus​
  • Singapore citizens only
  • Not a recipient of a scholarship with a specified bond obligation
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