In the hospital environment it is important for a robot to be able to autonomously navigate within a cluttered environment. This includes navigating around hospital beds, supply trays and other objects that are frequently found in hospitals. Because also many people move about (patients, children, nurses, doctors, guests), the hospital environment is a challenging environment for a robot to autonomously navigate and work in. For this reason, during robot autonomy developments, a realistic hospital ward simulation environment in Gazeebo would be extremely useful and provide benchmark evaluation potential during working developments. The current assignment involves the creation of such a simulation testing environment within Gazeebo.
The output of the project will be:
- A list of obstacles of importance that need to be accounted for during autonomy developments
- A Gazeebo simulation of a KKH ward
- A benchmark evaluation of the simulation environment with respect to the actual environment
- Excitement about new technologies, eager to work on state-of-the-art technologies;
- Independence, research appetite, enthusiastic, self-critical;
- Sense of responsibility, duty fulfillment;
- Eager to work in an international environment, team-player, collaborate with multiple institutions and companies, determined to dedicate time in the project, able to set priorities;
- Basic understanding of math and programming (C++, Python, Matlab);
- Familiarity with the Robot Operating System (ROS).
Nice to have:
- Basic understanding of machine and deep learning, neural networks, optimization techniques (gradient descent, mini-batch gradient descent);
- Basic understanding and/or experience with computer vision techniques (object recognition, feature detection, image classification etc.);
- Experience with deep learning frameworks (Caffe, CMTK, Tensorflow, Pytorch, Theano, etc.) would be highly appreciated;
- Hardware and software skills (experience with NVIDIA Cuda, Jetson, etc.).