GSoC 2021 Project Idea 6.4: Open-source biorobotics framework

[alexdewar]

Hi @nitik1998

@nitik1998

• It would be great if you could let me know whether, initially in Gazebo will I have to work with sensors like IMUs, GPS etc., or should I focus on mostly visual-based sensors?
• The Path Integration part was common in all the papers, and I was wondering about the possibility of improving that by fusing different sensors?
• You have mentioned simple and biological sensors, but I could not find a classification somewhere along these lines in the literature, hence, it would be great if you could elaborate a bit about your idea of this classification backed with examples

In answer to your questions:

1. I think the initial goal should indeed be to get things working with more conventional sensors, rather than any in-house sensor, just so we have a proof of concept. If you could fuse e.g. GPS and IMU inputs that would be fantastic – apart from anything we do use these sensors anyway so it could be directly useful for something – but I think it would also be ok as a very first step to use whatever simulated sensors best demonstrate the process (e.g. some kind of idealised simulated sensor).
2. Path integration is indeed super important to the kinds of insects we study (e.g. desert ants) and fusing it with visual input would be an eminently sensible thing to do and very relevant to the biological system that we’re interested (hence why this was what the authors looked at in the Webb paper I sent you). It might be good to list it as a goal in the proposal for this reason; it’s an interesting problem for both robotics and biology. The only question would be how we should frame it. There are some cool biological models of path integration (see e.g. our implementation of someone else’s here: bob_robotics/projects/stone_cx at master · BrainsOnBoard/bob_robotics · GitHub), but it might be hard to figure out the most biologically accurate way of doing this. Perhaps one goal could be to fuse the input from a visual navigation algorithm with that from an idealised pseudo-path integration system (e.g. using an IMU + wheel encoders)? Then another goal could be to subsequently substitute this for a more biologically realistic model, such as the Stone CX model in the link above.
3. I probably should have elaborated more on what I meant by “simple” and “biological” sensors. An example would be what I mentioned in my answer to Q2 (i.e. using a neural model vs IMU + wheel encoders). This project is technically an engineering project so we are focused on making things actually work rather than purely doing theoretical things, but we obviously have a particular emphasis on using biological models as a starting point, so that’s where our research tends to lie. That said, you’re always making some assumptions when modelling a biological system and we do want things to work so there is always a trade-off between biological realism and practical considerations. Does that help, or have I just made things more confusing? If so, then don’t worry; we can always talk about this kind of thing further down the line when it comes to it.