I am currently working on state estimation/navigation for a system with multiple robots. As of now, what I have is each robot localizing itself with a Kalman filter, given vision based measurements. As next steps, I am aiming to do two things:
- Extend this filtering framework to span over all robots so that they can cooperate and improve each other's localization
- Along with the above, construct a path planning framework such that they can navigate in a way that their localization accuracy is always maximized, thereby eliminating the problem of losing position etc.
To this end, I've been reading about multi-robot state estimation and planning strategies, and have come across belief space planning: or planning under uncertainty. While the math intuitively makes sense, I am having issues with how to implement these techniques in my real world scenario, especially for multiple robots. I have experience using algorithms such as EKF, UKF etc., and sampling based planning strategies like PRM/RRT, but I am having trouble with the probabilistic link between these two.
So far, I've been looking into research papers, but as someone who's mainly a programmer, I'm trying find something more approachable that will help me link the (somewhat abstract) math to my specific problem: for instance, helping me define terms such as 'joint belief of the entire group', using the data I have in hand. What are my best options, and are there any better resources I can consult?