3 accepted papers
Certain forms of uncertainty that robotic systems encounter can be explicitly learned within the context of a known model, like parametric model uncertainties such as mass and moments of inertia. Quantifying such parametric uncertainty is important for more accurate prediction of the system behavior
Space free-flyers like the Astrobee robots currently operating aboard the International Space Station must operate with inherent system uncertainties. Parametric uncertainties like mass and moment of inertia are especially important to quantify in these safety-critical space systems and can change i…
This paper develops a decentralized approach for mapping and information-driven path planning for Very Large Scale Robotic (VLSR) systems. In this approach, obstacle mapping is performed using a continuous probabilistic representation known as a Hilbert map, which formulates the mapping problem as a…