Robust range-based localization and motion planning under uncertainty using ultra-wideband radio
Samuel Prentice · DSpace@MIT (Massachusetts Institute of Technology) · 2007
The work presented in this thesis addresses two problems: accurately localizing a mo-bile robot using ultra-wideband (UWB) radio signals in GPS-denied environments; and planning robot trajectories that incorporate belief uncertainty using probabilistic state es-timates. Addressing the former, we improve upon traditional approaches to range-based localization by capturing non-linear sensor dynamics using a Monte Carlo method for hid-den bias estimation. For the latter, we overcome current limitations of scalable belief space planning by adapting the Probabilistic Roadmap algorithm to enable trajectory search in belief space for minimal uncertainty paths. We contribute a novel solution motivated by