  Simultaneous Localization and Planning (SLAP) under process and measurement
uncertainties is a challenge. It involves solving a stochastic control problem
modeled as a Partially Observed Markov Decision Process (POMDP) in a general
framework. For a convex environment, we propose an optimization-based open-loop
optimal control problem coupled with receding horizon control strategy to plan
for high quality trajectories along which the uncertainty of the state
localization is reduced while the system reaches to a goal state with minimum
control effort. In a static environment with non-convex state constraints, the
optimization is modified by defining barrier functions to obtain collision-free
paths while maintaining the previous goals. By initializing the optimization
with trajectories in different homotopy classes and comparing the resultant
costs, we improve the quality of the solution in the presence of action and
measurement uncertainties. In dynamic environments with time-varying
constraints such as moving obstacles or banned areas, the approach is extended
to find collision-free trajectories. In this paper, the underlying spaces are
continuous, and beliefs are non-Gaussian. Without obstacles, the optimization
is a globally convex problem, while in the presence of obstacles it becomes
locally convex. We demonstrate the performance of the method on different
scenarios.

Robotics