  This paper reports on developing an integrated framework for safety-aware
informative motion planning suitable for legged robots. The
information-gathering planner takes a dense stochastic map of the environment
into account, while safety constraints are enforced via Control Barrier
Functions (CBFs). The planner is based on the Incrementally-exploring
Information Gathering (IIG) algorithm and allows closed-loop kinodynamic node
expansion using a Model Predictive Control (MPC) formalism. Robotic exploration
and information gathering problems are inherently path-dependent problems. That
is, the information collected along a path depends on the state and observation
history. As such, motion planning solely based on a modular cost does not lead
to suitable plans for exploration. We propose SAFE-IIG, an integrated
informative motion planning algorithm that takes into account: 1) a robot's
perceptual field of view via a submodular information function computed over a
stochastic map of the environment, 2) a robot's dynamics and safety constraints
via discrete-time CBFs and MPC for closed-loop multi-horizon node expansions,
and 3) an automatic stopping criterion via setting an information-theoretic
planning horizon. The simulation results show that SAFE-IIG can plan a safe and
dynamically feasible path while exploring a dense map.
