  This paper studies the problem of risk-averse receding horizon motion
planning for agents with uncertain dynamics, in the presence of stochastic,
dynamic obstacles. We propose a model predictive control (MPC) scheme that
formulates the obstacle avoidance constraint using coherent risk measures. To
handle disturbances, or process noise, in the state dynamics, the state
constraints are tightened in a risk-aware manner to provide a disturbance
feedback policy. We also propose a waypoint following algorithm that uses the
proposed MPC scheme for discrete distributions and prove its risk-sensitive
recursive feasibility while guaranteeing finite-time task completion. We
further investigate some commonly used coherent risk metrics, namely,
conditional value-at-risk (CVaR), entropic value-at-risk (EVaR), and g-entropic
risk measures, and propose a tractable incorporation within MPC. We illustrate
our framework via simulation studies.
