  This paper proposes a formal robot motion risk reasoning framework and
develops a risk-aware path planner that minimizes the proposed risk. While
robots locomoting in unstructured or confined environments face a variety of
risk, existing risk only focuses on collision with obstacles. Such risk is
currently only addressed in ad hoc manners. Without a formal definition,
ill-supported properties, e.g. additive or Markovian, are simply assumed.
Relied on an incomplete and inaccurate representation of risk, risk-aware
planners use ad hoc risk functions or chance constraints to minimize risk. The
former inevitably has low fidelity when modeling risk, while the latter
conservatively generates feasible path within a probability bound. Using
propositional logic and probability theory, the proposed motion risk reasoning
framework is formal. Building upon a universe of risk elements of interest,
three major risk categories, i.e. locale-, action-, and traverse-dependent, are
introduced. A risk-aware planner is also developed to plan minimum risk path
based on the newly proposed risk framework. Results of the risk reasoning and
planning are validated in physical experiments in real-world unstructured or
confined environments. With the proposed fundamental risk reasoning framework,
safety of robot locomotion could be explicitly reasoned, quantified, and
compared. The risk-aware planner finds safe path in terms of the newly proposed
risk framework and enables more risk-aware robot behavior in unstructured or
confined environments.
