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This paper proposes a formal robot motion risk reasoning framework anddevelops a risk-aware path planner that minimizes the proposed risk. Whilerobots locomoting in unstructured or confined environments face a variety ofrisk, existing risk only focuses on collision with obstacles. Such risk iscurrently 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-awareplanners use ad hoc risk functions or chance constraints to minimize risk. Theformer inevitably has low fidelity when modeling risk, while the latterconservatively generates feasible path within a probability bound. Usingpropositional logic and probability theory, the proposed motion risk reasoningframework is formal. Building upon a universe of risk elements of interest,three major risk categories, i.e. locale-, action-, and traverse-dependent, areintroduced. A risk-aware planner is also developed to plan minimum risk pathbased on the newly proposed risk framework. Results of the risk reasoning andplanning are validated in physical experiments in real-world unstructured orconfined environments. With the proposed fundamental risk reasoning framework,safety of robot locomotion could be explicitly reasoned, quantified, andcompared. The risk-aware planner finds safe path in terms of the newly proposedrisk framework and enables more risk-aware robot behavior in unstructured orconfined environments.

Robot Risk-Awareness by Formal Risk Reasoning and Planning