Robot Risk-Awareness by Formal Risk Reasoning and Planning

by   Xuesu Xiao, et al.
Texas A&M University

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.


page 1

page 5

page 7


Robot Motion Risk Reasoning Framework

This paper presents a formal and comprehensive reasoning framework for r...

Explicit-risk-aware Path Planning with Reward Maximization

This paper develops a path planner that minimizes risk (e.g. motion exec...

Explicit Motion Risk Representation

This paper presents a formal definition and explicit representation of r...

DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds

Planning in unstructured environments is challenging – it relies on sens...

Tethered Aerial Visual Assistance

In this paper, an autonomous tethered Unmanned Aerial Vehicle (UAV) is d...

Lambda-Field: A Continuous Counterpart Of The Bayesian Occupancy Grid For Risk Assessment And Safe Navigation

In the context of autonomous robots, one of the most important tasks is ...

Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map

Motion planning in off-road environments requires reasoning about both t...

Please sign up or login with your details

Forgot password? Click here to reset