Tumato 2.0 - a constraint-based planning approach for safe and robust robot behavior

Abstract

Ensuring the safe and effective operation of autonomous systems is a complex undertaking that inherently relies on underlying decision-making processes. To rigorously analyze these processes, formal verification methods, such as model checking, offer a valuable means. However, the non-deterministic nature of realistic environments makes these approaches challenging and often impractical. This work explores the capabilities of a constraint-based planning approach, Tumato, in generating policies that guide the system to predefined goals while adhering to safety constraints. Constraint-based planning approaches are inherently able to provide guarantees of soundness and completeness. Our primary contribution lies in extending Tumato’s capabilities to accommodate non-deterministic outcomes of actions, enhancing the robustness of the behavior. Originally designed to accommodate only deterministic outcomes, actions can now be modeled to include alternative outcomes to address contingencies explicitly. The adapted solver generates policies that enable reaching the goals in a safe manner, even when such alternative outcomes of actions occur. Additionally, we introduce a purely declarative manner for specifying safety in Tumato to further enhance its expressiveness as well as to reduce the susceptibility to errors during specification. The incorporation of cost or duration values to actions enables the solver to restore safety in the most preferred manner when necessary. Finally, we highlight the overlap of Tumato’s safety-related capabilities with a systems-theoretic approach, STPA (Systems-Theoretic Process Analysis). The aim is to emphasize the ability to avoid unsafe control actions without their explicit identification, contributing to a more comprehensive and holistic understanding of safety.