Formalisms for Robotic Mission Specification and Execution: A Comparative Analysis

TL;DR

This paper systematically compares four formal methods—Behavior Trees, State Machines, Hierarchical Task Networks, and Business Process Model and Notation—for specifying complex robot missions, highlighting their strengths, limitations, and suitability for different robotic applications.

Contribution

It provides a comprehensive analysis of these formalisms focusing on mission-level descriptions, aiding practitioners in selecting appropriate approaches for robot mission design.

Findings

Behavior Trees offer high modularity and reactivity.

State Machines are well-understood but less scalable.

Hierarchical Task Networks excel in task decomposition.

Abstract

Robots are increasingly deployed across diverse domains and designed for multi-purpose operation. As robotic systems grow in complexity and operate in dynamic environments, the need for structured, expressive, and scalable mission-specification approaches becomes critical, with mission specifications often defined in the field by domain experts rather than robotics specialists. However, there is no standard or widely accepted formalism for specifying missions in single- or multi-robot systems. A variety of formalisms, such as Behavior Trees, State Machines, Hierarchical Task Networks, and Business Process Model and Notation, have been adopted in robotics to varying degrees, each providing different levels of abstraction, expressiveness, and support for integration with human workflows and external devices. This paper presents a systematic analysis of these four formalisms with respect to their suitability for robot mission specification. Our study focuses on mission-level descriptions rather than robot software development. We analyze their underlying control structures and mission concepts, evaluate their expressiveness and limitations in modeling real-world missions, and assess the extent of available tool support. By comparing the formalisms and validating our findings with experts, we provide insights into their applicability, strengths, and shortcomings in robotic system modeling. The results aim to support practitioners and researchers in selecting appropriate modeling approaches for designing robust and adaptable robot and multi-robot missions.