TaBSA -- A framework for training and benchmarking algorithms scheduling tasks for mobile robots working in dynamic environments
Wojciech Dudek, Daniel Giełdowski, Dominik Belter, Kamil Młodzikowski, Tomasz Winiarski
TL;DR
TaBSA presents a configurable, open-source framework for training and benchmarking robot task scheduling algorithms in dynamic, uncertain environments. It combines a SysML-based DSL with ROS integration to model scenarios, tasks, and plugins, enabling fair, reproducible comparisons across classical and AI-based decision agents. The work demonstrates both diagnostic and evaluative capabilities, showing how the framework can reveal algorithmic strengths, weaknesses, and implementation flaws under controlled, real-world-inspired conditions. The approach supports rapid tuning and selection of scheduling strategies for mobile robots, with practical impact for researchers and integrators in роботics deployment.
Abstract
This article introduces a software framework for benchmarking robot task scheduling algorithms in dynamic and uncertain service environments. The system provides standardized interfaces, configurable scenarios with movable objects, human agents, tools for automated test generation, and performance evaluation. It supports both classical and AI-based methods, enabling repeatable, comparable assessments across diverse tasks and configurations. The framework facilitates diagnosis of algorithm behavior, identification of implementation flaws, and selection or tuning of strategies for specific applications. It includes a SysML-based domain-specific language for structured scenario modeling and integrates with the ROS-based system for runtime execution. Validated on patrol, fall assistance, and pick-and-place tasks, the open-source framework is suited for researchers and integrators developing and testing scheduling algorithms under real-world-inspired conditions.