Accomplishing High-Level Tasks with Modular Robots
Gangyuan Jing, Tarik Tosun, Mark Yim, and Hadas Kress-Gazit

TL;DR
This paper presents an integrated system for high-level task execution using modular robots, combining planning, design, simulation, and hardware, with adaptive behaviors for complex multi-part tasks.
Contribution
It introduces an extended system that includes environmentally adaptive parametric behaviors, enhancing modular robot capabilities for challenging tasks.
Findings
Successfully accomplished multi-part tasks in hardware experiments.
Demonstrated the effectiveness of adaptive behaviors in modular robots.
Extended previous systems with integrated motion planning and feedback control.
Abstract
The advantage of modular self-reconfigurable robot systems is their flexibility, but this advantage can only be realized if appropriate configurations (shapes) and behaviors (controlling programs) can be selected for a given task. In this paper, we present an integrated system for addressing high-level tasks with modular robots, and demonstrate that it is capable of accomplishing challenging, multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) A high-level mission planner, (2) A large design library spanning a wide set of functionality, (3) A design and simulation tool for populating the library with new configurations and behaviors, and (4) modular robot hardware. This paper builds on earlier work by the authors, extending the original system to include environmentally adaptive parametric behaviors, which integrate motion planners…
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