Perception-Informed Autonomous Environment Augmentation With Modular Robots

TL;DR

This paper introduces a perception-informed modular robot system capable of autonomously building structures to augment environments, enabling traversal of large obstacles and expanding task capabilities.

Contribution

It presents a novel integrated system combining environment perception, planning, and modular construction for autonomous environment augmentation.

Findings

Successfully built structures to traverse obstacles in hardware experiments

Environment characterization algorithm effectively identifies augmentation points

High-level planning enables task-specific environment modifications

Abstract

We present a system enabling a modular robot to autonomously build structures in order to accomplish high-level tasks. Building structures allows the robot to surmount large obstacles, expanding the set of tasks it can perform. This addresses a common weakness of modular robot systems, which often struggle to traverse large obstacles. This paper presents the hardware, perception, and planning tools that comprise our system. An environment characterization algorithm identifies features in the environment that can be augmented to create a path between two disconnected regions of the environment. Specially-designed building blocks enable the robot to create structures that can augment the environment to make obstacles traversable. A high-level planner reasons about the task, robot locomotion capabilities, and environment to decide if and where to augment the environment in order to perform the desired task. We validate our system in hardware experiments