Perception-Informed Autonomous Environment Augmentation with Modular Robots

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.

Authors

• Tarik Tosun
• Jonathan Daudelin
• Gangyuan Jing
• Hadas Kress-Gazit
• Mark E. Campbell
• Mark Yim

Keywords

• Task analysis
• Hardware
• Mobile robots
• Bridges
• Buildings
• Planning
• Modular Robots
• Environmental Characteristics
• Robotic System
• Building Structures
• Modular System
• High-level Tasks
• Large Obstacles
• Hardware Experiments
• High-level Planner
• Autonomic System
• Task Completion
• Grid Cells
• Likelihood Function
• Current Environment
• State Machine
• Construction Of Structures
• Mobile Robot
• Total Probability
• Passive Elements
• Simultaneous Localization And Mapping
• Block Module
• Robot Operating System
• Template Feature
• Robot Configuration
• Sensor Module
• Structure Library
• Passive Structures
• Unstructured Environments
• Robot Behavior
• Manipulation Tasks