Perception-Informed Autonomous Environment Augmentation with Modular Robots
Perception-Informed Autonomous Environment Augmentation with Modular Robots
复制标题
使用模块化机器人增强感知信息的自主环境
DOI：
10.1109/icra.2018.8463155
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Mark H. Yim
中科院分区：
文献类型：
--
作者：
Tarik Tosun;Jonathan A. Daudelin;Gangyuan Jing;H. Kress;M. Campbell;Mark H. Yim
我们提出了一个系统，使模块化机器人自主构建结构，以完成高层次的任务。建筑结构允许机器人克服大型障碍，扩大它可以执行的任务。这解决了模块化机器人系统的一个共同弱点，即通常难以穿越大型障碍物。本文介绍了硬件，感知和规划工具，包括我们的系统。环境表征算法识别环境中的特征，所述特征可以被增强以在环境的两个断开区域之间创建路径。特别设计的积木使机器人能够创建可以增强环境的结构，使障碍物可以穿越。一个高层次的规划者对任务、机器人运动能力和环境进行推理，以决定是否以及在哪里增强环境，以执行所需的任务。我们验证了我们的系统在硬件实验。
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.