Coral: A Unifying Abstraction Layer for Composable Robotics Software
Steven Swanbeck, Mitch Pryor
- 发表年份
- 2025
- 访问权限
- 开放获取
摘要
Despite the multitude of excellent software components and tools available in the robotics and broader software engineering communities, successful integration of software for robotic systems remains a time-consuming and challenging task for users of all knowledge and skill levels. And with robotics software often being built into tightly coupled, monolithic systems, even minor alterations to improve performance, adjust to changing task requirements, or deploy to new hardware can require significant engineering investment. To help solve this problem, this paper presents Coral, an abstraction layer for building, deploying, and coordinating independent software components that maximizes composability to allow for rapid system integration without modifying low-level code. Rather than replacing existing tools, Coral complements them by introducing a higher-level abstraction that constrains the integration process to semantically meaningful choices, reducing the configuration burden without limiting adaptability to diverse domains, systems, and tasks. We describe Coral in detail and demonstrate its utility in integrating software for scenarios of increasing complexity, including LiDAR-based SLAM and multi-robot corrosion mitigation tasks. By enabling practical composability in robotics software, Coral offers a scalable solution to a broad range of robotics system integration challenges, improving component reusability, system reconfigurability, and accessibility to both expert and non-expert users. We release Coral open source.
关键词
相关论文
基于嵌入式语言模型的多机器人系统动态重构
Shokhikha Amalana Murdivien, Jongsu Park, Jumyung Um
Robotics and Computer-Integrated Manufacturing · 2026
基于大语言模型增强的多智能体强化学习的无人机博弈分层决策
Xinyu Dong, Bo Li, Guangyu Zhang 等 5 位作者
Aerospace Science and Technology · 2026
水下残骸区域多UUV协同覆盖搜索的编队优化与避碰决策方法
Haomiao Yu, Zeyuan Zhang, Yantian Ma
Robotics and Autonomous Systems · 2026
人在回路中的群体机器人:一种用于真实土壤测绘的仿生群体方法
Petras Swissler, Mohammadali Rashidioun, Nicholas Sahu 等 6 位作者
2026