动态环境中基于贝叶斯BIM引导与NLP安全提示的建筑机器人导航
Bayesian BIM-Guided Construction Robot Navigation with NLP Safety Prompts in Dynamic Environments
AI总结：
本文提出一种基于贝叶斯BIM引导与NLP情感分析的概率框架，通过动态调整势场实现建筑机器人的安全自适应路径规划，在优先考虑安全时使与障碍物最小距离提升50%。
摘要：
建筑机器人日益依赖自然语言处理来执行任务，这产生了在复杂、动态环境中解释命令的鲁棒方法的需求。现有研究主要关注机器人应执行什么任务，而较少关注如何安全高效地执行这些任务。本文提出了一种新颖的概率框架，利用自然语言命令的情感分析来动态调整建筑环境中的机器人导航策略。该框架利用Building Information Modeling (BIM)数据和自然语言提示创建自适应导航策略，以应对不同程度的环境风险和不确定性。我们引入了一种物体感知的路径规划方法，将指数势场与环境的网格表示相结合，其中势场根据用户提示的语义分析进行动态调整。该框架采用贝叶斯推理整合多个信息源：来自BIM的静态数据、自然语言命令的语义内容，以及用户提示中隐含的安全约束。我们通过比较三种场景的实验验证了该方法：基线最短路径规划、面向安全的导航和风险感知路由。结果表明，该方法成功根据自然语言情感调整路径规划，在优先考虑安全时，与障碍物的最小距离实现了50%的改善，同时保持了合理的路径长度。具有对比提示的场景，如“危险”和“安全”，证明了该框架修改路径的能力。该方法为将人类知识和安全考量整合到建筑机器人导航中提供了灵活的基础。
英文摘要：
Construction robotics increasingly relies on natural language processing for task execution, creating a need for robust methods to interpret commands in complex, dynamic environments. While existing research primarily focuses on what tasks robots should perform, less attention has been paid to how these tasks should be executed safely and efficiently. This paper presents a novel probabilistic framework that uses sentiment analysis from natural language commands to dynamically adjust robot navigation policies in construction environments. The framework leverages Building Information Modeling (BIM) data and natural language prompts to create adaptive navigation strategies that account for varying levels of environmental risk and uncertainty. We introduce an object-aware path planning approach that combines exponential potential fields with a grid-based representation of the environment, where the potential fields are dynamically adjusted based on the semantic analysis of user prompts. The framework employs Bayesian inference to consolidate multiple information sources: the static data from BIM, the semantic content of natural language commands, and the implied safety constraints from user prompts. We demonstrate our approach through experiments comparing three scenarios: baseline shortest-path planning, safety-oriented navigation, and risk-aware routing. Results show that our method successfully adapts path planning based on natural language sentiment, achieving a 50\% improvement in minimum distance to obstacles when safety is prioritized, while maintaining reasonable path lengths. Scenarios with contrasting prompts, such as "dangerous" and "safe", demonstrate the framework's ability to modify paths. This approach provides a flexible foundation for integrating human knowledge and safety considerations into construction robot navigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
