SayCoNav：利用大语言模型实现去中心化多机器人导航中的自适应协作
SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation
AI总结：
针对多机器人在未知环境中导航的自适应协作需求，提出SayCoNav方法，借助大语言模型生成协作策略与去中心化规划，经MultiON任务验证，最多可提升异构机器人搜索效率44.28%，且能动态适配任务变化。
摘要：
自适应协作对于自主机器人团队在大规模未知环境中执行复杂导航任务至关重要。有效的协作策略需要根据各机器人的技能与当前状态进行确定和调整，以顺利达成共同目标。我们提出了SayCoNav，这是一种利用大语言模型（LLMs）自动生成机器人团队间协作策略的新方法。在该协作策略的基础上，每个机器人借助LLM以去中心化的方式生成自身的规划与动作。导航过程中机器人彼此共享信息，每个机器人也会据此持续更新其分步规划。我们在多目标导航（Multi-Object Navigation, MultiON）任务上对SayCoNav进行评估，这类任务要求机器人团队利用自身互补优势，在未知环境中高效搜索多个不同物体。通过在不同团队构成与条件下将SayCoNav与基线方法对比验证，实验结果表明，SayCoNav通过异构机器人之间的有效协作，最多可将搜索效率提升44.28%，同时还能在任务执行过程中动态适应变化的条件。
英文摘要：
Adaptive collaboration is critical to a team of autonomous robots to perform complicated navigation tasks in large-scale unknown environments. An effective collaboration strategy should be determined and adapted according to each robot's skills and current status to successfully achieve the shared goal. We present SayCoNav, a new approach that leverages large language models (LLMs) for automatically generating this collaboration strategy among a team of robots. Building on the collaboration strategy, each robot uses the LLM to generate its plans and actions in a decentralized way. By sharing information to each other during navigation, each robot also continuously updates its step-by-step plans accordingly. We evaluate SayCoNav on Multi-Object Navigation (MultiON) tasks, that require the team of the robots to utilize their complementary strengths to efficiently search multiple different objects in unknown environments. By validating SayCoNav with varied team compositions and conditions against baseline methods, our experimental results show that SayCoNav can improve search efficiency by at most 44.28% through effective collaboration among heterogeneous robots. It can also dynamically adapt to the changing conditions during task execution.
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。
