AI 中文总结
针对感知受限的多机器人探索，提出SEAMLiS安全框架，保留上游探索堆栈，通过感知感知姿态和位置过滤器加强安全，经多种实验验证，可实现无碰撞探索并保留可见性偏航控制效率。
AI 中文摘要
未知环境中的自主探索通常由信息前沿、视点或轨迹驱动，而局部安全控制器避免当前地图中表示的障碍物。在有限传感范围和有限视野下，这种分离可能不安全。本文提出了SEAMLiS，一种用于分散式多机器人探索的模块化执行层安全框架。它保留上游探索堆栈，通过感知感知姿态和位置过滤器在执行层加强安全。基于守门人的姿态过滤器在促进可见性的偏航策略和速度跟踪备份策略之间切换，基于控制障碍函数的位置过滤器避免已知、新检测到的障碍物和其他机器人。通过随机模拟、Isaac Sim和Crazyflie硬件实验验证了该框架。结果表明在测试的单机器人和多机器人设置中可无碰撞探索，同时保留了促进可见性偏航控制的大部分效率。
英文摘要
Autonomous exploration in unknown environments is typically driven by informative frontiers, viewpoints, or trajectories, while local safety controllers avoid obstacles represented in the current map. Under finite sensing range and limited field of view, this separation can be unsafe: an exploration stack may plan optimistically through unobserved space and steer the sensor toward information gain rather than along the direction of motion, causing hidden obstacles to be detected too late for bounded-actuation avoidance. This paper presents SEAMLiS (Safe Exploration for Autonomous Multi-Robot Systems Under Limited Sensing), a modular execution-layer safety framework for decentralized multi-robot exploration. SEAMLiS preserves the upstream exploration stack, including the goal allocator and local planner, and enforces safety at the execution layer through perception-aware attitude and positional filters. A gatekeeper-based attitude filter switches between a visibility-promoting yaw policy and a velocity-tracking backup policy to preserve visibility of the critical known-free/unknown boundary with sufficient braking margin. A Control Barrier Function (CBF)-based positional filter then avoids known obstacles, newly detected obstacles, and other robots. We provide sufficient collision-avoidance conditions and validate the framework in randomized simulation, Isaac Sim, and Crazyflie hardware experiments. Results show collision-free exploration across tested single- and multi-robot settings while retaining much of the efficiency of visibility-promoting yaw control.
CommentsProject page: https://www.taekyung.me/seamlis
