SEAMLiS: Visibility-Aware Safety for Perception-Limited Multi-Robot Exploration

2026-07-10

AI Summary

This work addresses the challenge of safe autonomous multi-robot exploration under perceptual constraints—specifically limited range and field of view—where conventional decoupled planning and obstacle avoidance often incur collision risks due to delayed obstacle detection. To mitigate this, the authors propose SEAMLiS, a modular safety framework at the execution layer that preserves upstream exploration policies while dynamically switching between a visibility-enhancing yaw maneuver and a velocity-tracking fallback strategy via a gated pose filter. Integrated with a control barrier function (CBF)-based position filter, SEAMLiS holistically handles avoidance of known obstacles, newly detected obstacles, and other robots. Implemented in a decentralized architecture, the approach achieves collision-free exploration across simulation, Isaac Sim, and physical Crazyflie platforms, demonstrating a balanced trade-off between safety and exploration efficiency in both single- and multi-robot settings.

Abstract

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.

Problem

• multi-robot exploration
• limited sensing
• safety
• perception-aware
• collision avoidance

Innovation

• visibility-aware safety
• Control Barrier Function
• modular safety framework
• multi-robot exploration
• perception-limited autonomy