GoalSwarm：多无人机语义协调用于开放词汇物体导航
GoalSwarm: Multi-UAV Semantic Coordination for Open-Vocabulary Object Navigation
AI总结：
本文提出GoalSwarm框架，通过轻量语义地图构建、零样本目标检测和去中心化协调策略，实现多无人机在未知环境中的开放词汇物体导航。
摘要：
本文提出GoalSwarm框架，通过轻量语义地图构建、零样本目标检测和去中心化协调策略，实现多无人机在未知环境中的开放词汇物体导航。
英文摘要：
Cooperative visual semantic navigation is a foundational capability for aerial robot teams operating in unknown environments. However, achieving robust open-vocabulary object-goal navigation remains challenging due to the computational constraints of deploying heavy perception models onboard and the complexity of decentralized multi-agent coordination. We present GoalSwarm, a fully decentralized multi-UAV framework for zero-shot semantic object-goal navigation. Each UAV collaboratively constructs a shared, lightweight 2D top-down semantic occupancy map by projecting depth observations from aerial vantage points, eliminating the computational burden of full 3D representations while preserving essential geometric and semantic structure. The core contributions of GoalSwarm are threefold: (1) integration of zero-shot foundation model -- SAM3 for open vocabulary detection and pixel-level segmentation, enabling open-vocabulary target identification without task-specific training; (2) a Bayesian Value Map that fuses multi-viewpoint detection confidences into a per-pixel goal-relevance distribution, enabling informed frontier scoring via Upper Confidence Bound (UCB) exploration; and (3) a decentralized coordination strategy combining semantic frontier extraction, cost-utility bidding with geodesic path costs, and spatial separation penalties to minimize redundant exploration across the swarm.
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。
