轻量化双阶段框架提升空地机器人导航效率
A Two-Stage Lightweight Framework for Efficient Land-Air Bimodal Robot Autonomous Navigation
- 先预测全局关键点，再局部优化路径，降低计算负担
- 参数减少14%，空地转换能耗降35%，支持无GPU实时运行
- 适合需高效迁移的机器人导航场景，尤其关注能效与部署
空地双模态机器人（LABR）因其兼具飞行器高机动性与地面车辆长续航优势，成为自主导航研究热点。然而现有方法受限于基于地图的路径规划效率低下，或基于学习的方法计算开销过大。为此，本文提出一种双阶段轻量化框架：第一阶段通过全局关键点预测网络（GKPN）生成空地混合关键点路径，其中包含用于增强障碍物检测的Sobel感知网络（SPN）和利用上下文信息提升预测能力的轻量注意力规划网络（LAPN）；第二阶段基于预测的关键点分割全局路径，并采用基于地图的规划器进行局部优化，生成平滑且避障的轨迹。在自建LABR平台上实验表明，该框架相比现有方法减少14%网络参数，空地转换能耗降低35%，并可在无GPU条件下实现实时导航，支持从仿真到现实的零样本迁移。
原文摘要 · Abstract (English)
Land-air bimodal robots (LABR) are gaining attention for autonomous navigation, combining high mobility from aerial vehicles with long endurance from ground vehicles. However, existing LABR navigation methods are limited by suboptimal trajectories from mapping-based approaches and the excessive computational demands of learning-based methods. To address this, we propose a two-stage lightweight framework that integrates global key points prediction with local trajectory refinement to generate efficient and reachable trajectories. In the first stage, the Global Key points Prediction Network (GKPN) was used to generate a hybrid land-air keypoint path. The GKPN includes a Sobel Perception Network (SPN) for improved obstacle detection and a Lightweight Attention Planning Network (LAPN) to improves predictive ability by capturing contextual information. In the second stage, the global path is segmented based on predicted key points and refined using a mapping-based planner to create smooth, collision-free trajectories. Experiments conducted on our LABR platform show that our framework reduces network parameters by 14\% and energy consumption during land-air transitions by 35\% compared to existing approaches. The framework achieves real-time navigation without GPU acceleration and enables zero-shot transfer from simulation to reality during
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