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摘要: 飞滚多模态球形机器人(飞滚机器人, Flying-Rolling Robot, FRR)兼具空中飞行与地面滚动的多模态移动能力, 在搜索救援和巡检侦察等任务中展现出巨大潜力, 然而其在室内环境下的自主导航仍面临环境建模复杂和路径规划效率不足的挑战. 自然界生物普遍通过环境简化与能效权衡以实现高效的空间位移, 因此本文受到生物启发提出一种基于分层栅格地图的飞滚多模态路径规划方法. 首先, 构建由建筑结构层与障碍物层组成的分层栅格地图, 以实现对室内环境关键要素的高效表征. 其次, 设计改进的Jump A*算法, 在建筑结构层采用跳点搜索规划地面滚动路径, 在障碍物层采用A*搜索规划空中飞行路径, 并在代价函数中引入能量损耗项, 通过可调权重实现移动距离与能量消耗的平衡. 实验结果表明, 该方法能够有效构建室内环境的分层栅格地图, 并可在该地图上根据不同的距离和能耗目标进行多模态路径规划, 为FRR在复杂室内场景下的自主导航提供了可行方案.Abstract: The flying-rolling multimodal spherical robot (Flying-Rolling Robot, FRR) combines aerial flight and ground rolling capabilities, showing strong potential in search and rescue and inspection tasks. However, autonomous navigation in indoor environments remains challenging due to the complexity of environment modeling and insufficient path-planning efficiency. Inspired by biological systems that achieve efficient locomotion through environment simplification and energy trade-offs, this paper proposes a multimodal path planning method for FRRs based on a hierarchical grid map. A hierarchical grid map composed of a building-structure layer and an obstacle layer is constructed to represent key elements of indoor environments. An improved Jump A* algorithm is then designed, where jump point search is used to plan ground rolling paths on the building-structure layer, while A* search is applied to plan aerial flight paths on the obstacle layer. An energy consumption term is incorporated into the cost function to balance travel distance and energy usage. Experimental results demonstrate that the proposed method can effectively support multimodal path planning in complex indoor environments.
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Key words:
- Flying-rolling robot /
- layered grid map /
- multimodal path planning /
- jump point search /
- A* search
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表 1 改进JA*算法参数
Table 1 Parameters of the improved JA* algorithm
参数 值 $m$ 6 kg $g$ 9.81$\mathrm{m/s^2}$ $P_{\text{hover}}$ 800 W $v$ 1 m/s $\alpha$ 1 $\beta$ 1 
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表 2 路径规划实验结果
Table 2 Path planning experimental results
序号 $\alpha$ $\beta$ 飞/滚距离(m) 飞滚切换次数 能量消耗(J) 1 1.0 1.0 44.0/21.8 8 35069 2 1.0 2.5 59.8/9.1 2 31216 3 1.0 5.0 68.9/0.0 0 27588
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