Quadruped robots have the distinct ability to adapt their body and step height to navigate through cluttered environments. Nonetheless, for these robots to utilize their full potential in real-world scenarios, they require awareness of their environment and obstacle geometry. We propose a novel multi-agent robotic system that incorporates cutting-edge technologies. The proposed solution features a 3D neural reconstruction algorithm that enables navigation of a quadruped robot in both static and semi-static environments. The prior areas of the environment are also segmented according to the quadruped robots' abilities to pass them. Moreover, we have developed an adaptive neural field optimal motion planner (ANFOMP) that considers both collision probability and obstacle height in 2D space.Our new navigation and mapping approach enables quadruped robots to adjust their height and behavior to navigate under arches and push through obstacles with smaller dimensions. The multi-agent mapping operation has proven to be highly accurate, with an obstacle reconstruction precision of 82%. Moreover, the quadruped robot can navigate with 3D obstacle information and the ANFOMP system, resulting in a 33.3% reduction in path length and a 70% reduction in navigation time.

翻译：四足机器人具有独特的身体与步高自适应能力，可在杂乱环境中灵活穿行。然而，为在真实场景中充分发挥其潜力，此类机器人需具备环境感知与障碍物几何认知能力。我们提出一种融合前沿技术的多智能体机器人系统。该方案采用三维神经重建算法，使四足机器人能够在静态与半静态环境中实现导航，并根据四足机器人通行能力对环境的先验区域进行分割。此外，我们开发了自适应神经场最优运动规划器（ANFOMP），该规划器同时考虑二维空间中的碰撞概率与障碍物高度。这一新型导航与建图方法使四足机器人能够调整自身高度与行为，实现拱形结构下的穿行及小尺寸障碍物的推挤通行。多智能体建图操作展现出极高精度，障碍物重建准确率达82%。实验表明，结合三维障碍物信息与ANFOMP系统，四足机器人路径长度缩减33.3%，导航时间缩短70%。