两机器人通过连接长度调节，实现复杂地形集体通行。
Multi-robot connective collaboration toward collective obstacle field traversal
- 两机器人可物理连接，通过调节连接长度适应地形变化。
- 连接长度在0.86~0.9倍体长时通行最稳定，其他区间效率低。
- 基于势能景观的机制解释，适用于小型多机器人协同设计。
地形高差超过单个机器人腿长时，足式机器人行走面临巨大挑战。受火蚁群体组装行为启发，研究了两个‘可连接’机器人如何协同穿越高度变化大于腿长的崎岖地形。每台机器人结构极简，为立方体躯干，由一个旋转电机驱动四根成对竖直桩腿。两台或多台机器人可物理连接以增强集体移动能力。实验中，两机器人组在充满均匀分布半球形‘巨石’的障碍场中行进。实测速度显示，连接长度C在[0.86, 0.9]倍机器人单位体长（UBL）范围内时，系统可实现可持续移动；而连接长度在[0.63, 0.84]和[0.92, 1.1] UBL范围时，通行能力显著下降。基于势能景观的模型揭示了连接长度通过调控系统势能景观影响集体移动性的机制，并指导系统根据障碍场空间频率动态调整连接长度。结果表明，通过改变机器人间的连接配置，该双机器人系统可利用机械智能，更好地利用障碍物交互力，提升运动性能。未来工作将探索通用的机器人-环境耦合原则，指导大量小型机器人实现类蚂蚁的集体环境协商。
原文摘要 · Abstract (English)
Environments with large terrain height variations present great challenges for legged robot locomotion. Drawing inspiration from fire ants' collective assembly behavior, we study strategies that can enable two ``connectable'' robots to collectively navigate over bumpy terrains with height variations larger than robot leg length. Each robot was designed to be extremely simple, with a cubical body and one rotary motor actuating four vertical peg legs that move in pairs. Two or more robots could physically connect to one another to enhance collective mobility. We performed locomotion experiments with a two-robot group, across an obstacle field filled with uniformly-distributed semi-spherical ``boulders''. Experimentally-measured robot speed suggested that the connection length between the robots has a significant effect on collective mobility: connection length C in [0.86, 0.9] robot unit body length (UBL) were able to produce sustainable movements across the obstacle field, whereas connection length C in [0.63, 0.84] and [0.92, 1.1] UBL resulted in low traversability. An energy landscape based model revealed the underlying mechanism of how connection length modulated collective mobility through the system's potential energy landscape, and informed adaptation strategies for the two-robot system to adapt their connection length for traversing obstacle fields with varying spatial frequencies. Our results demonstrated that by varying the connection configuration between the robots, the two-robot system could leverage mechanical intelligence to better utilize obstacle interaction forces and produce improved locomotion. Going forward, we envision that generalized principles of robot-environment coupling can inform design and control strategies for a large group of small robots to achieve ant-like collective environment negotiation.
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