发表机构
University of Michigan; Charles Stark Draper Laboratory（密歇根大学; 查尔斯·斯塔克·德拉珀实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出AC-DC自适应通信方法，联合调整通信对象、时机与状态内容，实现多机器人遍历搜索的可扩展动态平均一致性，相比基线降低27.5%误差且通信量减少8.7倍。
AI 中文摘要
我们研究了在有限通信范围、有限速率和干扰受限通信条件下，多机器人系统中的可扩展点对点动态平均一致性（DC）问题。我们提出了动态平均一致性的自适应通信方法（AC-DC），该方法利用本地输入和成功接收的邻居信息，联合自适应地调整谁与谁通信、何时通信以及通信一致性状态的哪些部分。每个机器人的一致性状态估计当前机器人本地输入的平均值。AC-DC在本地输入变化时更新这些估计，并平均机器人对之间交换的值。在AC-DC中，机器人对无需等待每个机器人完成一轮通信即可进行更新，且所选状态消息携带的一致性状态坐标对于固定状态表示而言与团队规模无关。我们将AC-DC应用于动态优先级多机器人遍历搜索：一个一致性流估计团队访问情况以进行运动协调，另一个流融合区域测量信息以更新不确定性地图和搜索目标。在多达80个机器人的十二种设置及每种设置20次配对试验中，AC-DC在比较的分散方法中具有最低的平均（i）归一化协方差迹线下面积（AUC）和（ii）尝试的建模通信负载。跨设置平均，AC-DC相对于最先进的基线实现了27.5%的配对AUC降低，通信流量减少了8.7倍。随着机器人数量增加，我们观察到AC-DC的通信负载接近理想集中式基线（一个地面计算站直接与所有机器人通信）的通信负载：在固定600×600米扩展测试中，120个机器人时，AC-DC使用19.3 MB，而理想集中式基线为19.2 MB，同时保持点对点通信。
英文摘要
We study scalable peer-to-peer dynamic average consensus (DC) for multi-robot systems under finite-range, finite-rate, and interference-constrained communication. We introduce Adaptive Communication for Dynamic Average Consensus (AC-DC), which jointly adapts Who communicates with whom, When, and over What parts of the consensus state, using local inputs and successfully received neighbor information. Each robot's consensus state estimates the current average of the robots' local inputs. AC-DC updates these estimates as local inputs change and averages the values exchanged between robot pairs. In AC-DC, robot pairs update without waiting for every robot to complete a communication round, and the selected-state messages carry consensus state coordinates independent of team size for a fixed state representation. We apply AC-DC to dynamic-priority multi-robot ergodic search: one consensus stream estimates team visitation for motion coordination, while the other fuses regional measurement information to update uncertainty maps and search targets. Across twelve settings with up to 80 robots and 20 paired trials per setting, AC-DC has the lowest mean (i) normalized covariance-trace area under the curve (AUC) and (ii) attempted modeled communication payload among the compared decentralized methods. Averaged across settings, AC-DC achieves paired AUC reductions of 27.5% relative to state-of-the-art baselines, with 8.7x less communication traffic. As the number of robots increases, we observe that AC-DC's communication payload approaches that of the ideal centralized baseline (one ground compute-station communicating directly with all robots): with 120 robots in a fixed 600 x 600 m scaling test, AC-DC uses 19.3 MB versus 19.2 MB for the ideal centralized baseline, while remaining peer-to-peer.
Comments9 pages, 3 figures
