CC-OPI：通信约束下无人机集群的在线分布式任务分配
CC-OPI: Online Distributed Task Allocation for UAV Swarms under Communication Constraints
AI总结：
针对通信受限下无人机集群任务分配失效问题，提出CC-OPI事件驱动算法，交错协商与执行，在250米半径模拟中完成率约0.80，优于基线，以效率换鲁棒性。
AI中文摘要：
在多机器人任务（如灾后搜索与救援）中，短通信距离会将无人机（UAV）集群分割成短暂的信息孤岛。在这种间歇性连接下，主流的“先分配后执行”范式——要求在物理移动前达成全局共识——会失效。本文提出通信约束在线性能影响（CC-OPI）算法，一种事件驱动方法，将任务协商与物理执行交错进行。CC-OPI仅在离散的物理和拓扑事件时重新规划，并整合了两个额外要素。第一是一对适应动态拓扑的成本评估指标——一个带有空间局部性惩罚以促进区域化操作，另一个带有截止时间感知的紧急项——并辅以非抢占式状态锁，保护每架无人机正在进行的动作。第二是去中心化的容错层，将基于版本的状态同步与全局时间驱动的紧急池配对。我们证明CC-OPI在有限时间内终止，无陈旧完成死锁，且在任务时间范围内无无界重分配。在250米通信半径的模拟中，CC-OPI维持约0.80的任务完成率：它领先于匹配的未修改性能影响（PI）和共识捆绑算法（CBBA）规则的在线执行约七个百分点，超过天真转移的静态基线约20个百分点，并在完全连接下保持在PI和CBBA的几个百分点之内。在测试设置内，CC-OPI随连接减弱而优雅降级，并吸收数据包丢失、地形遮挡和运行时任务到达。代价是更多消息和部分冗余旅行——这是为鲁棒性而故意做出的效率权衡。
英文摘要：
In multi-robot missions such as post-disaster search and rescue, a short communication range fragments a swarm of Unmanned Aerial Vehicles (UAVs) into transient information islands. Under such intermittent connectivity, the prevailing "allocate-then-execute" paradigm--which requires global consensus before any physical movement--breaks down. This paper proposes the Communication-Constrained Online Performance Impact (CC-OPI) algorithm, an event-driven method that interleaves task negotiation with physical execution. CC-OPI replans only at discrete physical and topological events and integrates two further elements. The first is a pair of cost-evaluation metrics adapted to dynamic topologies--one with a spatial locality penalty that promotes regionalized operation, the other with a deadline-aware urgency term--complemented by a non-preemptive state lock that shields each UAV's ongoing action. The second is a decentralized fault-tolerance layer that pairs version-based state synchronization with a global-time-driven emergency pool. We establish that CC-OPI terminates in finite time, free of stale-completion deadlock and of unbounded reassignment within the mission horizon. In simulations at a 250 m communication radius, CC-OPI sustains a task completion rate of about 0.80: it leads a matched online execution of the unmodified Performance Impact (PI) and Consensus-Based Bundle Algorithm (CBBA) rules by about seven percentage points, exceeds the naively transferred static baselines by roughly 20 points, and remains within several points of PI and CBBA under full connectivity. Within the tested settings, CC-OPI degrades gracefully as connectivity weakens and absorbs packet loss, terrain occlusion, and runtime task arrival. The price is more messages and some redundant travel--a deliberate trade-off of efficiency for robustness.
