Flexible team formation and task allocation for multi-robot systems in complex domains

探索未知环境以收集信息（例如绘图）是一个自然适合使用多个智能机器人的问题。在极具挑战性和危险的环境中，如城市搜索和救援（USAR，机器人在灾难发生后用于搜索人类受害者），使用具有不同运动、感觉和控制能力的机器人（异构机器人）会带来显著的好处。例如，当机器人发现一个它无法进入的裂缝时，另一个团队成员可能足够小，可以通过这个裂缝。同样，不同的地形适用于不同类型的机器人（如人形机器人和轮式机器人）。在这些领域，机器人也必须被认为是消耗性的，这意味着团队结构将不是静态的：可能需要新型的专业机器人，丢失或损坏的机器人必须更换。理想情况下，多个团队应该在一个广泛的区域发布，并协作构建一个共享的地图。现有的方法通常需要集中控制（如果控制器丢失，系统会很脆弱）、静态团队结构和空间的共享外部表示（例如GPS，可能不可用）。与USAR类似的问题存在于太空探索、军事和安全领域，以及区域覆盖任务（例如机器人收割）。这项工作将涉及在USAR领域中分散协调多个异构机器人团队（轮式、脚踏式、人形和微型机器人）。它将通过机器人之间的局部相遇（不需要广播通信），支持对世界和彼此能力的共享表征的逐步发展。这将允许更好的决策，哪些任务应该分配给哪些机器人（例如适应机器人的部分故障，或者知道什么时候新遇到的机器人类型可能有用）。随着探索的展开，团队将调整其结构，例如，增加成员以获得新技能，并删除不需要或表现不佳的成员（包括他们的领导者），允许其他团队拾取这些成员（以及他们所包含的知识）。所有机器人都将在其能力允许的范围内自主运行（即不完全依赖于领导者）。

The exploration of an unknown environment to gather information (e.g. mapping) is a problem naturally suited to the use of multiple intelligent robots. In highly challenging and dangerous environments such as urban search and rescue (USAR, where robots are used to search for human victims after a disaster) significant benefit derives from using robots with differing motor, sensory, and control abilities (heterogeneous robots). While a robot might find a crevasse it cannot enter, another team member might be small enough to move through this, for example. Similarly, different terrain is amenable to different robot types (e.g. humanoid vs wheeled). In such domains robots must also be considered expendable, meaning that team structure will not be static: new types of specialized robots may be called for, and robots that are lost or damaged must be replaced. Ideally multiple teams should be released across a broad area and collaborate to build a shared map. Existing approaches to this typically require centralized control (leaving the system vulnerable if a controller is lost), static team structures, and a shared external representation for space (e.g. GPS, which may be unavailable). Similar issues to USAR exist in space exploration, military, and security domains, as well as area coverage tasks (e.g. robotic harvesting). This work will involve the decentralized coordination of multiple teams of heterogeneous robots (wheeled, treaded, humanoid, and microrobots) in a USAR domain. It will support the gradual development of a shared representation of the world and one another's abilities through local encounters between robots (without broadcast communication). This will allow better decision making as to which tasks should be distributed to which robots (e.g. adapting to partial failure of robots, or knowing when a newly-encountered robot type might be useful). Teams will adapt their structure as exploration unfolds, adding members to acquire new skills, for example, and removing unneeded or poorly performing members (including their leaders), allowing these to be picked up by other teams (along with the knowledge they contain). All robots will function autonomously (i.e. not rely solely on a leader) to the degree their abilities allow.