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

探索未知环境以收集信息(例如地图)自然是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，在灾难发生后，机器人被用来搜索人类受害者)，使用具有不同运动、感觉和控制能力的机器人(异类机器人)会带来显著的好处。例如，虽然机器人可能会发现它无法进入的裂缝，但另一名团队成员可能足够小，可以穿过这个裂缝。同样，不同的地形适用于不同的机器人类型(例如，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可牺牲的，这意味着团队结构不会一成不变：可能需要新型专门的机器人，丢失或损坏的机器人必须被取代。理想情况下，应该在大范围内释放多个团队，并协作构建共享地图。现有的办法通常需要集中控制(如果控制器丢失，系统容易受到攻击)、静态团队结构和共享的外部空间表示(例如，GPS，这可能不可用)。在空间探索、军事和安全领域以及区域覆盖任务(例如机器人收割)中存在与USAR类似的问题。这项工作将涉及在USAR领域内分散协调多个不同类型的机器人团队(轮式、踩踏式、人形和微型机器人)。它将支持通过机器人之间的局部相遇(无需广播通信)，逐步发展共享的世界和彼此的能力。这将允许更好地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，或了解新遇到的机器人类型何时可能有用)。随着探索的展开，团队将调整他们的结构，例如，增加成员以获取新技能，并删除不需要的或表现不佳的成员(包括他们的领导)，允许其他团队学习这些成员(以及他们包含的知识)。所有机器人都将在其能力允许的程度上自主运行(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.