Smart Teams: Local, Distributed Strategies for Self-Organizing Robotic Exploration Teams

在第二阶段，我们从第一阶段向几个方向扩展了我们的工作：我们开发了第一个渐近最优策略-Hopper策略-在移动继电器的帮助下，使一个探索机器人与其大本营保持连接。此外，我们还设计了应对多个探险者的优先策略。由于在第二阶段开发了Smart Team模拟器，我们将能够在第三阶段试验地理探索策略。我们进一步扩展和总结了集合图探索方面的工作，给出了局部性约束下机器人任务分配问题的第一个模型和复杂性理论刻画。自主机器人移动的另一个限制是能量限制。我们已经引入了算法来寻找一条最小化移动和通信的能量消耗的路径。最后，我们已经开始研究仅使用本地信息的移动目标的定位。在新阶段，我们计划延伸、评估和统一我们的工作。更具体地说，我们将解决以下问题：·探索：我们计划弄清楚如何将我们对图形探索的见解应用于地理探索。这项工作将部分是试验性的，使用我们的Smart Teams模拟器。·保持通信和编队：我们计划进一步研究如何在有障碍物的地形上使用移动接力机器人保持多个探险机器人的连接。此外，我们计划开发新的、更现实的保持沟通的成本模型。我们观察到，对于许多探险者来说，我们的通信问题与保持一群移动物体的队形密切相关。因此，我们将朝着这个方向扩展我们的工作。·任务：我们计划开发本地算法，能够将多组机器人分配给任务，这样机器人就可以集体解决这些问题。我们的复杂性理论洞察表明，这些问题是困难的。因此，我们将尝试开发局部近似，并将采用新的局部模型变体来描述我们的作业问题。·能耗：我们计划进一步研究涉及多个移动中继的通信和运动联合成本的优化。此外，我们希望开发利用机器人可控移动性的本地算法，以实现Smart Teams中的整体能源优化配置。·统一：在最后阶段，我们将努力结合我们在勘探、沟通、任务和能源效率方面的成果。例如，我们计划弄清楚有关特定运动模式的知识，例如它们是由(地理)探索战略提供的，可以在多大程度上用来简化维持通信网络的战略。

„During the second phase, we have extended our work from the first phase in several directions: We have developed the first asymptotically optimal strategy - the Hopper Strategy - for keeping an exploring robot connected to its base camp with the help of mobile relays. In addition, we have designed first strategies dealing with multiple explorers. Thanks to the development ofthe Smart Teams Simulator in the second phase, we will be able to experiment with geographic exploration strategies in the third phase. We have furthermore extended and wrapped up our work on collective graph exploration and presented first models and complexity theoretic characterizations for the problem of assigning tasks to robots under locality constraints. Another restraint for autonomous robot movement are energy limitations. We have introduced algorithms to find a path minimizing the energy consumption of movement and communication. Finally, we have started to investigate the localization of mobile targets using only local information. In the new phase, we plan to extend, evaluate, and unify our work. More specifically, we will tackle the following problems: • Exploration: We plan to figure out how to adapt our insights into graph exploration to geographic exploration. This work will partly be experimental, using our Smart Teams Simulator. • Maintaining Communication and Formations: We plan to further investigate how to keep multiple explorer robots connected using mobile relay robots in terrains with obstacles. In addition, we plan to develop new, more realistic cost models for maintaining communication. We have observed that our communication problem for many explorers is closely connected to maintaining formations in flocks of moving objects. We will therefore extend our work in this direction. • Assignment: We plan to develop local algorithms that are able to assign groups of robots to tasks such that the robots are collectively able to solve them. Our complexity theoretic insights show that these problems are hard. Therefore we will try to develop local approximations and will employ new variants of locality models to describe our assignments problems. • Energy Consumption: We plan to further study the optimization of the joint cost for communication and motion that involves multiple mobile relays. Moreover, we want to develop local algorithms that exploit controllable mobility of robots to achieve an overall energy-optimal configuration in Smart Teams. • Unification: In the last phase we will work on combining our results on exploration, communication, assignment, and energy efficiency. E.g., we plan to figure out how far the knowledge about specific movement pattems, as they are given for example by (geographic) exploration strategies, can be used to simplify strategies for maintaining a communication network.“