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

“在第二阶段，我们在几个方向上扩展了第一阶段的工作：我们开发了第一个渐近最优策略——霍珀策略——用于在移动中继的帮助下保持探索机器人与大本营的连接。此外，我们还设计了处理多个探索者的第一策略。由于第二阶段智能团队模拟器的开发，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我们在集体图探索方面的工作，并提出了在局部性约束下为机器人分配任务问题的第一个模型和复杂性理论表征。自主机器人运动的另一个限制是能量限制。我们引入了一些算法来找到一条路径，使运动和通信的能量消耗最小化。最后，我们已经开始研究仅使用本地信息的移动目标的本地化。在新的阶段，我们计划扩展、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适应于地理探索。这项工作将部分是实验性的，使用我们的智能团队模拟器。•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中使用移动中继机器人保持多个探索机器人的连接。此外，我们计划开发新的、更现实的成本模型来维持沟通。我们已经观察到，对于许多探索者来说，我们的沟通问题与在一群移动物体中保持队形密切相关。因此，我们将在这方面扩大我们的工作。•分配：我们计划开发局部算法，能够将一组机器人分配到任务中，使机器人能够集体解决问题。我们的复杂性理论见解表明，这些问题是困难的。因此，我们将尝试发展局部近似，并将采用局部模型的新变体来描述我们的分配问题。•能源消耗：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，我们希望开发局部算法，利用机器人的可控移动性来实现智能团队的整体能量优化配置。•统一：在最后阶段，我们将把我们在探索、交流、分配和能源效率方面的成果结合起来。例如，我们计划弄清楚关于特定运动模式的知识，例如（地理）探索策略，可以用来简化维护通信网络的策略。”

„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.“