Adaptation to Team Composition Changes for Heterogeneous Multi-Robot Sensor Coverage
Adaptation to Team Composition Changes for Heterogeneous Multi-Robot Sensor Coverage
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DOI：
10.1109/icra48506.2021.9560960
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Brian Reily;Terran Mott;Hao Zhang-
中科院分区：
文献类型：
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作者：
Brian Reily;Terran Mott;Hao Zhang-
我们考虑多机器人传感器覆盖的问题，它涉及在一个环境中部署一个多机器人团队，并优化整个环境的传感质量。由于真实世界的环境涉及各种传感器信息，并且单个机器人的传感器数量有限，因此成功的多机器人传感器覆盖需要以每个团队成员的传感质量最大化的方式部署机器人。此外，由于单个机器人具有不同的传感器补充，并且机器人和传感器都可能发生故障，因此机器人必须能够适应和调整它们如何评估每个传感能力，以便获得最完整的环境视图，即使团队组成发生变化。我们引入了一种新的配方，传感器覆盖率的多机器人团队异构的传感能力，最大限度地提高每个机器人的传感质量，平衡不同的传感能力的基础上，个别机器人的整体团队组成。我们提出了一个基于正则化优化的解决方案，该解决方案使用稀疏诱导项来确保机器人团队专注于所有可能的事件类型，并且我们证明了它收敛到最优解。通过大量的模拟，我们表明，我们的方法是能够有效地部署一个多机器人团队，以最大限度地提高环境的传感质量，响应故障的多机器人团队比非自适应方法更强大。
We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor coverage requires the deployment of robots in such a way that each individual team member’s sensing quality is maximized. Additionally, because individual robots have varying complements of sensors and both robots and sensors can fail, robots must be able to adapt and adjust how they value each sensing capability in order to obtain the most complete view of the environment, even through changes in team composition. We introduce a novel formulation for sensor coverage by multi-robot teams with heterogeneous sensing capabilities that maximizes each robot's sensing quality, balancing the varying sensing capabilities of individual robots based on the overall team composition. We propose a solution based on regularized optimization that uses sparsity-inducing terms to ensure a robot team focuses on all possible event types, and which we show is proven to converge to the optimal solution. Through extensive simulation, we show that our approach is able to effectively deploy a multi-robot team to maximize the sensing quality of an environment, responding to failures in the multi-robot team more robustly than non-adaptive approaches.