Abstract-Large numbers of collaborating robots are advantageous for solving distributed problems. In order to efficiently solve the task at hand, the robots often need accurate localization. In this work, we address the localization problem by developing a solution that has low computational and sensing requirements, and that is easily deployed on large robot teams composed of cheap robots. We build upon a real-time, particlefilter based localization algorithm that is completely decentralized and scalable, and accommodates realistic robot assumptions including noisy sensors, and asynchronous and lossy communication. In order to further reduce this algorithm's overall complexity, we propose a low-cost particle clustering method, which is particularly well suited to the collaborative localization problem. Our approach is experimentally validated on a team of ten real robots. A variety of tasks performed by multi-robot systems such as search and rescue and environmental monitoring often need accurate localization to succeed. Due to the intrinsic nature of such tasks, the individual agents are often confined to small size and weight, which sets hard limits on on-board resources. Simultaneously, a large portion of the robot's resources may be dedicated to the task at hand, leaving little room for solving the localization problem. These compounding problems pose the challenge of designing systems and algorithms that can flexibly accommodate given restrictions, without compromising performance. In this paper, we consider the problem of absolute localization of a team of mobile robots for unknown initial robot positions. Our method builds upon previous work [15] , where we proposed a collaborative localization algorithm targeting miniaturized, computationally limited platforms equipped with noisy, low-power sensing modalities. The collaboration strategy employs a robot detection model based on noisy, local, relative range and bearing observations. The work in this paper extends our previous approach by proposing a particle clustering method which reduces the complexity of the overall localization algorithm. We detail how the clustering method accommodates the constraints of the robot detection model, and show that, for modest range and bearing noise, it finds a near-optimal approximation. Finally, we test the scalability and robustness of our approach by performing experiments on a team of ten real robots.
Amanda et al. provide a PF-based algorithm that is completely decentralized and a low-cost particle clustering method to reduce the computational complexity REF .
Low-cost collaborative localization for large-scale multi-robot systems
2012 IEEE International Conference on Robotics and Automation
2012 IEEE International Conference on Robotics and Automation
Computer Science