Abstract
Conference Title: 2019 American Control Conference (ACC) Conference Start Date: 2019, July 10 Conference End Date: 2019, July 12 Conference Location: Philadelphia, PA, USA Swarm applications use motion capture system or GPS sensors as localization systems. However, motion capture systems provide local solutions, and GPS sensors are not reliable in occluded environments. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard localization framework for multi-robot systems. Our framework consists of an anchor robot with three ultrawideband (UWB) sensors and a tag robot with a single UWB sensor. The anchor robot utilizes the three UWB sensors as a localization infrastructure and estimates the tag robot's location by using its on-board sensing and computational capabilities solely, without explicit inter-robot communication. We utilize a dual Monte-Carlo localization approach to capture the agile maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor experiments on a two-drone setup. The proposed framework with the dual MCL algorithm yields accurate estimates for various speed profiles of the tag robot, outperforms the standard particle filter and extended Kalman filter, and suffice for a relative position maintenance application.