Browsing by Author "Shamma, Jeff S."

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Citation - WoS: 1
Citation - Scopus: 1
Peer-to-Peer Localization via On-Board Sensing for Aerial Flocking
(Institute of Electrical and Electronics Engineers Inc., 2020) Omar Rajab, Fat Hy; Guler, Samet; Shamma, Jeff S.
The performance of mobile multi-robot systems dramatically depends on the mutual awareness of individual robots, particularly the positions of other robots. GPS and motion capture cameras are commonly used to acquire and ultimately communicate positions of robots. Such sensing schemes depend on infrastructure and restrict the capabilities of a multi-robot system, e.g., the robots cannot operate in both indoor and outdoor environments. Conversely, peer-to-peer localization algorithms can be used to free the robots from such infrastructures. In such systems, robots use on-board sensing to infer the positions of nearby robots. In this approach, it is essential to have a model of the motion of other robots. We introduce a flocking localization scheme that takes into account motion behavior exhibited by the other robots. The proposed scheme depends only on the robots' on-board sensors and computational capabilities and yields a more accurate localization solution than the peer-to-peer localization algorithms that do not take into account the flocking behavior. We verify the performance of our scheme in simulations and demonstrate experiments on two unmanned aerial vehicles. © 2022 Elsevier B.V., All rights reserved.
Citation - WoS: 43
Citation - Scopus: 49
Peer-to-Peer Relative Localization of Aerial Robots With Ultrawideband Sensors
(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Guler, Samet; Abdelkader, Mohamed; Shamma, Jeff S.
Robots in swarms take advantage of localization infrastructure, such as a motion capture system or global positioning system (GPS) sensors to obtain their global position, which can then be communicated to other robots for swarm coordination. However, the availability of localization infrastructure needs not to be guaranteed, e.g., in GPS-denied environments. Likewise, the communication overhead associated with broadcasting locations may be undesirable. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard relative localization framework for multirobot systems. The setup consists of an anchor robot with three onboard ultrawideband (UWB) sensors and a tag robot with a single onboard UWB sensor. The anchor robot utilizes the three UWB sensors to estimate the tag robot's location by using its onboard sensing and computational capabilities solely, without explicit interrobot communication. Because the anchor UWB sensors lack the physical separation that is typical in fixed UWB localization systems, we introduce filtering methods to improve the estimation of the tag's location. In particular, we utilize a mixture Monte Carlo localization (MCL) approach to capture maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor field experiments on a two-drone setup. The proposed mixture MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates superior performance over the standard particle filter and the extended Kalman filter.