Solmaz S. Kia

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— We present a novel decentralized cooperative local-ization algorithm for mobile robots. The proposed algorithm is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each robot propagates new intermediate local variables that can be used in(More)
— This paper analyzes distributed algorithmic solutions to dynamic average consensus implemented in continuous time and relying on communication at discrete instants of time. Our starting point is a distributed coordination strategy that, under continuous-time communication, achieves practical asymptotic tracking of the dynamic average of the time-varying(More)
— For a team of mobile robots with limited onboard resources, we propose a partially decentralized implementation of an extended Kalman filter for cooperative localization. In the proposed algorithm, unlike a fully centralized scheme that requires, at each timestep, information from the entire team to be gathered together and be processed by a single(More)
— For a group of mobile robots with communication and computation capabilities, we consider a cooperative local-ization algorithm based on Unscented Kalman filtering. We present a server-client paradigm to distribute the computational cost of this algorithm among team members. The highest computational cost of the Unscented Kalamn filter comes from(More)
Technological advances in ad-hoc networking and miniaturization of electro-mechanical systems are making possible the use of large numbers of mobile agents (e.g., mobile robots, human agents, unmanned vehicles) to perform surveillance, search and rescue, transport and delivery tasks in aerial, underwater, space, and land environments. However, the(More)
This paper reports a partially decentralized implementation of an Extended Kalman filter for the cooperative localization of a team of mobile robots with limited onboard resources. Unlike a fully centralized scheme that requires, at each timestep, information from the entire team to be gathered together and be processed by a single device, our algorithm(More)
This paper presents distributed algorithmic solutions that employ opportunistic inter-agent communication to achieve dynamic average consensus. Our solutions endow individual agents with autonomous criteria that can be checked with the information available to them in order to determine whether to broadcast their state to their neighbors. Our starting point(More)
— We propose a distributed continuous-time algorithm to solve a network optimization problem where the global cost function is a strictly convex function composed of the sum of the local cost functions of the agents. We establish that our algorithm, when implemented over strongly connected and weight-balanced directed graph topologies, converges(More)