1,721,101 research outputs found
Multi-stage discrete time and randomized dynamic average consensus
Full text linkIn this paper we propose a novel local interaction protocol which solves the discrete time dynamic average consensus problem, i.e., the consensus problem on the average value of a set of time-varying input signals in an undirected graph. The proposed interaction protocol is based on a multi-stage cascade of dynamic consensus filters which tracks the average value of the inputs with small and bounded error. We characterize its convergence properties for time-varying discrete-time inputs with bounded variations. The main novelty of the proposed algorithm is that, with respect to other dynamic average consensus protocols, we obtain the next unique set of advantages: i) The protocol, inspired by proportional dynamic consensus, does not exploit integral control actions or input derivatives, thus exhibits robustness to re-initialization errors, changes in the network size and noise in the input signals; ii) The proposed design allows to trade-off the quantity of information locally exchanged by the agents, i.e., the number of stages, with steady-state error, tracking error and convergence time; iii) The protocol can be implemented with randomized and asynchronous local state updates and keep in expectation the performance of the discrete-time version. Numerical examples are given to corroborate the theoretical findings, including the case where a new agent joins and leaves the network during the algorithm execution to show robustness to re-initialization errors during runtime
A Spatially Structured Genetic Algorithm for Multi-Robot Localization
No full textIn this paper the multi-robot localization problem is addressed. A new framework based on a spatially structured genetic algorithm is proposed. Collaboration among robots is considered and is limited to the exchange of sensor data. Additionally, the relative distance and orientation among robots are assumed to be available. The proposed framework (MR-SSGA) takes advantage of the cooperation so that the perceptual capability of each robot is extended. Cooperation can be set-up at any time when robots meet, it is fully decoupled and does not require robots to stop. Several simulations have been performed, either considering cooperation activated or not, in order to emphasize the effectiveness of the collaboration strategy
Multi-Agent Coordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Management
Full text linkThis article presents a multi-agent control architecture and an online optimization method based on a dynamic average consensus to coordinate the power consumption of a large population of thermostatically controlled loads (TCLs). Our objective is to penalize peaks of power demand, smooth the load profile, and enable demand-side management. The proposed architecture and methods exploit only local measurements of power consumption via smart power sockets (SPSs) with no access to their internal temperature. No centralized aggregator of information is exploited, and agents preserve their privacy by cooperating anonymously only through consensus-based distributed estimation. The interactions among devices occur through an unstructured peer-to-peer (P2P) network over the Internet. Methods for parameter identification, state estimation, and mixed logical modeling of TCLs and SPSs are included. The architecture is designed from a multi-agent and plug-and-play perspective, in which existing household appliances can interact with each other in an urban environment. Finally, a novel low-cost testbed is proposed along with numerical tests and experimental validation
Gossip-Based Estimation of Centroid and Common Reference Frame in Open Multi-Agent Systems
No full textDecentralized estimation of centroid and common reference frame in multi-agent systems is a challenging problem, particularly when agents do not have access to global positioning data. This challenge intensifies in Open Multi-Agent Systems (OMAS), where the network composition dynamically changes due to agents joining or leaving, causing fluctuations in the number of participants. This paper presents a novel, decentralized gossip-based algorithm that enables agents in OMAS to collaboratively estimate both the centroid and a common reference frame in a 2-D environment where the number of agents may fluctuate over time. Notably, our approach remains robust despite noisy distance measurements and intermittent participation, as it relies on asynchronous, local pairwise interactions. Designed to accommodate the dynamic nature of network topologies, our algorithm can be employed for real-world applications where agents can join or leave the system due to failures, resource limitations or external environmental factors
A Fitness-Sharing based Genetic Algorithm for Collaborative Multi Robot Localization
No full textIn this paper, a novel genetic algorithm based on a “collaborative” fitness-sharing technique to deal with the multi-robot localization problem is proposed. Indeed, the use of the fitness-sharing is twofold and competitive. It preserves the diversity among individuals during the space exploration process, thus maintaining evolutionary niches over time, and reinforces the best hypotheses by means of collaboration among robots, thus augmenting the selection pressure. Simulations by exploiting the robotics framework Player/Stage have been performed along with a proper statistical analysis for performance assessment
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