635 research outputs found
Local ant system for allocating robot swarms to time-constrained tasks
We propose a novel application of the Ant Colony Optimization algorithm to efficiently allocate a swarm of homogeneous robots to a set of tasks that need to be accomplished by specific deadlines. We exploit the local communication between robots to periodically evaluate the quality of the allocation solutions, and agents select independently among the high-quality alternatives. The evaluation is performed using pheromone trails to favor allocations which minimize the execution time of the tasks. Our approach is validated in both static and dynamic environments (i.e. the task availability changes over time) using different sets of physics-based simulations. (C) 2018 Elsevier B.V. All rights reserved
Collective sampling of environmental features under limited sampling budget
Exploration of an unknown environment is one of the most prominent tasks for multi-robot systems. In this paper, we focus on the specific problem of how a swarm of simulated robots can collectively sample a particular environment feature. We propose an energy-efficient approach for collective sampling, in which we aim to optimize the statistical quality of the collective sample while each robot is restricted in the number of samples it can take. The individual decision to sample or discard a detected item is performed using a voting process, in which robots vote to converge to the collective sample that reflects best the inter-sample distances. These distances are exchanged in the local neighbourhood of the robot. We validate our approach using physics-based simulations in a 2D environment. Our results show that the proposed approach succeeds in maximizing the spatial coverage of the collective sample, while minimizing the number of taken samples. (C) 2019 Elsevier B.V. All rights reserved
Scale-free features in collective robot foraging
In many complex systems observed in nature, properties such as scalability, adaptivity, or rapid information exchange are often accompanied by the presence of features that are scale-free, i.e., that have no characteristic scale. Following this observation, we investigate the existence of scale-free features in artificial collective systems using simulated robot swarms. We implement a large-scale swarm performing the complex task of collective foraging, and demonstrate that several space and time features of the simulated swarm-such as number of communication links or time spent in resting state-spontaneously approach the scale-free property with moderate to strong statistical plausibility. Furthermore, we report strong correlations between the latter observation and swarm performance in terms of the number of retrieved items
3D Multi-Robot Exploration with a Two-Level Coordination Strategy and Prioritization
This work presents a 3D multi-robot exploration framework for a team of UGVs
moving on uneven terrains. The framework was designed by casting the two-level
coordination strategy presented in [1] into the context of multi-robot
exploration. The resulting distributed exploration technique minimizes and
explicitly manages the occurrence of conflicts and interferences in the robot
team. Each robot selects where to scan next by using a receding horizon
next-best-view approach [2]. A sampling-based tree is directly expanded on
segmented traversable regions of the terrain 3D map to generate the candidate
next viewpoints. During the exploration, users can assign locations with higher
priorities on-demand to steer the robot exploration toward areas of interest.
The proposed framework can be also used to perform coverage tasks in the case a
map of the environment is a priori provided as input. An open-source
implementation is available online
Anonymous hedonic game for task allocation in a large-scale multiple agent system
This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a strongly connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the outcome, and additionally show that at least 50% of suboptimality can be guaranteed if social utilities are nondecreasing functions with respect to the number of coworking agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation
A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems
The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms
Hybrid Data-driven Framework for Shale Gas Production Performance Analysis via Game Theory, Machine Learning and Optimization Approaches
A comprehensive and precise analysis of shale gas production performance is
crucial for evaluating resource potential, designing field development plan,
and making investment decisions. However, quantitative analysis can be
challenging because production performance is dominated by a complex
interaction among a series of geological and engineering factors. In this
study, we propose a hybrid data-driven procedure for analyzing shale gas
production performance, which consists of a complete workflow for dominant
factor analysis, production forecast, and development optimization. More
specifically, game theory and machine learning models are coupled to determine
the dominating geological and engineering factors. The Shapley value with
definite physical meanings is employed to quantitatively measure the effects of
individual factors. A multi-model-fused stacked model is trained for production
forecast, on the basis of which derivative-free optimization algorithms are
introduced to optimize the development plan. The complete workflow is validated
with actual production data collected from the Fuling shale gas field, Sichuan
Basin, China. The validation results show that the proposed procedure can draw
rigorous conclusions with quantified evidence and thereby provide specific and
reliable suggestions for development plan optimization. Comparing with
traditional and experience-based approaches, the hybrid data-driven procedure
is advanced in terms of both efficiency and accuracy.Comment: 37 pages, 15 figures, 6 table
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