相关论文: Mr.MSTE: Multi-robot Multi-Source Term Estimation …
Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. As the complicated characteristics of wind power prediction error, it would…
Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil spill. The flow dynamics can be time-varying and chaotic,…
This paper introduces a state-machine model for a multi-modal, multi-robot environmental sensing algorithm tailored to dynamic real-world settings. The algorithm uniquely combines two exploration strategies for gas source localization and…
This paper proposes a state-machine model for a multi-modal, multi-robot environmental sensing algorithm. This multi-modal algorithm integrates two different exploration algorithms: (1) coverage path planning using variable formations and…
Heterogeneous multi-robot sensing systems are able to characterize physical processes more comprehensively than homogeneous systems. Access to multiple modalities of sensory data allow such systems to fuse information between complementary…
Accurate projections of wind energy potential under climate change are critical for effective long-term energy planning. While previous studies have highlighted the value of multi-model ensembles, they often fall short in capturing the full…
The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for…
We address the problem of coordinating a team of robots to cover an unknown environment while ensuring safe operation and avoiding collisions with non-cooperative agents. Traditional coverage strategies often rely on simplified assumptions,…
The problem of multi-robot coverage control becomes significantly challenging when multiple robots leave the mission space simultaneously to charge their batteries, disrupting the underlying network topology for communication and sensing.…
Leveraging sensing modalities across diverse spatial and temporal resolutions can improve performance of robotic manipulation tasks. Multi-spatial resolution sensing provides hierarchical information captured at different spatial scales and…
The use of autonomous vehicles for chemical source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using…
We propose a distributed control law for a heterogeneous multi-robot coverage problem, where the robots could have different energy characteristics, such as capacity and depletion rates, due to their varying sizes, speeds, capabilities, and…
Generative robot policies such as Flow Matching offer flexible, multi-modal policy learning but are sample-inefficient. Although object-centric policies improve sample efficiency, it does not resolve this limitation. In this work, we…
This paper presents a cooperative multi-robot multi-target tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network and, consequently, improving overall target tracking accuracy. The concept of normalized…
In the context of constraint-driven control of multi-robot systems, in this paper, we propose an optimization-based framework that is able to ensure resilience and energy-awareness of teams of robots. The approach is based on a novel,…
The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despite attaining substantial performance improvements over their…
Distributed energy resources offer a control-based option to improve distribution system reliability by ensuring system states that positively impact component failure rates. This option is an attractive complement to otherwise costly and…
In this paper, we consider the problem of multirotor flying robots physically interacting with the environment under wind influence. The result are the first algorithms for simultaneous online estimation of contact and aerodynamic wrenches…
Ensemble forecasts and their combination are examined from the perspective of probability spaces. Manipulating ensemble forecasts as discrete probability distributions, multi-model ensemble (MME) forecasts are reformulated as barycenters of…
Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few…