Zobrazeno 1 - 10
of 301
pro vyhledávání: '"P, Augustinos"'
This paper provides an overview, analysis, and comparison of second-order dynamic optimization algorithms, i.e., constrained Differential Dynamic Programming (DDP) and Sequential Quadratic Programming (SQP). Although a variety of these algorithms has
Externí odkaz:
http://arxiv.org/abs/2409.11649
This paper presents a novel distributed robust optimization scheme for steering distributions of multi-agent systems under stochastic and deterministic uncertainty. Robust optimization is a subfield of optimization which aims in discovering an optima
Externí odkaz:
http://arxiv.org/abs/2402.16227
As the scale and complexity of multi-agent robotic systems are subject to a continuous increase, this paper considers a class of systems labeled as Very-Large-Scale Multi-Agent Systems (VLMAS) with dimensionality that can scale up to the order of mil
Externí odkaz:
http://arxiv.org/abs/2305.18718
In this paper, we introduce Tolerant Discrete Barrier States (T-DBaS), a novel safety-embedding technique for trajectory optimization with enhanced exploratory capabilities. The proposed approach generalizes the standard discrete barrier state (DBaS)
Externí odkaz:
http://arxiv.org/abs/2303.03360
This paper proposes Distributed Model Predictive Covariance Steering (DMPCS), a novel method for safe multi-robot control under uncertainty. The scope of our approach is to blend covariance steering theory, distributed optimization and model predicti
Externí odkaz:
http://arxiv.org/abs/2212.00398
We systematically review the Variational Optimization, Variational Inference and Stochastic Search perspectives on sampling-based dynamic optimization and discuss their connections to state-of-the-art optimizers and Stochastic Optimal Control (SOC) t
Externí odkaz:
http://arxiv.org/abs/2211.11878
In this paper, we propose two novel decentralized optimization frameworks for multi-agent nonlinear optimal control problems in robotics. The aim of this work is to suggest architectures that inherit the computational efficiency and scalability of Di
Externí odkaz:
http://arxiv.org/abs/2207.13255
Publikováno v:
Robotics: Science and Systems (RSS), 2022
In this work, we propose a novel safe and scalable decentralized solution for multi-agent control in the presence of stochastic disturbances. Safety is mathematically encoded using stochastic control barrier functions and safe controls are computed b
Externí odkaz:
http://arxiv.org/abs/2202.10658
Generalized Polynomial Chaos (gPC) theory has been widely used for representing parametric uncertainty in a system, thanks to its ability to propagate uncertainty evolution. In an optimal control context, gPC can be combined with several optimization
Externí odkaz:
http://arxiv.org/abs/2104.10836
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