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pro vyhledávání: '"Li, Shaoyuan"'
Autor:
Chen, Qianqian, Li, Shaoyuan
This paper investigates an aperiodic distributed model predictive control approach for multi-agent systems (MASs) in which parameterized synchronization constraints is considered and an innovative self-triggered criterion is constructed. Different fr
Externí odkaz:
http://arxiv.org/abs/2405.11006
Autor:
Chen, Qianqian, Li, Shaoyuan
This paper investigates the distributed model predictive control for an asynchronous nonlinear multi-agent system with external interference via a self-triggered generator and a prediction horizon regulator. First, a shrinking constraint related to t
Externí odkaz:
http://arxiv.org/abs/2405.11005
We investigate the problem of optimal control synthesis for Markov Decision Processes (MDPs), addressing both qualitative and quantitative objectives. Specifically, we require the system to fulfill a qualitative surveillance task in the sense that a
Externí odkaz:
http://arxiv.org/abs/2403.18632
In this paper, we investigate the problem of linear temporal logic (LTL) path planning for multi-agent systems, introducing the new concept of \emph{ordering constraints}. Specifically, we consider a generic objective function that is defined for the
Externí odkaz:
http://arxiv.org/abs/2403.17704
In this paper, we investigate the problem of optimal supervisory control for the discrete event systems under energy constraints. We consider that the execution of events consumes energy and the energy can be replenished at specific reload states. Wh
Externí odkaz:
http://arxiv.org/abs/2402.05564
Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning
This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial rob
Externí odkaz:
http://arxiv.org/abs/2312.05764
Inspired by biological motion generation, central pattern generators (CPGs) is frequently employed in legged robot locomotion control to produce natural gait pattern with low-dimensional control signals. However, the limited adaptability and stabilit
Externí odkaz:
http://arxiv.org/abs/2310.07744
Ensuring safety for vehicle overtaking systems is one of the most fundamental and challenging tasks in autonomous driving. This task is particularly intricate when the vehicle must not only overtake its front vehicle safely but also consider the pres
Externí odkaz:
http://arxiv.org/abs/2310.06553
Publikováno v:
NeurIPS 2023
Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like
Externí odkaz:
http://arxiv.org/abs/2310.04078
In this work, we investigate task planning for mobile robots under linear temporal logic (LTL) specifications. This problem is particularly challenging when robots navigate in continuous workspaces due to the high computational complexity involved. S
Externí odkaz:
http://arxiv.org/abs/2309.14050