Zobrazeno 1 - 10
of 347
pro vyhledávání: '"Zhang, Zengjie"'
This paper develops a correct-by-design controller for an autonomous vehicle interacting with opponent vehicles with unknown intentions. We define an intention-aware control problem incorporating epistemic uncertainties of the opponent vehicles and m
Externí odkaz:
http://arxiv.org/abs/2404.09037
Autor:
Engelaar, Maico H. W., Zhang, Zengjie, Vlahakis, Eleftherios E., Lazar, Mircea, Haesaert, Sofie
This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on
Externí odkaz:
http://arxiv.org/abs/2404.02111
This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are predefined and remain unchanged dur
Externí odkaz:
http://arxiv.org/abs/2402.03165
Model Predictive Control (MPC) has been widely applied to the motion planning of autonomous vehicles. An MPC-controlled vehicle is required to predict its own trajectories in a finite prediction horizon according to its model. Beyond this, the vehicl
Externí odkaz:
http://arxiv.org/abs/2310.02843
The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying the driving styles of its nearby AVs can reliably evaluate the risk of c
Externí odkaz:
http://arxiv.org/abs/2308.12069
Reinforcement learning (RL) for motion planning of multi-degree-of-freedom robots still suffers from low efficiency in terms of slow training speed and poor generalizability. In this paper, we propose a novel RL-based robot motion planning framework
Externí odkaz:
http://arxiv.org/abs/2307.16062
Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment. Nevertheless, the reward function for an RL a
Externí odkaz:
http://arxiv.org/abs/2306.03220
This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR). The work is motivated by the limitation of the conventional method that does not ensure the satisfaction of hard state- a
Externí odkaz:
http://arxiv.org/abs/2304.05723
Finding an efficient way to adapt robot trajectory is a priority to improve overall performance of robots. One approach for trajectory planning is through transferring human-like skills to robots by Learning from Demonstrations (LfD). The human demon
Externí odkaz:
http://arxiv.org/abs/2304.05703
Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may l
Externí odkaz:
http://arxiv.org/abs/2304.05693