1 code implementation • 13 Apr 2024 • Zengjie Zhang, Zhiyong Sun, Sofie Haesaert
This paper develops a correct-by-design controller for an autonomous vehicle interacting with opponent vehicles with unknown intentions.
no code implementations • 2 Apr 2024 • Maico H. W. Engelaar, Zengjie Zhang, Eleftherios E. Vlahakis, Mircea Lazar, Sofie Haesaert
This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications.
no code implementations • 5 Feb 2024 • Maico H. W. Engelaar, Zengjie Zhang, Mircea Lazar, Sofie Haesaert
This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime.
no code implementations • 23 Aug 2023 • Ni Dang, Tao Shi, Zengjie Zhang, Wanxin Jin, Marion Leibold, Martin Buss
Nevertheless, an important indicator of the driving style, i. e., how an AV reacts to its nearby AVs, is not fully incorporated in the feature design of previous ME-IRL methods.
no code implementations • 29 Jul 2023 • Zengjie Zhang, Jayden Hong, Amir Soufi Enayati, Homayoun Najjaran
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.
1 code implementation • 5 Jun 2023 • Lin-Chi Wu, Zengjie Zhang, Sofie Haesaert, Zhiqiang Ma, Zhiyong Sun
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.
no code implementations • 12 Apr 2023 • Jayden Hong, Zengjie Zhang, Amir M. Soufi Enayati, Homayoun Najjaran
Our contribution is introducing a systematic method to extract the dynamic features from human demonstration to auto-tune the parameters in the DMP framework.
1 code implementation • 12 Apr 2023 • Zengjie Zhang, Fangzhou Liu, Tong Liu, Jianbin Qiu, Martin Buss
A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied.
no code implementations • 12 Apr 2023 • Qingchen Liu, Zengjie Zhang, Nhan Khanh Le, Jiahu Qin, Fangzhou Liu, Sandra Hirche
This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR).
no code implementations • 12 Apr 2023 • Ram Dershan, Amir M. Soufi Enayati, Zengjie Zhang, Dean Richert, Homayoun Najjaran
Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation.
no code implementations • 12 Apr 2023 • Amir M. Soufi Enayati, Zengjie Zhang, Kashish Gupta, Homayoun Najjaran
A comparison study between the proposed method and a traditional off-policy reinforcement learning algorithm indicates its advantage in learning performance and potential value for applications.
no code implementations • 1 Apr 2023 • Shuhao Qi, Zengjie Zhang, Sofie Haesaert, Zhiyong Sun
In many practical scenarios, multi-robot systems are envisioned to support humans in executing complicated tasks within structured environments, such as search-and-rescue tasks.
1 code implementation • 30 Mar 2023 • Zengjie Zhang, Sofie Haesaert
In this paper, we propose a framework to transform a long and complex specification into separate forms in time, to be more specific, the logical combination of a series of short and simple subformulas with non-overlapping timing intervals.
no code implementations • 7 Mar 2023 • Zengjie Zhang, Yingwei Du, Tong Liu, Fangzhou Liu, Martin Buss
Thirdly, techniques of incremental support vector machine are applied to develop the recursive algorithm to estimate the system switching manifolds, with its stability proven by a Lynapunov-based method.
no code implementations • 13 Jan 2023 • Zengjie Zhang, Qingchen Liu, Mohammad H. Mamduhi, Sandra Hirche
Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes.
no code implementations • 30 Mar 2022 • Tong Liu, Zengjie Zhang, Fangzhou Liu, Martin Buss
These responses depend on the unknown states at switching instants (SASI) and constitute an additive disturbance to the parameter estimation, which obstructs parameter convergence to zero.
no code implementations • 9 Jun 2021 • Cong Li, Zengjie Zhang, Ahmed Nesrin, Qingchen Liu, Fangzhou Liu, Martin Buss
This paper presents an integrated perception and control approach to accomplish safe autonomous navigation in unknown environments.