Jianqiang Ding's Homepage

My ultimate vision is to enhance the autonomy of machines to reliably assist and benefit human society. My research goal is developing efficient autonomous control policies to function in safety-critical settings with formal guarantees.

I conduct research at the intersection of formal methods and control theory. Through investigating in constructing a unified framework that integrates high-level planning with low-level controls, my research is dedicated to offering universally applicable techniques to improve autonomous agents' stability, safety, and robustness in a variety of applications, including autonomous driving and robotics.

Publications [Google Scholar]

2024

Jianqiang Ding, Taoran Wu, Zhen Liang and Bai Xue PyBDR: Set-boundary based Reachability Analysis Toolkit in Python [FM 2024] 26th International Symposium on Formal Methods [pdf] [code]

Dejin Ren, Zhen Liang, Chenyu Wu, Jianqiang Ding, Taoran Wu, Bai Xue Inner-approximate Reachability Computation via Zonotopic Boundary Analysis [CAV 2024] 36th International Conference on Computer Aided Verification [pdf]

2023

Jianqiang Ding, Taoran Wu, Yuping Qian, Lijun Zhang, Bai Xue Provable Reach-avoid Controllers Synthesis Based on Inner-approximating Controlled Reach-avoid Sets Preprint , arXiv:2304.11550. [pdf] [abstract]
In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a desired target set. The approach falls within the reachability analysis framework and is based on the computation of inner-approximations of controlled reach-avoid sets(CRSs). Given a target set and a safe set, the controlled reach-avoid set is the set of states such that starting from each of them, there exists at least one controller to ensure that the system can enter the target set while staying inside the safe set before the target hitting time. Therefore, the boundary of the controlled reach-avoid set acts as a barrier, which separating states capable of achieving the reach-avoid objective from those that are not, and thus the computed inner-approximation provides a viable space for the system to achieve the reach-avoid objective. Our approach for synthesizing reach-avoid controllers mainly consists of three steps. We first learn a safe set of states in the unknown environment from sensor measurements based on a support vector machine approach. Then, based on the learned safe set and target set, we compute an inner-approximation of the CRS. Finally, we synthesize controllers online to ensure that the system will reach the target set by evolving inside the computed inner-approximation. The proposed method is demonstrated on a Dubin's car system.