arXiv subjects

Jinyong Chen

Publications and source records attributed to Jinyong Chen.

3 recordsLinked to original sources

arXiv2026

Task-Oriented Formation Decision via Reinforcement Learning: Herding an Attacking Swarm

Multi-robot systems can accomplish tasks that are difficult for a single robot by organizing into task-specific formations. Different from existing studies on multi-robot shape formation, we here study the task-oriented formation decision problem, with a focus on the herding task. This task is challenging due to the attackers' superior maneuverability and their unknown strategies. To address these challenges, we propose the following novel results. First, we encode the formation shape using a low-dimensional parameter vector. This parametric representation reformulates the formation decision as a parameter optimization problem, thereby resolving the limited flexibility of predefined shapes. By optimizing these formation parameters, the defenders' maneuverability disadvantage is mitigated through a formation shape that continuously adapts to task requirements. Second, we develop a reinforcement learning-based policy to regulate the formation parameters. Trained offline in simulations covering diverse attacking strategies, the learned policy can effectively handle adversarial unpredictability during online deployment. Comparative simulations against three baselines demonstrate that our method can successfully accomplish challenging herding tasks. Additional scalability simulations further verify its applicability to simulated scenarios involving dozens of robots. We also validate the practical feasibility of our method on a physical robotic platform with 3 attackers and 7 defenders.

cs.RO↗

arXiv2024

Multi-Robot Pursuit in Parameterized Formation via Imitation Learning

This paper studies the problem of multi-robot pursuit of how to coordinate a group of defending robots to capture a faster attacker before it enters a protected area. Such operation for defending robots is challenging due to the unknown avoidance strategy and higher speed of the attacker, coupled with the limited communication capabilities of defenders. To solve this problem, we propose a parameterized formation controller that allows defending robots to adapt their formation shape using five adjustable parameters. Moreover, we develop an imitation-learning based approach integrated with model predictive control to optimize these shape parameters. We make full use of these two techniques to enhance the capture capabilities of defending robots through ongoing training. Both simulation and experiment are provided to verify the effectiveness and robustness of our proposed controller. Simulation results show that defending robots can rapidly learn an effective strategy for capturing the attacker, and moreover the learned strategy remains effective across varying numbers of defenders. Experiment results on real robot platforms further validated these findings.

cs.RO↗

arXiv2021

Detection and Prevention of New Attacks for ID-based Authentication Protocols

The rapid development of information and network technologies motivates the emergence of various new computing paradigms, such as distributed computing, and edge computing. This also enables more and more network enterprises to provide multiple different services simultaneously. To ensure these services can conveniently be accessed only by authorized users, many password and smart card-based authentication schemes for multi-server architecture have been proposed. In this paper, we review several dynamic ID-based password authentication schemes for multi-server environments. New attacks against four of these schemes are presented, demonstrating that an adversary can impersonate either legitimate or fictitious users. The impact of these attacks is the failure to achieve the main security requirement: authentication. Thus, the security of the analyzed schemes is proven to be compromised. We analyze these four dynamic ID-based schemes and discuss the reasons for the success of the new attacks. Additionally, we propose a new set of design guidelines to prevent such exploitable weaknesses on dynamic ID-based authentication protocols. Finally, we apply the proposed guidelines to the analyzed protocols and demonstrate that violation of these guidelines leads to insecure protocols.

cs.CR↗