arXiv subjects

Kairong Qin

Publications and source records attributed to Kairong Qin.

4 recordsLinked to original sources

arXiv2025

Ask1: Development and Reinforcement Learning-Based Control of a Custom Quadruped Robot

In this work, we present the design, development, and experimental validation of a custom-built quadruped robot, Ask1. The Ask1 robot shares similar morphology with the Unitree Go1, but features custom hardware components and a different control architecture. We transfer and extend previous reinforcement learning (RL)-based control methods to the Ask1 robot, demonstrating the applicability of our approach in real-world scenarios. By eliminating the need for Adversarial Motion Priors (AMP) and reference trajectories, we introduce a novel reward function to guide the robot's motion style. We demonstrate the generalization capability of the proposed RL algorithm by training it on both the Go1 and Ask1 robots. Simulation and real-world experiments validate the effectiveness of this method, showing that Ask1, like the Go1, is capable of navigating various rugged terrains.

cs.RO↗

arXiv2021

Microbial transport and dispersion in heterogeneous flows created by pillar arrays

Swimming microbes, such as bacteria and algae, live in diverse habitats including soil, ocean and human body which are characterized by structural boundaries and heterogeneous fluid flows. Although much progress has been made in understanding the Brownian ratchet motions of microbes and their hydrodynamic interactions with the wall over the last decades, the complex interplay between the structural and fluidic environment and the self-propelling microbial motions still remains elusive. Here, we developed a Langevin model to simulate and investigate the transport and dispersion of microbes in periodic pillar arrays. By tracing the spatial-temporal evolution of microbial trajectories, we show that the no-slip pillar surface induces local fluid shear which redirects microbial movements. In the vicinity of pillars, looping trajectories and slowly moving speed lead to the transient accumulation and the sluggish transport of microbes. Comprehensive microscopic motions including the swinging, zigzag and adhesive motions are observed. In the pillar array of asymmetric pillar arrangements, the adjacent downstream pillars provide geometric guidance such that the microbial population has a deterministic shift perpendicular to the flow direction. Moreover, effects of the topology of the pillar array, fluid flowing properties and microbial properties on the microbial advection and dispersion in pillar arrays are quantitatively analyzed. These results highlight the importance of structures and flows on the microbial transport and distribution which should be carefully considered in the study of microbial processes.

physics.bio-ph↗

arXiv2020

Infinite-Horizon Optimal Control of Switched Boolean Control Networks with Average Cost: An Efficient Graph-Theoretical Approach

This study investigates the infinite-horizon optimal control problem for switched Boolean control networks with an average-cost criterion. A primary challenge of this problem is the prohibitively high computational cost when dealing with large-scale networks. We attempt to develop a more efficient and scalable approach from a graph-theoretical perspective. First, a weighted directed graph structure called the $\textit{optimal state transition graph}$ (OSTG) is established, whose edges encode the optimal action for each one-step transition between states reachable from a given initial state subject to various constraints. Then, we reduce the infinite-horizon optimal control problem into a minimum mean cycle (MMC) problem in the OSTG. Finally, we develop a novel algorithm that can quickly find a particular MMC by resorting to Karp's algorithm in graph theory and construct afterward an optimal switching and control law based on state feedback. Time complexity analysis shows that our algorithm can outperform all existing methods in terms of time efficiency. A 16-node signaling network in leukemia is used as a benchmark to test its effectiveness. Results show that the proposed graph-theoretical approach is much more computationally efficient: it runs hundreds or even thousands of times faster than existing methods.

eess.SY↗

arXiv2019

Finite-Horizon Optimal Control of Boolean Control Networks: A Unified Graph-Theoretical Approach

This paper investigates the finite-horizon optimal control (FHOC) problem of Boolean control networks (BCNs) from a graph theory perspective. We first formulate two general problems to unify various special cases studied in the literature: (i) the horizon length is $\textit{a priori}$ fixed; (ii) the horizon length is unspecified but finite for given destination states. Notably, both problems can incorporate time-variant costs, which are rarely considered in existing work, and a variety of constraints. The existence of an optimal control sequence is analyzed under mild assumptions. Motivated by BCNs' finite state space and control space, we approach the two general problems in an intuitive and efficient way under a graph-theoretical framework. A weighted state transition graph and its time-expanded variants are developed, and the equivalence between the FHOC problem and the shortest path problem in specific graphs is established rigorously. Two custom algorithms are developed to find the shortest path and construct the optimal control sequence with lower time complexity, though technically a classical shortest-path algorithm in graph theory is sufficient for all problems. Compared with existing algebraic methods, our graph-theoretical approach can achieve state-of-the-art time efficiency while targeting the most general problems. Furthermore, our approach is the first one capable of solving Problem (ii) with time-variant costs. Finally, the Ara operon genetic network in $\textit{E. coli}$ is used as a benchmark example to validate the effectiveness of our approach, and the results of two tasks show that our approach can dramatically reduce the running time.

math.OC↗