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Changlin Chen

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4 papers

2 author rows

ICRA Conference 2025 Conference Paper

Heuristically Guided Compilation for Task Assignment and Path Finding

Zheng Chen
Changlin Chen
Yiran Ni
Junhao Wang

We investigate the Combined Target-Assignment and Path-Finding (TAPF) problem that computes both task assignments and collision-free paths for multiple agents, that is, each agent is required to select a target from an underlying set, reaching which leads to a payoff. There is a cost closely related to the time required for each agent to reach the goal. The objective is to maximize the minimum gain generated by the agents. We proposed a Compilation-Based Approach with Heuristics (TA-CBWH) to approximate the optimal solution, behind which are two critical ideas: (i) for a specific task assignment, we formulate an integer linear programming (ILP) and create the iteration combined with large neighborhood search (LNS) to quickly improve the solution quality to near-optimal; (ii) regarding distinct task assignments, a switching mechanism is developed to determine the most promising iteration while progressively eliminating unnecessary task assignments. Comparative experiments demonstrate that TA-CBWH outperforms a wide range of existing approaches across various maps and different numbers of agents.

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ICRA Conference 2025 Conference Paper

ICBSS: An Improved Algorithm for Multi-Agent Combinatorial Path Finding

Zheng Chen
Changlin Chen
Yiran Ni

The Multi-Agent Combinatorial Path Finding (MCPF) problem is a generalized version of the Multi-Agent Path Finding (MAPF) problem, in which each agent must collectively visit multiple intermediate target locations on the way to its final destination. The state-of-the-art approach for addressing MCPF, known as Conflict-Based Steiner Search (CBSS) [1], leverages K-best joint sequences to create multiple search trees, and employs a CBS-like search to resolve collisions for each tree. Despite its optimality guarantee, CBSS is computationally burdensome due to the duplicated collision resolutions across multiple trees and the computation of the K best joint sequences. To address these challenges, we propose a novel algorithm called Improved Conflict-Based Steiner Search (ICBSS), aiming at expediting CBSS by replacing the multi trees with a single constraint tree (CT), which can be implemented by interleaving the time-dependent traveling salesman algorithm to compute the optimal joint path for agents under the newly generated constraints in each CT vertex. Additionally, we introduce a sub-optimal variant of ICBSS, which improves computational efficiency at the expense of solution optimality. Empirical results show that ICBSS outperforms state-of-the-art MCPF algorithms on a variety of MAPF instances.

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EAAI Journal 2024 Journal Article

Regularized joint self-training: A cross-domain generalization method for image classification

Changlin Chen
Yukun Yang
Minghao Liu
Zhaomin Rong
Shuangbao Shu

Unsupervised domain adaptive (UDA) is a widely used approach in machine learning strategies that helps move information from a source domain with rich supervised data to a target domain with limited supervised data. In recent years, self-training derived from semi-supervised learning provides a powerful tool for unsupervised domain adaptive by training unlabeled target domain data using pseudo-tags. However, in the case of domain shift, the pseudo-labels generated by standard self-training are no longer reliable due to the influence of the class-distribution shift in a specific domain, resulting in the noise of the pseudo-label error blocking the upper bound of the accuracy. To solve these problems, a domain adaptive method based on regularization joint self-training (RJS) is proposed in this paper. RJS consists of two key steps: First, the generation and optimization of pseudo-tags are trained jointly on the source domain to achieve automatic cross-domain generalization of pseudo-tags. Secondly, the regularization method is introduced as a confidence rule for false labels in initial model training, which implicitly prevents the model from remembering false labels. To verify the effectiveness of the proposed method, experiments were performed on VisDA-2017, Office-31, Office-Home, and DomainNet datasets, and the accuracy rates reached 87. 1%, 89. 3%, 76. 9%, and 57. 3%, respectively. In addition, the average classification performance of RJS on VisDA-2017 improved by about 12. 6% compared to FixMatch, the standard self-training method. Experimental results demonstrate that RJS achieves state-of-the-art performance on multiple standard UDA benchmarks and confirm its effectiveness in the presence of domain-specific class distribution shifts.

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EAAI Journal 2023 Journal Article

A storage-efficient SNN–CNN hybrid network with RRAM-implemented weights for traffic signs recognition

Yufei Zhang
Hui Xu
Lixing Huang
Changlin Chen

Traffic Signs Recognition (TSR) is a key technology to implement Automatic Driving System (ADS) and Advanced Driver Assistant System (ADAS). Numerous efforts have been endeavored to improve the TSR accuracy and speed, and Convolution Neural Networks (CNN) are usually employed. However, most of the existing works require large network scale and thus suffer from the drawback of high computation and power consumption. To alleviate this issue, in this paper, we propose an SNN–CNN hybrid network with RRAM-implemented weights to improve the storage and computing efficiency of TSR. Specifically, we utilize SNN to quickly determine the superclass that a traffic sign belongs to. Then utilize CNNs to determine the subclass accurately, thus the complex TSR task is decomposed into multiple simple tasks. Finally, the network weights are binarized to alleviate the implementation of the hybrid network on RRAM-based accelerators, which have higher power efficiency than CMOS logic based ones. Experimental results indicate that, when compared with state of the art CNN method, the SNN–CNN hybrid network can achieve similar accuracy with less weight scale (−69. 21%) and lower power consumption (−81. 55%). Binarizing the hybrid network can further reduce the weights storage requirement by another 96. 875% with only 1. 518% accuracy loss. When the network weights are implemented with state of the art RRAM array (resistance variation is 5%), the proposed network can achieve a mean accuracy of 96. 47% with 95% ∼ 97. 5% confidence interval.

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