Author name cluster

Ling Xu

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6 papers

2 author rows

JBHI Journal 2025 Journal Article

A Semi-supervised Ultrasound Image Segmentation Network Integrating Enhanced Mask Learning and Dynamic Temperature-controlled Self-distillation

Ling Xu
Yian Huang
Haoran Zhou
Qichao Mao
Wenjing Yin

Ultrasound imaging is widely used in clinical practice due to its advantages of no radiation and real-time capability. However, its image quality is often degraded by speckle noise, low contrast, and blurred boundaries, which pose significant challenges for automatic segmentation. In recent years, deep learning methods have achieved notable progress in ultrasound image segmentation. Nonetheless, these methods typically require large-scale annotated datasets, incur high computational costs, and suffer from slow inference speeds, limiting their clinical applicability. To overcome these limitations, we propose EML-DMSD, a novel semi-supervised segmentation network that combines Enhanced Mask Learning (EML) and Dynamic Temperature-Controlled Multi-Scale Self-Distillation (DMSD). The EML module improves the model's robustness to noise and boundary ambiguity, while the DMSD module introduces a teacher-free, multi-scale self-distillation strategy with dynamic temperature adjustment to boost inference efficiency and reduce reliance on extensive resources. Experiments on multiple ultrasound benchmark datasets demonstrate that EML-DMSD achieves superior segmentation accuracy with efficient inference, highlighting its strong generalization ability and clinical potential.

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IJCAI Conference 2021 Conference Paper

Evolutionary Gradient Descent for Non-convex Optimization

Ke Xue
Chao Qian
Ling Xu
Xudong Fei

Non-convex optimization is often involved in artificial intelligence tasks, which may have many saddle points, and is NP-hard to solve. Evolutionary algorithms (EAs) are general-purpose derivative-free optimization algorithms with a good ability to find the global optimum, which can be naturally applied to non-convex optimization. Their performance is, however, limited due to low efficiency. Gradient descent (GD) runs efficiently, but only converges to a first-order stationary point, which may be a saddle point and thus arbitrarily bad. Some recent efforts have been put into combining EAs and GD. However, previous works either utilized only a specific component of EAs, or just combined them heuristically without theoretical guarantee. In this paper, we propose an evolutionary GD (EGD) algorithm by combining typical components, i. e. , population and mutation, of EAs with GD. We prove that EGD can converge to a second-order stationary point by escaping the saddle points, and is more efficient than previous algorithms. Empirical results on non-convex synthetic functions as well as reinforcement learning (RL) tasks also show its superiority.

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ICRA Conference 2011 Conference Paper

An efficient algorithm for environmental coverage with multiple robots

Ling Xu
Anthony Stentz

Tasks such as street mapping and security surveillance seek a route that traverses a given space to perform a function. These task functions may involve mapping the space for accurate modeling, sensing the space for unusual activity, or searching the space for an object. In many cases, the use of multiple robots can greatly improve the performance of these tasks. We assume a prior map is available, but it may be inaccurate due to factors such as occlusion, age, dynamic objects, and resolution limitations. In this work, we address the NP-hard problem of environmental coverage with incomplete prior map information using k robots. To utilize related algorithms in graph theory, we represent the environment as a graph and model the coverage problem as a k-Rural Postman Problem. Using this representation, we present a graph coverage approach for plan generation that can handle graph changes online. Our approach proposes two improvements to an existing heuristic algorithm for the coverage problem. Our improvements seek to equalize the length of the k paths by minimizing the length of the maximum tour. We evaluate our approach on a set of comparison tests in simulation.

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IROS Conference 2011 Conference Paper

Market-based coordination of coupled robot systems

Ling Xu
Anthony Stentz

Tasks such as street mapping and security surveillance seek a route that traverses a given space to perform a function. These task functions may involve mapping the space for accurate modeling, sensing the space for unusual activity, or searching the space for an object. In many cases, the use of multiple robots can greatly improve the performance of these tasks. We assume a prior map is available, but it may be inaccurate due to factors such as occlusion, age, dynamic objects, and resolution limitations. In this work, we address the NP-hard problem of environmental coverage with incomplete prior map information using multiple robots. To utilize related algorithms in graph theory, we represent the environment as a graph and model the coverage problem as a k-Rural Postman Problem where k represents the number of robots. Using this representation, the problem can be solved using a branch-and-bound approach to find an optimal route, and a route division heuristic to separate the route into k pieces. Since the branch-and-bound technique is exponential time, we present an approach to decompose the search problem into subtasks that are distributed among the robots. Using ideas from market-based approaches, we allow the robots to auction particular sections of the problem space to other robots as a way to more evenly divide the work and focus the search. Finally, we evaluate these methods on test graphs in simulation.

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ICRA Conference 2010 Conference Paper

Human-guided grasp measures improve grasp robustness on physical robot

Ravi Balasubramanian
Ling Xu
Peter D. Brook
Joshua R. Smith 0001
Yoky Matsuoka

Humans are adept at grasping different objects robustly for different tasks. Robotic grasping has made significant progress, but still has not reached the level of robustness or versatility shown by human grasping. It would be useful to understand what parameters (called grasp measures) humans optimize as they grasp objects, how these grasp measures are varied for different tasks, and whether they can be applied to physical robots to improve their robustness and versatility. This paper demonstrates a new way to gather human-guided grasp measures from a human interacting haptically with a robotic arm and hand. The results revealed that a human-guided strategy provided grasps with higher robustness on a physical robot even under a vigorous shaking test (91%) when compared with a state-of-the-art automated grasp synthesis algorithm (77%). Furthermore, orthogonality of wrist orientation was identified as a key human-guided grasp measure, and using it along with an automated grasp synthesis algorithm improved the automated algorithm's results dramatically (77% to 93%).

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IROS Conference 2008 Conference Paper

Blended local planning for generating safe and feasible paths

Ling Xu
Anthony Stentz

Many planning approaches adhere to the two-tiered architecture consisting of a long-range, low fidelity global planner and a short-range high fidelity local planner. While this architecture works well in general, it fails in highly constrained environments where the available paths are limited. These situations amplify mismatches between the global and local plans due to the smaller set of feasible actions. We present an approach that dynamically blends local plans online to match the field of global paths. Our blended local planner generates paths from control commands to ensure the safety of the robot as well as achieve the goal. Blending also results in more complete plans than an equivalent unblended planner when navigating cluttered environments. These properties enable the blended local planner to utilize a smaller control set while achieving more efficient planning time. We demonstrate the advantages of blending in simulation using a kinematic car model navigating through maps containing tunnels, cul-de-sacs, and random obstacles.

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