arXiv subjects

Peihan Zhang

Publications and source records attributed to Peihan Zhang.

7 recordsLinked to original sources

arXiv2026

LapSurgie: Humanoid Robots Performing Surgery via Teleoperated Handheld Laparoscopy

Robotic laparoscopic surgery has gained increasing attention in recent years for its potential to deliver more efficient and precise minimally invasive procedures. However, adoption of surgical robotic platforms remains largely confined to high-resource medical centers, exacerbating healthcare disparities in rural and low-resource regions. To close this gap, a range of solutions has been explored, from remote mentorship to fully remote telesurgery. Yet, the practical deployment of surgical robotic systems to underserved communities remains an unsolved challenge. Humanoid systems offer a promising path toward deployability, as they can directly operate in environments designed for humans without extensive infrastructure modifications -- including operating rooms. In this work, we introduce LapSurgie, the first humanoid-robot-based laparoscopic teleoperation framework. The system leverages an inverse-mapping strategy for manual-wristed laparoscopic instruments that abides to remote center-of-motion constraints, enabling precise hand-to-tool control of off-the-shelf surgical laparoscopic tools without additional setup requirements. A control console equipped with a stereo vision system provides real-time visual feedback. Finally, a comprehensive user study across platforms demonstrates the effectiveness of the proposed framework and provides initial evidence for the feasibility of deploying humanoid robots in laparoscopic procedures.

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arXiv2026

In vivo feasibility study of humanoid robots in surgery

Recent advances in actuation, control and learning have rapidly pushed humanoid robots from a distant vision towards near-term real-world deployment. Healthcare is a particularly pressing domain, in which staffing shortages and increasing care demand are widening the gap between clinical workload and available skilled labour. Although current automation has largely focused on digital and logistical tasks, much hospital work remains embodied, requiring mobility, manipulation and safe interaction in human-designed environments. Humanoid form factors offer unique potential, particularly for assisting with surgical tasks. Traditionally, robotic systems for surgery are purpose-built platforms such as Intuitive Surgical's da Vinci Surgical System, and it remains unclear how close current humanoid systems are to meeting the precision, control and safety requirements of minimally invasive surgery. Here we present a systematic evaluation of contemporary humanoid technology for laparoscopic surgical tasks. We develop a humanoid-based laparoscopic teleoperation framework using general-purpose instruments and assess its abilities through benchtop characterization, dry-laboratory user studies spanning diverse surgical experience levels and in vivo porcine studies. Across these evaluations, we quantify technical feasibility, task performance and clinical readiness relative to established surgical platforms. Together, our study provides an evidence-based assessment of current humanoid abilities and limitations for surgical applications, highlighting both their promise and key technical challenges that must be addressed before clinical deployment.

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arXiv2025

Towards Autonomous Tape Handling for Robotic Wound Redressing

Chronic wounds, such as diabetic, pressure, and venous ulcers, affect over 6.5 million patients in the United States alone and generate an annual cost exceeding \$25 billion. Despite this burden, chronic wound care remains a routine yet manual process performed exclusively by trained clinicians due to its critical safety demands. We envision a future in which robotics and automation support wound care to lower costs and enhance patient outcomes. This paper introduces an autonomous framework for one of the most fundamental yet challenging subtasks in wound redressing: adhesive tape manipulation. Specifically, we address two critical capabilities: tape initial detachment (TID) and secure tape placement. To handle the complex adhesive dynamics of detachment, we propose a force-feedback imitation learning approach trained from human teleoperation demonstrations. For tape placement, we develop a numerical trajectory optimization method based to ensure smooth adhesion and wrinkle-free application across diverse anatomical surfaces. We validate these methods through extensive experiments, demonstrating reliable performance in both quantitative evaluations and integrated wound redressing pipelines. Our results establish tape manipulation as an essential step toward practical robotic wound care automation.

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arXiv2025

Dexterous Control of an 11-DOF Redundant Robot for CT-Guided Needle Insertion With Task-Oriented Weighted Policies

Computed tomography (CT)-guided needle biopsies are critical for diagnosing a range of conditions, including lung cancer, but present challenges such as limited in-bore space, prolonged procedure times, and radiation exposure. Robotic assistance offers a promising solution by improving needle trajectory accuracy, reducing radiation exposure, and enabling real-time adjustments. In our previous work, we introduced a redundant robotic platform designed for dexterous needle insertion within the confined CT bore. However, its limited base mobility restricts flexible deployment in clinical settings. In this study, we present an improved 11-degree-of-freedom (DOF) robotic system that integrates a 6-DOF robotic base with a 5-DOF cable-driven end-effector, significantly enhancing workspace flexibility and precision. With the hyper-redundant degrees of freedom, we introduce a weighted inverse kinematics controller with a two-stage priority scheme for large-scale movement and fine in-bore adjustments, along with a null-space control strategy to optimize dexterity. We validate our system through both simulation and real-world experiments, demonstrating superior tracking accuracy and enhanced manipulability in CT-guided procedures. The study provides a strong case for hyper-redundancy and null-space control formulations for robot-assisted needle biopsy scenarios.

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arXiv2025

Performance-Barrier Event-Triggered PDE Control of Traffic Flow

For stabilizing stop-and-go oscillations in traffic flow by actuating a variable speed limit (VSL) at a downstream boundary of a freeway segment, we introduce event-triggered PDE backstepping designs employing the recent concept of performance-barrier event-triggered control (P-ETC). Our design is for linearized hyperbolic Aw-Rascle-Zhang (ARZ) PDEs governing traffic velocity and density. Compared to continuous feedback, ETC provides a piecewise-constant VSL commands-more likely to be obeyed by human drivers. Unlike the existing regular ETC (R-ETC), which enforces conservatively a strict decrease of a Lyapunov function, our performance-barrier (P-ETC) approach permits an increase, as long as the Lyapunov function remains below a performance barrier, resulting in fewer control updates than R-ETC. To relieve VSL from continuously monitoring the triggering function, we also develop periodic event-triggered (PETC) and self-triggered (STC) versions of both R-ETC and P-ETC. These are referred to as R/P-PETC and R/P-STC, respectively, and we show that they both guarantee Zeno-free behavior and exponential convergence in the spatial $L^2$ norm. With comparative simulations, we illustrate the benefits of the performance-barrier designs through traffic metrics (driver comfort, safety, travel time, fuel consumption). The proposed algorithms reduce discomfort nearly in half relative to driver behavior without VSL, while tripling the driver safety, measured by the average dwell time, relative to the R-ETC frequent-switching VSL schedule.

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arXiv2022

Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Obstacle avoidance of quadrotors in dynamic environments is still a very open problem. Current works commonly leverage traditional static maps to represent static obstacles and the detection and tracking of moving objects (DATMO) method to model dynamic obstacles separately. The detection module requires pre-training, and the dynamic obstacles can only be modeled with certain shapes, such as cylinders or ellipsoids. This work utilizes the dual-structure particle-based (DSP) dynamic occupancy map to represent the arbitrary-shaped static obstacles and dynamic obstacles simultaneously, and proposes an efficient risk-aware sampling-based local trajectory planner to realize safe flights in this map. The trajectory is planned by sampling motion primitives generated in the state space. Each motion primitive is divided into two phases: a short-term phase with a strict risk limitation and a relatively long-term phase designed to avoid high-risk regions. The risk is evaluated with the predicted particle-form future occupancy status, considering the time dimension. With an approach to split from and merge to an arbitrary global trajectory, the planner can also be used in the tasks with preplanned global trajectories. Comparison experiments show that the obstacle avoidance system composed of the DSP map and our planner performs the best in dynamic environments. In real-world tests, our quadrotor reaches a speed of 6 m/s with the motion capture system and 2.5 m/s with everything running on a low-price single-board computer.

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arXiv2021

Agile Formation Control of Drone Flocking Enhanced with Active Vision-based Relative Localization

The vision-based relative localization can provide effective feedback for the cooperation of aerial swarm and has been widely investigated in previous works. However, the limited field of view (FOV) inherently restricts its performance. To cope with this issue, this letter proposes a novel distributed active vision-based relative localization framework and apply it to formation control in aerial swarms. Inspired by bird flocks in nature, we devise graph-based attention planning (GAP) to improve the observation quality of the active vision in the swarm. Then active detection results are fused with onboard measurements from Ultra-WideBand (UWB) and visual-inertial odometry (VIO) to obtain real-time relative positions, which further improve the formation control performance of the swarm. Simulations and experiments demonstrate that the proposed active vision system outperforms the fixed vision system in terms of estimation and formation accuracy.

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