arXiv subjects

Fanxin Wang

Publications and source records attributed to Fanxin Wang.

7 recordsLinked to original sources

arXiv2026

A Deployable Bio-inspired Compliant Leg Design for Enhanced Leaping in Quadruped Robots

Quadruped robots are becoming increasingly essential for various applications, including industrial inspection and catastrophe search and rescue. These scenarios require robots to possess enhanced agility and obstacle-navigation skills. Nonetheless, the performance of current platforms is often constrained by insufficient peak motor power, limiting their ability to perform explosive jumps. To address this challenge, this paper proposes a bio-inspired method that emulates the energy-storage mechanism found in froghopper legs. We designed a Deployable Compliant Leg (DCL) utilizing a specialized 3D-printed elastic material, Polyether block amide (PEBA), featuring a lightweight internal lattice structure. This structure functions analogously to biological tendons, storing elastic energy during the robot's squatting phase and rapidly releasing it to augment motor output during the leap. The proposed mechanical design significantly enhances the robot's vertical jumping capability. Through finite element analysis (FEA) and experimental validation, we demonstrate a relative performance improvement of 17.1% in vertical jumping height.

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arXiv2026

Sampling Strategy Design for Model Predictive Path Integral Control on Legged Robot Locomotion

Model Predictive Path Integral (MPPI) control has emerged as a powerful sampling-based optimal control method for complex, nonlinear, and high-dimensional systems. However, directly applying MPPI to legged robotic systems presents several challenges. This paper systematically investigates the role of sampling strategy design within the MPPI framework for legged robot locomotion. Based upon the idea of structured control parameterization, we explore and compare multiple sampling strategies within the framework, including both unstructured and spline-based approaches. Through extensive simulations on a quadruped robot platform, we evaluate how different sampling strategies affect control smoothness, task performance, robustness, and sample efficiency. The results provide new insights into the practical implications of sampling design for deploying MPPI on complex legged systems.

eess.SY↗

arXiv2025

Needle Biopsy And Fiber-Optic Compatible Robotic Insertion Platform

Tissue biopsy is the gold standard for diagnosing many diseases, involving the extraction of diseased tissue for histopathology analysis by expert pathologists. However, this procedure has two main limitations: 1) Manual sampling through tissue biopsy is prone to inaccuracies; 2) The extraction process is followed by a time-consuming pathology test. To address these limitations, we present a compact, accurate, and maneuverable robotic insertion platform to overcome the limitations in traditional histopathology. Our platform is capable of steering a variety of tools with different sizes, including needle for tissue extraction and optical fibers for vibrational spectroscopy applications. This system facilitates the guidance of end-effector to the tissue and assists surgeons in navigating to the biopsy target area for multi-modal diagnosis. In this paper, we outline the general concept of our device, followed by a detailed description of its mechanical design and control scheme. We conclude with the validation of the system through a series of tests, including positioning accuracy, admittance performance, and tool insertion efficacy.

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arXiv2025

DBaS-Log-MPPI: Efficient and Safe Trajectory Optimization via Barrier States

Optimizing trajectory costs for nonlinear control systems remains a significant challenge. Model Predictive Control (MPC), particularly sampling-based approaches such as the Model Predictive Path Integral (MPPI) method, has recently demonstrated considerable success by leveraging parallel computing to efficiently evaluate numerous trajectories. However, MPPI often struggles to balance safe navigation in constrained environments with effective exploration in open spaces, leading to infeasibility in cluttered conditions. To address these limitations, we propose DBaS-Log-MPPI, a novel algorithm that integrates Discrete Barrier States (DBaS) to ensure safety while enabling adaptive exploration with enhanced feasibility. Our method is efficiently validated through three simulation missions and one real-world experiment, involving a 2D quadrotor and a ground vehicle navigating through cluttered obstacles. We demonstrate that our algorithm surpasses both Vanilla MPPI and Log-MPPI, achieving higher success rates, lower tracking errors, and a conservative average speed.

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arXiv2025

MPPI-DBaS: Safe Trajectory Optimization with Adaptive Exploration

In trajectory optimization, Model Predictive Path Integral (MPPI) control is a sampling-based Model Predictive Control (MPC) framework that generates optimal inputs by efficiently simulating numerous trajectories. In practice, however, MPPI often struggles to guarantee safety assurance and balance efficient sampling in open spaces with the need for more extensive exploration under tight constraints. To address this challenge, we incorporate discrete barrier states (DBaS) into MPPI and propose a novel MPPI-DBaS algorithm that ensures system safety and enables adaptive exploration across diverse scenarios. We evaluate our method in simulation experiments where the vehicle navigates through closely placed obstacles. The results demonstrate that the proposed algorithm significantly outperforms standard MPPI, achieving a higher success rate and lower tracking errors.

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arXiv2024

Transformer Based Tissue Classification in Robotic Needle Biopsy

Image-guided minimally invasive robotic surgery is commonly employed for tasks such as needle biopsies or localized therapies. However, the nonlinear deformation of various tissue types presents difficulties for surgeons in achieving precise needle tip placement, particularly when relying on low-fidelity biopsy imaging systems. In this paper, we introduce a method to classify needle biopsy interventions and identify tissue types based on a comprehensive needle-tissue contact model that incorporates both position and force parameters. We trained a transformer model using a comprehensive dataset collected from a formerly developed robotics platform, which consists of synthetic and porcine tissue from various locations (liver, kidney, heart, belly, hock) marked with interaction phases (pre-puncture, puncture, post-puncture, neutral). This model achieves a significant classification accuracy of 0.93. Our demonstrated method can assist surgeons in identifying transitions to different tissues, aiding surgeons with tissue awareness.

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arXiv2024

An Optimization-Based Planner with B-spline Parameterized Continuous-Time Reference Signals

For the cascaded planning and control modules implemented for robot navigation, the frequency gap between the planner and controller has received limited attention. In this study, we introduce a novel B-spline parameterized optimization-based planner (BSPOP) designed to address the frequency gap challenge with limited onboard computational power in robots. The proposed planner generates continuous-time control inputs for low-level controllers running at arbitrary frequencies to track. Furthermore, when considering the convex control action sets, BSPOP uses the convex hull property to automatically constrain the continuous-time control inputs within the convex set. Consequently, compared with the discrete-time optimization-based planners, BSPOP reduces the number of decision variables and inequality constraints, which improves computational efficiency as a byproduct. Simulation results demonstrate that our approach can achieve a comparable planning performance to the high-frequency baseline optimization-based planners while demanding less computational power. Both simulation and experiment results show that the proposed method performs better in planning compared with baseline planners in the same frequency.

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