Robot Inverse Kinematics & Motion Planning
Fast inverse kinematics and trajectory planning for redundant and hyper-redundant manipulators, with multi-solution generation and constraint-aware selection.
Research Overview
My research connects mechanics with AI — using generative and surrogate models to accelerate robot motion solving and engineering structural optimization. Below are my focus areas and specific projects.
Focus Areas
Research Interests
Generative Models for Engineering
Applying conditional normalizing flows and other deep generative models to physical and mechanical problems where one input admits many valid solutions.
Surrogate Modeling & Structural Optimization
Kriging and data-driven surrogates that replace expensive finite-element simulation, combined with intelligent optimization for multi-objective lightweight design.
Mechanics Simulation & FEA
Parametric modeling and automated simulation with Abaqus, ANSYS, and SolidWorks, including Python-driven secondary development for batch data generation.
Experience
Research Experience
- Oct 2024 – Present
Fast Inverse Kinematics and Trajectory Planning for an 8-DOF Redundant Manipulator · Core Researcher
- Built a conditional normalizing-flow generative model for an 8-DOF hybrid-joint redundant manipulator that solves inverse kinematics for arbitrary end-effector poses in milliseconds and returns multiple feasible joint solutions at once.
- Modeled the kinematics of a hybrid (revolute + prismatic) redundant arm and trained a generative IK network that produces multiple solutions per query.
- Designed a constraint-based selection scheme to pick the optimal solution under joint limits, obstacle avoidance, and motion continuity.
- Refined generated solutions with an improved optimization algorithm to reach industrial-grade end-effector accuracy.
- Completed Cartesian- and joint-space trajectory planning for smooth, continuous, and efficient motion control.
- Nov 2024 – Jun 2025
Simulation and Multi-Objective Optimization of a Pole Waist-Ring Structure · Core Researcher
- Used Python-driven Abaqus secondary development for parametric modeling and batch simulation, built a Kriging surrogate to replace costly finite-element analysis, and applied intelligent optimization for multi-objective lightweight design.
- Parametrized the waist-ring model through the Python–Abaqus scripting interface and auto-generated multiple simulation datasets, replacing repetitive manual modeling.
- Trained a Kriging surrogate model to approximate FEA responses at very low computational cost, greatly improving optimization efficiency.
- Applied intelligent optimization to the surrogate for multi-objective design, obtaining an optimal waist-ring sizing that balances strength, stiffness, and weight.
- Jul 2025 – Present
Lightweight Redesign of a Power-Tower Lifting-Frame Mechanism · Structural Design & Simulation
- Redesigned a power-tower lifting frame by replacing the conventional rope-lifting scheme with a lead-screw mechanism, achieving an integrated and lightweight structure verified by finite-element analysis.
- Proposed an integrated redesign that replaces rope lifting with a lead-screw drive, effectively reducing the mechanism's volume and weight.
- Completed motor selection and lifting-platform structural design following engineering steel-selection standards, with 3D modeling in SolidWorks.
- Performed stress and displacement analysis of key load-bearing structures in ANSYS to verify that the design meets strength and stiffness requirements.