Publications

To the best of our knowledge, it is the first study that employs the Graph Neural Network (GNN) to solve the forward kinematics (FK) problem for Cable-Driven Parallel Robots (CDPRs).

A soft tactile sensor w/ a two-layered weaved polymer-based optical fiber anisotropic structure embedded in a soft elastomer.

In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning.

Detection of buried objects beneath granules in advance based on the study of proximity sensing.

In this letter, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDA for short), for the model predictive control (MPC) based motion planning problem.

GCCD-PT: a continuous collision detection method that works in UAVs/AGVs/CDPRs.

In this letter, we propose a distributed approach, RL-RVO, for multi-robot navigation combining reciprocal velocity obstacle (RVO) and deep reinforcement learning (DRL) to solve the reciprocal collision avoidance problem under limited information.

A centralized planner w/ low computational burden to schedule multiple AGVs/mobile robots at the intersection of polynomial curves.

A ray-based method to check interference-free condition (IFC) and wrench-closure condition (WCC) along the given polynomial paths for cable-driven parallel robots (CDPRs).

This thesis studies the interference-free workspace (IFW) of arbitrary cable-driven robots (CDRs) for both cables and obstacles using ray-based method, followed by the point-to-point path planning and verification technique to ensure that the resulting motion is within the workspace.