Reachability-based Trajectory Design for Bipedal Walking Robots
The goal of this project is to create a real-time path planner for bipedal robots Digit and Cassie, allowing them to travel autonomously and safely without collision with any obstacles. My main contributions are:
Utilized the idea of “template and anchor” to simplify the dynamics of bipedal robots Cassie and Digit by approximating the full-order model (anchor) with a reduced-order model (template).
Solved a sum-of-squares (SOS) optimization problem to outer approximate the forward reachable set (FRS) of the anchor model using the dynamics of the template model with the error between two models being conservatively considered.
Achieved real-time path planning by solving a nonlinear optimization problem inspired by Model Predictive Control (MPC).
Achieved collision-free navigation for Cassie in a simulated warehouse environment, and for Digit in our lab and outside around the Michigan Robotics building.
Since Digit’s low-level controller is proprietary, I am currently developing our own controller to enable Digit to navigate itself in more challenging scenarios View More
Simulation-based Localization and Navigation of Autonomous Surface Vehicle
Autonomous Surface Vehicle (ASVs) have extensive application potentials in unmanned hydrographic surveys and marine operations. However, traditional path-following controllers of ASVs may not detect obstacles on the water's surface, such as floating buoys and reefs, which can create serious safety risks. I created a fast, reliable sensing system to address this issue. To accurately locate obstacles on the water's surface, I implemented a sensor fusion method. Rather than the traditional template matching method, I created a novel bag-of-visual-words model to classify obstacles, which significantly improved the speed of the perception algorithm. Eventually, I created an algorithm for safe navigation and applied it to detect an underwater target.
Mobile Robot Localization using Kalman Filter and Particle Filter
The goal of this project was to do localization for the mobile robot PR2 on the OpenRAVE using different filters, evaluate and compare their performance in terms of the robustness in the environment with obstacles and noise. I created algorithms for filters, robot dynamic functions, sensors functions and path planning. To evaluate the performance of my algorithms, I conducted experiments on several interesting scenarios and different maps, including an open area environment, an environment with obstacles, and an office-like environment. I found that the Extended Kalman Filter (EKF) was more efficient than the Particle filter while the Particle filter performed more robust to the highly nonlinear dynamics and complex environments with many obstacles.
Patient Outcomes Prediction Using Privacy-Preserving Machine Learning
In this project, I created machine learning classifiers for predicting patient outcomes in the peritoneal dialysis (PD) treatment. The dataset that I used in this work was originated from the BRAZPD database. While handling the dataset, I created algorithms for feature selection, data balancing, feature clustering and averaging. I tested the performance of XGBoost model and neural network model that were used for classification. The accuracy of classifiers was quantified by F1 score. From results, important features that have obvious influence on the outcomes were identified.
Monocular Image Depth Prediction via Encoder-Decoder-based Deep Learning
The goal of this project was to conduct depth prediction for monocular camera image. We achieved this task via building two deep neural networks based on the DenseNet and MiniNet. NYUv2 dataset was utilized for our training and evaluation. The performance of the networks was evaluated in both qualitative and quantitative ways.