Current projects

Rendering of the Magnetic Levitation Floor

The goal of this project is to levitate a group of robots in 3D space using electromagnetic energy. MagLev (magnetically levitated) robots, providing frictionless motions and precise motion control, have promising potential applications in many fields. Controlling magnetic levitation systems is not an easy task; therefore, designing a robust controller is crucial for accurate manipulations in the 3D space and to allow the robots to reach any desired location smoothly.

This project focuses on deploying a set of autonomous robots to efficiently service tasks that arrive sequentially in an environment over time. Each task is serviced when the robot visits the corresponding task location. Robots can then redeploy while waiting for the next task to arrive. The objective is to redeploy the robots taking into account the expected response time to service tasks that will arrive in the future.

Multicamera Cluster SLAM Visualization

This project brings together several novel components to help solve the problem of multi-camera SLAM with non-overlapping fields of view to generate relative pose estimation data. This includes the Multi-Camera Parallel Tracking and Mapping (MCPTAM) algorithm, as well as novel approaches to dealing with scale recovery and reducing degenerate motions for multi-camera SLAM.

Labeled Points in an Image Feed

Visual navigation algorithms pose many difficult challenges which must be overcome in order to achieve mass deployment. State-of-the-art methods are susceptible to error when measurements are noisy or corrupted, and prone to failure when the camera undergoes degenerate motions.

Example of Modified Motion Plan

While autonomous robots are finding increasingly widespread application, specifying robot tasks usually requires a high level of expertise.  In this work, the focus is on enabling a broader range of users to direct autonomous robots by designing human-robot interfaces that allow non-expert users to set up complex task specifications. To achieve this, we investigate how user preferences can be learned through human-robot interaction (HRI).