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.
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.
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.
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).
The goal of this work is to develop methods and tools which will enable reliable dynamic balance and gait for bipedal robots. Currently, the focus of this project is on the development and application of methods for quantifying and measuring various kinematic and dynamic properties of the robot which are critical to dynamic bipedal gait and balance.
This work presents a path following controller for a quadrotor vehicle. A smooth, dynamic, feedback controller is designed that allows the quadrotor to follow both closed and non-closed embedded curves while maintaining a desired speed, a desired acceleration or while stabilizing desired points along the curves.
The main motivation for this project is to improve the model of dynamical systems, beyond the physics-based models, by incorporating measurements of the model outputs/states in the modelling process. This method of modelling is called Grey-Box which employs a White-Box model (physics-based model) in conjunction with a Black-Box model (purely based on data).