This project aims to detect and classify physical interaction between a human and the robot, and the human's intent behind this interaction. Examples of this include and accidental bump, helpful adjustments, or pushing the robot out of a dangerous situation.
By distinguishing between these cases, the robot can adjust its behaviour accordingly. The framework begins with collecting feedback from the robot's joint sensors, including position, speed, and torque. Additional features, such as model-based torque calculations and joint power, are then calculated.
This work aims to ease the implementation and reproducibility of human-robot collaborative assembly user studies. A software framework based on ROS is being implemented with four key modules: perception, decision, action and metrics.
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).