Development of a Virtual Collision Sensor for Industrial Robots

Collision detection is a fundamental issue for the safety of a robotic cell. While several common methods require specific sensors or the knowledge of the robot dynamic model, the proposed solution is constituted by a virtual collision sensor for industrial manipulators, which requires as inputs only the motor currents measured by the standard sensors that equip a manipulator and the estimated currents provided by an internal dynamic model of the robot (i.e., the one used inside its controller), whose structure, parameters and accuracy are not known. The collision detection is achieved by comparing the absolute value of the current residue with a time-varying, positive-valued threshold function, including an estimate of the model error and a bias term, corresponding to the minimum collision torque to be detected. The value of such a term, defining the sensor sensitivity, can be simply imposed as constant, or automatically customized for a specific robotic application through a learning phase and a subsequent adaptation process, to achieve a more robust and faster collision detection, as well as the avoidance of any false collision warnings, even in case of slow variations of the robot behavior. Experimental results are provided to confirm the validity of the proposed solution, which is already adopted in some industrial scenarios

Intent Classification during Human-Robot Contact

Robots are used in many areas of industry and automation. Currently, human safety is ensured through physical separation and safeguards. However, there is increasing interest in allowing robots and humans to work in close proximity or on collaborative tasks. In these cases, there is a need for the robot itself to recognize if a collision has occurred and respond in a way which prevents further damage or harm. At the same time, there is a need for robots to respond appropriately to intentional contact during interactive and collaborative tasks. This thesis proposes a classification-based approach for differentiating between several intentional contact types, accidental contact, and no-contact situations. A dataset is de- veloped using the Franka Emika Panda robot arm. Several machine learning algorithms, including Support Vector Machines, Convolutional Neural Networks, and Long Short-Term Memory Networks, are applied and used to perform classification on this dataset. First, Support Vector Machines were used to perform feature identification. Compar- isons were made between classification on raw sensor data compared to data calculated from a robot dynamic model, as well as between linear and nonlinear features. The results show that very few features can be used to achieve the best results, and accuracy is highest when combining raw data from sensors with model-based data. Accuracies of up to 87% were achieved. Methods of performing classification on the basis of each individual joint, compared to the whole arm, are tested, and shown not to provide additional benefits. Second, Convolutional Neural Networks and Long Short-Term Memory Networks were evaluated for the classification task. A simulated dataset was generated and augmented with noise for training the classifiers. Experiments show that additional simulated and augmented data can improve accuracy in some cases, as well as lower the amount of real- world data required to train the networks. Accuracies up to 93% and 84% we achieved by the CNN and LSTM networks, respectively. The CNN achieved an accuracy of 87% using all real data, and up to 93% using only 50% of the real data with simulated data added to the training set, as well as with augmented data. The LSTM achieved an accuracy of 75% using all real data, and nearly 80% accuracy using 75% of real data with augmented simulation data

Adaptive physical human-robot interaction (PHRI) with a robotic nursing assistant.

Recently, more and more robots are being investigated for future applications in health-care. For instance, in nursing assistance, seamless Human-Robot Interaction (HRI) is very important for sharing workspaces and workloads between medical staff, patients, and robots. In this thesis we introduce a novel robot - the Adaptive Robot Nursing Assistant (ARNA) and its underlying components. ARNA has been designed specifically to assist nurses with day-to-day tasks such as walking patients, pick-and-place item retrieval, and routine patient health monitoring. An adaptive HRI in nursing applications creates a positive user experience, increase nurse productivity and task completion rates, as reported by experimentation with human subjects. ARNA has been designed to include interface devices such as tablets, force sensors, pressure-sensitive robot skins, LIDAR and RGBD camera. These interfaces are combined with adaptive controllers and estimators within a proposed framework that contains multiple innovations. A research study was conducted on methods of deploying an ideal HumanMachine Interface (HMI), in this case a tablet-based interface. Initial study points to the fact that a traded control level of autonomy is ideal for tele-operating ARNA by a patient. The proposed method of using the HMI devices makes the performance of a robot similar for both skilled and un-skilled workers. A neuro-adaptive controller (NAC), which contains several neural-networks to estimate and compensate for system non-linearities, was implemented on the ARNA robot. By linearizing the system, a cross-over usability condition is met through which humans find it more intuitive to learn to use the robot in any location of its workspace, A novel Base-Sensor Assisted Physical Interaction (BAPI) controller is introduced in this thesis, which utilizes a force-torque sensor at the base of the ARNA robot manipulator to detect full body collisions, and make interaction safer. Finally, a human-intent estimator (HIE) is proposed to estimate human intent while the robot and user are physically collaborating during certain tasks such as adaptive walking. A NAC with HIE module was validated on a PR2 robot through user studies. Its implementation on the ARNA robot platform can be easily accomplished as the controller is model-free and can learn robot dynamics online. A new framework, Directive Observer and Lead Assistant (DOLA), is proposed for ARNA which enables the user to interact with the robot in two modes: physically, by direct push-guiding, and remotely, through a tablet interface. In both cases, the human is being “observed” by the robot, then guided and/or advised during interaction. If the user has trouble completing the given tasks, the robot adapts their repertoire to lead users toward completing goals. The proposed framework incorporates interface devices as well as adaptive control systems in order to facilitate a higher performance interaction between the user and the robot than was previously possible. The ARNA robot was deployed and tested in a hospital environment at the School of Nursing of the University of Louisville. The user-experience tests were conducted with the help of healthcare professionals where several metrics including completion time, rate and level of user satisfaction were collected to shed light on the performance of various components of the proposed framework. The results indicate an overall positive response towards the use of such assistive robot in the healthcare environment. The analysis of these gathered data is included in this document. To summarize, this research study makes the following contributions: Conducting user experience studies with the ARNA robot in patient sitter and walker scenarios to evaluate both physical and non-physical human-machine interfaces. Evaluation and Validation of Human Intent Estimator (HIE) and Neuro-Adaptive Controller (NAC). Proposing the novel Base-Sensor Assisted Physical Interaction (BAPI) controller. Building simulation models for packaged tactile sensors and validating the models with experimental data. Description of Directive Observer and Lead Assistance (DOLA) framework for ARNA using adaptive interfaces