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With the increased influence of automation into every part of our lives, tomorrow’s engineers must be capable working with autonomous systems. The explosion of automation and robotics has created a need for a massive increase in engineers who possess the skills necessary to work with twenty-first century systems. Autonomous Systems (MEEM4707) is a new senior/graduate level elective course with goals of: 1) preparing the next generation of skilled engineers, 2) creating new opportunities for learning and well informed career choices, 3) increasing confidence in career options upon graduation, and 4) connecting academic research to the students world. Presented in this paper is the developed curricula, key concepts of the project-based approach, and resources for other educators to implement a similar course at their institution. In the course, we cover the fundamentals of autonomous robots in a hands-on manner through the use of a low-cost mobile robot. Each student builds and programs their own robot, culminating in operation of their autonomous mobile robot in a miniature city environment. The concepts covered in the course are scalable from middle school through graduate school. Evaluation of student learning is completed using pre/post surveys, student progress in the laboratory environment, and conceptual examinations.

Navigation in pedestrian environments is critical to enabling independent mobility for the blind and visually impaired (BVI) in their daily lives. White canes have been commonly used to obtain contact feedback for following walls, curbs, or man-made trails, whereas guide dogs can assist in avoiding physical contact with obstacles or other pedestrians. However, the infrastructures of tactile trails or guide dogs are expensive to maintain. Inspired by the autonomous lane following of self-driving cars, we wished to combine the capabilities of existing navigation solutions for BVI users. We proposed an autonomous, trail-following robotic guide dog that would be robust to variances of background textures, illuminations, and interclass trail variations. A deep convolutional neural network (CNN) is trained from both the virtual and realworld environments. Our work included major contributions: 1) conducting experiments to verify that the performance of our models trained in virtual worlds was comparable to that of models trained in the real world; 2) conducting user studies with 10 blind users to verify that the proposed robotic guide dog could effectively assist them in reliably following man-made trails.

In recent years, population aging becomes a serious problem. To decrease the demand for labor when navigating visitors in museums, exhibitions, or libraries, this research designs an automatic museum robotic guide which integrates image and gesture recognition technologies to enhance the guided tour quality of visitors. The robot is a self-propelled vehicle developed by ROS (Robot Operating System), in which we achieve the automatic driving based on the function of lane-following via image recognition. This enables the robot to lead guests to visit artworks following the preplanned route. In conjunction with the vocal service about each artwork, the robot can convey the detailed description of the artwork to the guest. We also design a simple wearable device to perform gesture recognition. As a human machine interface, the guest is allowed to interact with the robot by his or her hand gestures. To improve the accuracy of gesture recognition, we design a two phase hybrid machine learning-based framework. In the first phase (or training phase), k-means algorithm is used to train historical data and filter outlier samples to prevent future interference in the recognition phase. Then, in the second phase (or recognition phase), we apply KNN (k-nearest neighboring) algorithm to recognize the hand gesture of users in real time. Experiments show that our method can work in real time and get better accuracy than other methods.

Modern autonomous systems such as driverless vehicles need to safely operate in a wide range of conditions. A potential solution is to employ a hybrid systems approach, where safety is guaranteed in each individual mode within the system. This offsets complexity and responsibility from the individual controllers onto the complexity of determining discrete mode transitions. In this work we propose an efficient framework based on recursive neural networks and coreset data summarization to learn the transitions between an arbitrary number of controller modes that can have arbitrary complexity. Our approach allows us to efficiently gather annotation data from the large-scale datasets that are required to train such hybrid nonlinear systems to be safe under all operating conditions, favoring underexplored parts of the data. We demonstrate the construction of the embedding, and efficient detection of switching points for autonomous and non-autonomous car data. We further show how our approach enables efficient sampling of training data, to further improve either our embedding or the controllers.

The decentralized trading market approach, where both autonomous agents and people can consume and produce services expanding own opportunities to reach goals, looks very promising as a part of the Fourth Industrial revolution. The key component of the approach is a blockchain platform that allows an interaction between agents via liability smart contracts. Reliability of a service provider is usually determined by a reputation model. However, this solution only warns future customers about an extent of trust to the service provider in case it could not execute any previous liabilities correctly. From the other hand a blockchain consensus protocol can additionally include a validation procedure that detects incorrect liability executions in order to suspend payment transactions to questionable service providers. The paper presents the validation methodology of a liability execution for agent-based service providers in a decentralized trading market, using the Model Checking method based on the mathematical model of finite state automata and Temporal Logic properties of interest. To demonstrate this concept, we implemented the methodology in the Duckietown application, moving an autonomous mobile robot to achieve a mission goal with the following behavior validation at the end of a completed scenario.