Mathematical foundations and principles of processing sensor information in robotic systems. Topics include an introduction to probabilistic concepts related to sensors, sensor signal processing, multi-sensor control systems and optimal estimation. The material presented will focus on the types of control problems encountered when a robot must operate in an environment where sensor noise and/or tracking errors are significant. Techniques for assessing the stability, controllability and expected accuracy of multi-sensor control and tracking systems will be presented. Lab projects will involve processing live and synthetic data, robot simulation, and projects involving the control of robot platforms. (Prerequisites: Differential Equations (MA 2051 or equivalent), Linear Algebra (MA 2071 or equivalent) and the ability to program in a high-level language.)
Foundations and principles of robotic manipulation. Topics include computational models of objects and motion, the mechanics of robotic manipulators, the structure of manipulator control systems, planning and programming of robot actions. The focus of this class is on the kinematics and programming of robotic mechanisms. Important topics also include the dynamics, control, sensor and effector design, and automatic planning methods for robots. The fundamental techniques apply to arms, mobile robots, active sensor platforms, and all other computer-controlled kinematic linkages. The primary applications include robotic arms and mobile robots and lab projects would involve programming of representative robots. An end of term team project would allow students to program robots to participate in challenges or competitions. (Prerequisite: RBE 500 or equivalent.)
This course demonstrates the synergy between the control theory and robotics through applications and provides an in-depth coverage of control of manipulators and mobile robots. Topics may include kinematic and dynamic models, trajectory and motion planning, feedback control, compliance and force control, impedance control, control of redundant manipulators, control of underactuated robots, adaptive robot control, integrated force and motion control, digital implementation of control laws, model identification and parameter estimation techniques. Course projects will emphasize modeling, simulation and practical implementation of control systems for robotic applications. (Prerequisites: Linear algebra; Differential equations; Linear systems and control theory as in ECE 504 or consent of the instructor.)
This course covers the foundation and principles of multi-robot systems. The course will cover the development of the field and provide an overview on different control architectures (deliberative, reactive, behavior-based and hybrid control), control topologies, and system configurations (cellular automata, modular robotic systems, mobile sensor networks, swarms, heterogeneous systems). Topics may include, but are not limited to, multi-robot control and connectivity, path planning and localization, sensor fusion and robot informatics, task-level control, and robot software system design and implementation. These topics will be pursued through independent reading, class discussion, and a course project. The course will culminate in a group project focusing on a collaborative/cooperative multi-robot system. The project may be completed through simulation or hands-on experience with available robotic platforms. Groups will present their work and complete two professional-quality papers in IEEE format. (Prerequisites: Linear algebra, differential equations, linear systems, controls, and mature programming skills, or consent of the instructor.) Students cannot receive credit for this course if they have taken the Special Topics (ME 593S) version of the same course.
This course focuses on human-robot interaction and social robot learning, exploring the leading research, design principles and technical challenges we face in developing robots capable of operating in real-world human environments. The course will cover a range of multidisciplinary topics, including physical embodiment, mixed-initiative interaction, multi-modal interfaces, human-robot teamwork, learning algorithms, aspects of social cognition, and long-term nteraction. These topics will be pursued through independent reading, class discussion, and a final project. (Prerequisites: Mature programming skills and at least undergraduate level knowledge of Artificial Intelligence, such as CS 4341. No hardware experience is required.)
This course introduces an approach to robotics called Sensitive Robotics. This approach allows robots to perform complex tasks by using large array of sensors that provide information relevant to the task at hand. The course studies the hardware and software implications of this approach. At the hardware level, we discuss the mechanical and electrical characteristic of the sensors and actuators, the design consideration of arms and limbs, and the hardware architecture alternatives. At the software level, we discuss the implications that the hardware changes have in the software architecture, and the control algorithms. Machine learning techniques, needed to deal with large array of sensors, are also covered. The case of robotic manipulation (sensitive manipulation) is introduced as an example of this approach and it is expanded to walking, flying and swimming robots. (Prerequisites: RBE 500)
This course examines current issues in the computer implementation of visual perception. Topics include image formation, edge detection, segmentation, shape-from-shading, motion, stereo, texture analysis, pattern classification and object recognition. We will discuss various representations for visual information, including sketches and intrinsic images. (Prerequisites: CS 534, CS 543, CS 545, or the equivalent of one of these courses.)
This course will provide an overview of a multitude of biomedical applications of robotics. Applications covered include: image-guided surgery, percutaneous therapy, localization, robot-assisted surgery, simulation and augmented reality, laboratory and operating room automation, robotic rehabilitation, and socially assistive robots. Specific subject matter includes: medical imaging, coordinate systems and representations in 3D space, robot kinematics and control, validation, haptics, teleoperation, registration, calibration, image processing, tracking, and human-robot interaction. Topics will be discussed in lecture format followed by interactive discussion of related literature. The course will culminate in a team project covering one or more of the primary course focus areas. Recommended background: Linear algebra, ME/ RBE 501 or equivalent. Students cannot receive credit for this course if they have taken the Special Topics (ME 593U) version of the same course.
Arranged by individual faculty with special expertise, these courses survey fundamentals in areas that are not covered by the regular Robotics Engineering course offerings. Exact course descriptions are disseminated by the Robotics Engineering Program well in advance of the offering. (Prerequisite: Consent of instructor. See the Supplement section of the on-line catalog at www.wpi. edu/+gradcat for descriptions of courses to be offered each academic year.)