Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems

We present a light-weight body-terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse-kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst-case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments

ESMD Space Grant Faculty Project

No abstract availabl

Bond graph model based control of robotic manipulators

The performance of robotic manipulators is critical to their widespread use in industry. As manipulators become faster, their potential productivity can rise thus improving the return on the investment required to purchase them. Improving accuracy, on the other hand, increases the range of tasks for which the manipulator is suitable. The speed and accuracy of a manipulator is partly determined by the capability of the algorithm used to control it. Whilst being a highly non-linear multiple input, multiple output device, however, most industrial controllers are derived on the basis that the robot is a series of independent, linear actuator+ link subsystems. The resulting independent joint controller is simple to design and implement but is limited in its performance as link interactions and the non-linear effects of centrifugal and Coriolis forces degrade the accuracy at high manipulator velocities. Improvements in the control of manipulators may be made by incorporating a mathematical model of the manipulator in the control algorithm. Control schemes such as `computed torque' incorporate an inverse model of the manipulator to calculate the input torques required to force the end-effector to follow a desired trajectory. The equations of motion required to implement these controllers are large and complex even for relatively simple manipulators. This thesis explores how bond graph representations of robotic manipulators may be used to automate the implementation of model based controllers. To provide a practical basis for this research the bond graph derived controllers are tested on an experimental rigid, planar, direct drive two-link manipulator. It is shown how the bondgraph for this manipulator, including d.c. motor actuators, can be constructed and used to derive the equations of motion of the manipulator automatically. The bond graph model is then validated by comparing simulations obtained using these equations of motion with experimental data