Robotic systems have benefitted from standardized middleware that can componentize the development of new capabilities for a robot. The popularity of these robotic middleware systems has resulted in sizable libraries of components that are now available to roboticists. However, many robotic systems (such as autonomous vehicles) must adhere to externally defined standards that do not contain a large repository of components. Due to the real-time and safety concerns that accompany the domain of unmanned systems, it is not trivial to interface these middleware systems. However, previous attempts to do so have succeeded at the cost of ad hoc design and implementation. This paper describes a domain-specific approach to the synthesis of a bridge between the popular Robotic Operating System (ROS) and the Joint Architecture for Unmanned Systems (JAUS). The domain-specific nature of the approach permits the bridge to be limited in scope by the application's specific messages (and their attribute mappings between JAUS/ROS), resulting in smaller code size and overhead than would be incurred by a generic solution. Our approach is validated by tests performed on an unmanned vehicle with and without the JAUS/ROS bridge.

This paper presents a framework for accurately and efficiently estimating a walking human’s trajectory using a computationally inexpensive non-Gaussian recursive Bayesian estimator. The proposed framework fuses global and inertial measurements with predictions from a kinematically driven step model to provide robustness in localization. A maximum a posteriori-type filter is trained on typical human kinematic parameters and updated based on live measurements. Local step size estimates are generated from inertial measurement units using the zero-velocity update (ZUPT) algorithm, while global measurements come from a wearable GPS. After each fusion event, a gradient ascent optimizer efficiently locates the highest likelihood of the individual’s location which then triggers the next estimator iteration.The proposed estimator was compared to a state-of-the-art particle filter in several Monte Carlo simulation scenarios, and the original framework was found to be comparable in accuracy and more efficient at higher resolutions. It is anticipated that the methods proposed in this work could be more useful in general real-time estimation (beyond just personal navigation) than the traditional particle filter, especially if the state is many-dimensional. Applications of this research include but are not limited to: in natura biomechanics measurement, human safety in manual fieldwork environments, and human/robot teaming.

No abstract

The purpose of this project is to safely integrate robots and humans into industrial processes. The most prevalent current solution to the problem of safe integration of robots and humans is to place the robots in cages to separate the workspaces of humans and robots. The cages prevent humans from entering the robot’s workspace and prevent any contact between the two entities. However, cages present an inefficiency in the industrial process as they require additional space and do not allow a seamless integration of robots and humans. This paper proposes a multi-tiered safety system that combines vision and torque feedback safety measures that can stop robot movement. The vision safety system proposed detects foreign movement in the camera frame and stops the robot’s motion. The torque system proposed detects unexpected torques in the robot’s motors and stops the robot’s motion. The results show that both safety systems can effectively stop robot motion if an unsafe condition is detected. For the industrial process of interest, the multi-tiered safety system is expected to lay the foundation for future integration of humans and robots on the industrial process. Contributions to the academic community for this paper are a multi-tiered safety system for robots in industrial processes, a machine learning circle detection algorithm, and a novel end-of-arm-tooling (EOAT) for the industrial process of interest.

No abstract