As robotics continues to integrate into society, systems and interactions become increasingly complex. To be able to accurately model such systems, a method is needed that can scale with this complexity, but that can also be standardized. This paper proposes the use of the Systems Modeling Language, or SysML, as such a modeling approach. Based on the popular UML standard, and designed for systems engineering, SysML can scale up for complex systems and is already an industry standard for modeling systems. In this paper we introduce SysML, compare SysML to other formal modeling languages, and discuss its benefits and drawbacks as a modeling language for robotic systems. We also provide an example of how SysML can be used to model robot manipulation tasks.

In this report, we present a robotic sample introduction/ionization system for mass spectrometry (MS) for spot analysis and imaging of non-planar surfaces. The system operates by probing the sample surface with an acupuncture needle, followed by direct plasma chemical ionization time-of-flight MS.

Robust methods for representing, generalizing, and sharing knowledge across dierent robotic systems and congurations are important in many domains of robotics research and application. In this paper we present a framework for capturing robot capability and process specication to simplify the sharing and reuse of knowledge between robots in manufacturing environments. A SysML model is developed that represents knowledge about system capabilities in the form of simple skills and skill primitives that can be used in dierent situations or contexts. We present a discussion of the form this model takes and advantages of this type of representation, as well as a demonstration of how the model can be applied to dierent assembly tasks.

In this paper we present the idea that by using AI planning in concert with formal task modeling, the overhead associated with plan creation for complex tasks can be reduced. The proposed approach uses a SysML taxonomy to model the system capabilities and the process specification, and the PDDL planning language to determine acceptable objective solutions. This idea is applied to the manufacturing domain, and examples are shown modeling a multi-robot system in an automobile manufacturing environment. A discussion is given regarding the merits of the demonstrated approach.

High precision tasks are an important part of the manufacturing industry. For example, safety constraints require that some manufacturing must be done to a very high degree of accuracy. Robotics and automation are well suited for such tasks, as there is high repeatability on specialized repetitive tasks. Robotics has a long history in manufacturing in the form of industrial automation. Yet these robotic systems tend to be very large, restricting their use to tasks in open spaces that are easily accessible to the robot. In the last few years there have been a number of advances in the area of small, light weight robots. One such robot is the KUKA Light-Weight Robot (LBR) [1]. With the availability of smaller robot manipulators, the question arises of whether it would be feasible to use them in tasks that would otherwise be unsuitable for the standard large industrial robots. Possible tasks would be those in tight or constrained spaces, such as in sub-structure drilling. For this to be possible, the light weight robot would need to be stiff enough to be able to meet safety constraints. To that end, this study is to determine whether the dynamic characteristics, specifically stiffness, of small light weight robots would make it possible for them to be used in manufacturing. Two light weight robots are considered in this study, the LBR and the KUKA KR5 sixx.

Personal service robots will need to understand semantic object relationships and task context in order to assist humans in their everyday lives. This paper will demonstrate a technique using keywords, spatial relationships, colors, and other contextual information to assist in the mobile manipulation and object recognition tasks. Preliminary results using a mobile manipulation platform are also presented.

We present a mobile manipulation system used by the Georgia Tech team in the RoboCup@Home 2010 competition. An overview of the system is provided, including the approach taken for manipulation, SLAM, object detection, object recognition, and system integration. We focus on our manipulation strategy, which utilizes a low-degree of freedom manipulator and makes use of the robot’s differential drive as part of the manipulation strategy. Empirical results demonstrating our platform’s ability to detect and grasp a variety of tabletop objects are presented.