An Overview of Kinematic and Calibration Models Using Internal/External Sensors or Constraints to Improve the Behavior of Spatial Parallel Mechanisms

This paper presents an overview of the literature on kinematic and calibration models of parallel mechanisms, the influence of sensors in the mechanism accuracy and parallel mechanisms used as sensors. The most relevant classifications to obtain and solve kinematic models and to identify geometric and non-geometric parameters in the calibration of parallel robots are discussed, examining the advantages and disadvantages of each method, presenting new trends and identifying unsolved problems. This overview tries to answer and show the solutions developed by the most up-to-date research to some of the most frequent questions that appear in the modelling of a parallel mechanism, such as how to measure, the number of sensors and necessary configurations, the type and influence of errors or the number of necessary parameters

On neuromechanical approaches for the study of biological and robotic grasp and manipulation

abstract: Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.The electronic version of this article is the complete one and can be found online at: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-017-0305-

Techniques For Sensor-Integrated Robotic Systems: Raman Spectra Analysis, Image Guidance, And Kinematic Calibration

Robotics and sensor technology have made impressive advancements over the years. There are now robotic systems that help perform surgeries or explore the surface of Mars, and there are sensors that detect trace amounts of explosives or identify diseased human tissue. The most powerful systems integrate robots and sensors, which are natural complements to each other. Sensors can provide information that might otherwise be unavailable due to indirect robotic manipulation (e.g., images of the target environment), and robots can provide suitably precise positioning of an analytical sensor. To have an effective sensor-integrated robotic system, multiple capabilities are needed in the areas of sensors, robotics, and techniques for robot/sensor integration. However, for many types of applications, there are shortcomings in the current technologies employed to provide these capabilities. For the analysis of complex sensor signals, there is a need for improved algorithms and open platforms that enable techniques to be shared. For the path planning and tracking of integrated sensors and the visualization of collected information, image guidance systems that support advanced analytical sensors would be very beneficial. For robotic placement of a sensor, easily usable calibration procedures and methods to overcome limited feedback could help improve the accuracy. To help address these issues, some novel systems and techniques were developed in this research. First, a software system was created to process, analyze, and classify data from a specific kind of sensor (a Raman spectrometer). The system is open and extensible, and it contains novel techniques for processing and analyzing the sensor data. Second, an image guidance system was made for use with a sensor-integrated robotic system (a Raman probe attached to a surgical system). The system supports tool tracking, sensor activation, real-time sensor data analysis, and presentation of the results in a 3D computer visualization of the environment. Third, a kinematic calibration technique was developed for serial manipulators. It requires no external measurement devices for calibration, provides solutions for some limitations of existing techniques, and can significantly enhance the positional accuracy of a robot to improve sensor placement. The implemented techniques and systems were successfully evaluated using various data sets and conditions. Together, the contributions of this work provide important building blocks for an accurate robot with an integrated analytical sensor. This type of a system would be a powerful tool for many future applications, such as a surgical robot that automatically scans for diseased tissue and assists the surgeon in the necessary treatment. Ultimately, this work is intended to foster the development of advanced sensor-integrated robotic systems