51 research outputs found
Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark
This paper presents Sim-Suction, a robust object-aware suction grasp policy
for mobile manipulation platforms with dynamic camera viewpoints, designed to
pick up unknown objects from cluttered environments. Suction grasp policies
typically employ data-driven approaches, necessitating large-scale,
accurately-annotated suction grasp datasets. However, the generation of suction
grasp datasets in cluttered environments remains underexplored, leaving
uncertainties about the relationship between the object of interest and its
surroundings. To address this, we propose a benchmark synthetic dataset,
Sim-Suction-Dataset, comprising 500 cluttered environments with 3.2 million
annotated suction grasp poses. The efficient Sim-Suction-Dataset generation
process provides novel insights by combining analytical models with dynamic
physical simulations to create fast and accurate suction grasp pose
annotations. We introduce Sim-Suction-Pointnet to generate robust 6D suction
grasp poses by learning point-wise affordances from the Sim-Suction-Dataset,
leveraging the synergy of zero-shot text-to-segmentation. Real-world
experiments for picking up all objects demonstrate that Sim-Suction-Pointnet
achieves success rates of 96.76%, 94.23%, and 92.39% on cluttered level 1
objects (prismatic shape), cluttered level 2 objects (more complex geometry),
and cluttered mixed objects, respectively. The Sim-Suction policies outperform
state-of-the-art benchmarks tested by approximately 21% in cluttered mixed
scenes.Comment: IEEE Transactions on Robotic
Development of a Shape Memory Polymer Soft Microgripper
The ability to control microrobots by means of magnetic fields has become of increasing interest to researchers. These robots’ ability to reach places tethered microrobots otherwise could not leads to many possible applications in the body, such as delivering drugs to targeted locations and performing biopsies. This study shows the use of shape memory polymer (SMP) to wirelessly actuate a microgripper to be used by a controllable microrobot to achieve these functions. Many smart materials were analyzed in order to find the material that most effectively would accomplish wirelessly gripping, manipulating, and releasing a microobject. Multiple microgripper designs were designed, analyzed, and constructed at a macroscale from acrylic, simulating a microscale counterpart. Simulated and experimental data were compared to determine the design that would require the least amount of inputted force and displacement from the SMP. This study shows a proposal for scaling this final design to the microscale involving experimentation with different forms of SMP in order to make the gripper actuatable in biologically relevant conditions. This technology could provide an inexpensive and effective solution for manipulating cells and other microobjects in vitro and in vivo
Improved Microrobotic Control through Image Processing and Automated Hardware Interfacing
Untethered submilliliter-sized robots (microrobots) are showing potential use in different industrial, manufacturing and medical applications. A particular type of these microrobots, magnetic robots, have shown improved performance in power and control capabilities compared to the other thermal and electrostatic based robots. However, the magnetic robot designs have not been assessed in a robust manner to understand the degree of control in different environments and their application feasibility. This research project seeks to develop a custom control software interface to provide a holistic tool for researchers to evaluate the microrobotic performance through advance control features. The software deliverable involved two main aspects: 1) Real-time microrobot detection and tracking through image processing, achieved through testing with different combinations of built-in tracking algorithms in OpenCV package, and 2) hardware interfacing with a microcontroller based coil control system through serial port communication for direct control of the magnetic coils. The robotic motion control was studied using error mode correction strategies to provide a robust, accurate and time efficient image stream based robotic controls. The user interface developed conducts change in brightness and rotation invariant tracking with an efficient speed of 12 frames per second and performs real-time calculation of robot’s position and orientation. It provides robust automatic control of directing microrobotic motion along the specific path waypoints entered on the images, through recursive serial bus communication. The project showcases the advanced importance and the powerful tool of image processing and microcontroller based communication in conducting the performance analysis of promising microrobotic designs
Micro-Manipulation Using Learned Model
Microscale devices can be found in applications ranging from sensors to structural components. The dominance of surface forces at the microscale hinders the assembly processes through nonlinear interactions that are difficult to model for automation, limiting designs of microsystems to primarily monolithic structures. Methods for modeling surface forces must be presented for viable manufacturing of devices consisting of multiple microparts. This paper proposes the implementation of supervised machine learning models to aid in automated micromanipulation tasks for advanced manufacturing applications. The developed models use sets of training data to implicitly model surface interactions and predict end-effector placement and paths that will yield a desired part trajectory. Conclusions and recommendations are based on evaluations of a collection of machine learning models and the effects of training data size and hyperparameter tuning on a collection of error metrics
U.S. General Population Estimate for “Excellent” to “Poor” Self-Rated Health Item
BACKGROUND: The most commonly used self-reported health question asks people to rate their general health from excellent to poor. This is one of the Patient-Reported Outcomes Measurement Information System (PROMIS) global health items. Four other items are used for scoring on the PROMIS global physical health scale. Because the single item is used on the majority of large national health surveys in the U.S., it is useful to construct scores that can be compared to U.S. general population norms. OBJECTIVE: To estimate the PROMIS global physical health scale score from the responses to the single excellent to poor self-rated health question for use in public health surveillance, research, and clinical assessment. DESIGN: A cross-sectional survey of 21,133 individuals, weighted to be representative of the U.S. general population. PARTICIPANTS: The PROMIS items were administered via a Web-based survey to 19,601 persons in a national panel and 1,532 subjects from PROMIS research sites. The average age of individuals in the sample was 53 years, 52 % were female, 80 % were non-Hispanic white, and 19 % had a high school degree or lower level of education. MAIN OUTCOME MEASURES: PROMIS global physical health scale. KEY RESULTS: The product–moment correlation of the single item with the PROMIS global physical health scale score was 0.81. The estimated scale score based on responses to the single item ranged from 29 (poor self-rated health, 2.1 SDs worse than the general population mean) to 62 (excellent self-rated health, 1.2 SDs better than the general population mean) on a T-score metric (mean of 50). CONCLUSIONS: This item can be used to estimate scores for the PROMIS global physical health scale for use in monitoring population health and achieving public health objectives. The item may also be used for individual assessment, but its reliability (0.52) is lower than that of the PROMIS global health scale (0.81)
Nicotine deprivation elevates neural representation of smoking-related cues in object-sensitive visual cortex: a proof of concept study
Flexible automation of micro and meso-scale manipulation tasks with applications to manufacturing & biotechnology
There are many challenges in robotics that need to be addressed in order to solve the meso, micro, and nano-scale manipulation and assembly problems that are becoming more and more important today. Traditionally, solutions for precision assembly have been highly customized and accomplished at the expense of system cost and complexity. More expensive sensors and actuators are used that drive up cost and reduce throughput. However, at smaller scales, often times sensors are either complex and expensive or non-existent. Furthermore, there is a lack of good contact interaction models at these scales making model-based approaches to control difficult. The work presented here investigates ways to solve these new manipulation and assembly challenges at these small scales. The contributions include the following: First, a novel, flexible automation system for micro and meso-scale manipulation tasks has been designed and implementation. Second, the development of a methodology for meso-scale manipulation tasks along with experimental validation of this methodology has been realized. The third contribution is the design, fabrication, and testing of a novel, two-dimensional, ÎĽN level force sensing device. Finally, the last contribution is the application of flexible automation technologies for increased biotechnology process throughput, analysis, and automation and performance of single-cell manipulations for characterization studies
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