A proof of concept project was devised that produced an autonomous wireless mobile robot platform for sensing, locating, and inspecting a line of rivets. More, the robot system had to adhere to an aircraft surface when at a high degree of tilt angle, it needed to posses wall adhesion capabilities. Early on a decision was made to use the MSP430 MCU and the CRIM-mote design as the basic sensing and control technology on the robot system, since the CRIM is experienced with this processor and the learning curve was minimized by using this technology. Next, a literature search produced exampl
Wireless sensor networks (WSN’s) provide unprecedented spatial and temporal sensory resolution. The ubiquity of wireless sensor networks is made possible by their small size, Figure 1. These devices have remarkably low power consumption and once powered up, can operate without service for months, or years.
Congenital talipes equinovarus, commonly referred to as clubfoot, is a complex deformity that occurs in an otherwise normal child. It presents in utero bilaterally or unilaterally with the affected feet completely turned inward. Clubfoot is the seventh most common congenital birth defect, and the first most common musculoskeletal birth defect, occurring in about 150,000-200,000 babies each year worldwide. In addition to its congenital presentation, clubfoot can also accompany such disorders as Spina Bifida and Arthrogryposis . Despite extensive research, the etiopathogenesis of club
The first part of this project involves an investigation of current musculoskeletal modeling techniques and an attempt increase the accessibility and visibility of these techniques to clinical researchers. There are many software packages available for analyzing motion capture data, but a lot of researchers are forced to write their own software due to compatibility problems or prohibitive costs.
Personalized long-term health monitoring has the potential to improve medicine’s capabilities for diagnosing and correctly treating diseases at an early stage. Here, electronic textile based sensors were designed and fabricated to measure ECG and respiration. Recommendations are made for developing an unobtrusive, wireless, health monitoring garment. Wireless sensor networks (WSN’s) provide unprecedented spatial and temporal sensory resolution.
The goal of the research project is to increase the consistency and efficiency rates for the microinjection of embryonic stem cells into blastocysts through automation and development of an intelligent control strategy.
Evolutionary robotics (ER), a sub-area of cognitive robotics, employs population-based artificial evolution to develop behavioral robotics controllers. The research was conducted using the CRIM’s EvBot 1 and EvBot II colonies of autonomous robots, Figure 1. This research focuses on the formulation and application of a fitness selection function for ER that makes use of intra-population competitive selection, based on range sensor data, Figure 2, and genome mutation, Figure 3.
Current work focuses on determining methods of integrating multiple diverse sensors, and corresponding motor states, into a unified framework for learning and control. Initial implementation will be performed on the EvBot II robotic platform. Through effective sensorimotor integration, learning time will be reduced, and the learned control systems will exhibit more robustness of control and increased generalizability.
Our goal is to integrate circuits into nonwoven textiles to create textile printed circuit boards. This technology will be applied towards wearable computing applications like physiological monitoring garments, textile RFID, and human-robot distributed networks. Our research adopts the technologies used in the polymer thick film (PTF) industry and adapts and applies them to nonwoven textiles. Instead of weaving or knitting conductive yarns with fabrics, we are currently screen printing conductive inks onto nonwoven textile substrates.
The goal of this project is to establish and control formation movement of an EvBot II colony without explicitly stating which position each EvBot II is to maintain. Each EvBot II will compete for its position in the formation based on an internal heuristic that is a function of: (1) the location of the EvBot II, (2) the desired formation, (3) the proximity of the EvBot II to the formation configuration, and (4) an internal self-diagnostic control algorithm.