Research

Medical

Compliant magnetic devices can go places and reach autonomy that their more rigid mechanical cousins cannot. The M3Robotics lab is researching how to design and control highly flexible magnetic tools for both investigative and surgical use that can enable clinicians to have unprecedented reach and dexterity. Application areas range from neurosurgery to bronchoscopy.

Current Projects

Neurosurgical Electrode Guidance

neurosurgical electrode guidanceNon-invasive pharmacological treatments fail for some patients with brain-based neurological conditions, such as Parkinson’s disease, Tourette’s syndrome, epilepsy, and some forms of chronic pain. In these cases, the patient may opt for a neurosurgical solution. To address these conditions surgically, clinicians insert an electrode to stimulate a location deep within the patient’s brain.  Currently, there is no method to guide the electrode should it divert from the desired path. The goal of this project is to develop a system to actively and precisely guide an electrode to its target along any trajectory the clinician desires.

Sponsor: Boettcher Foundation

Past Projects

Cardiovascular Catheter Guidance

Cardiovascular Catheter GuidanceAtrial fibrillation is a condition that affects the ability of the heart to move blood efficiently.  To treat this condition, clinicians will ablate away areas within the heart to correct for abnormal neuro-signaling. Currently, this is performed using manually steered catheters with the patient and the doctor exposed to Fluoroscopic imaging X-Rays. The purpose of this project is to develop a closed-loop control method so that clinicians can guide ablation catheters precisely using magnetic fields.

Sponsor: European Research Council grant BOT-MED

Selected Publications

Magnetic

Magnetism enables engineers and scientists to effect objects without a mechanical connection. The M3Robotics Lab is researching how to design manipulation systems and use these systems to provide unprecedented control of both tethered and untethered objects. Projects range from fundamental investigations into materials to pushing the boundaries of what’s considered possible.

Mining

Underground environments offer many challenges to the men and women that work there. The M3Robotics Lab is researching how to bring autonomous platforms to reduce the risk to the workers and provide enhanced information to the mining companies. Projects range from autonomous navigation in underground environments to automated mineral detection.

Current Projects

Flying Underground

Drone in Edgar MineThe goal of this proof-of-concept project is to develop and demonstrate ground and air vehicles that are capable of autonomous navigation in the Edgar Experimental Mine. The primary focus will be on adapting a commercial UAV to autonomously fly in the confined space of an underground mine while building a 3D model of the mine as it travels and monitors key safety metrics such, as O2 levels. Such capability will provide many benefits to the safety and health of underground miners; for example, assessment of the risk of roof falls, the estimation of excavated volume, or assessment of ore pass blockage. Having an autonomous system perform these functions removes humans from these dangerous conditions. Once developed, this technology can be applied to other tasks such as inspection operations in open stopes, exploration of abandoned workings, and potentially search and rescue—where in the air vehicles search and ground vehicles rescue.

Sponsor: Alpha Foundation

NIOSH Smart Bit Project

Beam Acceleration response mode vs driving frequencyThe aim of this project is to develop a sensor framework that can be integrated into existing mining infrastructure to facilitate the estimation of rock strength parameters and ultimately, rock type. Sponsored by the National Institue of Occupational Safety and Health (NIOSH), this project will help mitigate the risk of roof collapse by alerting operators to changing mining conditions. Currently, this task is performed by experienced operators using audio-visual cues which is both difficult and subjective. Providing assistance to this operators in the form of objective measurements obtained from the machine/material interface will no doubt improve both safety and efficiency of these mines. This project will leverage analytical digging models developed as early as the 1960’s as well as modern Machine Learning techniques coupled with signal processing insight. This technology will be built on a general platform such that it can be adapted to many mining machines and force sensing applications.

Other

Current Projects

Robotic Motion and Calibration for Antenna Characterization

Antennas are a part of everyday life, allowing for the fast and convenient transfer of information in many applications. Understanding the properties of each antenna as manufactured requires high accuracy readings across a large number of volumetric points. While the positioning of sensors during this process has traditionally been accomplished with specialized rail systems, a transition is happening in the measurement community to robotic positioning systems to introduce versatility in pose selection and operation range. The Colorado School of Mines is collaborating with NIST in order to enhance the accuracy and motion planning capabilities of such systems with a focus on collaborative robot motion.

Sponsor: NIST

Soft Robotics Using Shape Memory Materials

The research is focused on modeling and 3D printing composites of Polyurethane (PU) based shape memory polymer (SMP) and NiTi shape memory alloy (SMA) wires for soft robotic applications using COMSOL Multiphysics. The composite is “trained” to undergo bending deformation upon Joule heating actuation. An inchworm inspired motion is mimicked to convert the bending deformations as useful locomotion. The thermo-mechanical constitutive model is implemented in COMSOL Multiphysics to study the complex deformation based on temperature and heat generation. The actuator design is optimized for Cost of transport (COT) using the Derivative-free Monte-Carlo method inbuilt in COMSOL Optimization module.