Announcements

Title: Physics-Based and Data-Driven Modeling of Hybrid Robot Movement on Soft Terrain

Author: Guanjin Wang

Advisory Committee:

• Professor Balakumar Balachandran, Chair & Advisor
• Associate Professor Amir Riaz (Co-Advisor)
• Professor Teng Li
• Professor Amr Baz
• Professor Peter Chung
• Professor Derek Richardson (Dean’s Representative)

Date & Time: November 13, 2020 2pm-4pm

Abstract: Navigating the unmapped environment is one of the ten biggest challenges facing the robotics community. A vision-based navigation system embedded in the mobile robot can only help to negotiate obstacles, which are well described by geometrical features, like sharp-edged stones and rocks.  Other aspects like sand, snow, and challenging terrains, are challenges for motions that robots cannot avoid during missions. Thus, designing and selecting effective gaits to navigate over terrains that may not be well describable by geometry is crucial for robot exploration. Wheeled robots can move fast on flat surfaces but suffer from loss of traction and immobility on soft ground. However, legged machines have superior mobility over wheeled locomotion when they are in motion over flowable ground or a terrain with obstacles but can only move at relatively low speeds on flat surfaces. A question is: If legged and wheeled locomotion are combined, can the resulting hybrid leg-wheel locomotion enable fast movement in any terrain condition?

Investigations into vehicle terrain interaction fall in the area of terramechanics. Traditional terra-mechanics theory can help capture large wheel vehicle interaction with the ground. However, legged or hybrid locomotion on a granular substrate is difficult to investigate by using classical empirical terra-mechanics theory due to sharp-edge contact. Recent studies show the continuum simulation can serve as an accurate tool for simulating dynamic interactions with granular material at laboratory and field scales. Therefore, to investigate the rich physics during dynamic interactions between the robot and the granular terrain, a computational framework based on the Smooth particle hydrodynamics (SPH) method has been developed and validated by using experimental results for single robot appendage interaction with the granular system. This framework has been extended and coupled with a multi-body simulator to model different robot configurations. Encouraging agreement is found amongst the numerical, theoretical, and experimental results, for a wide range of robot leg configurations, such as curvature and shape. The sensitive dependence of robot performance on different gaits has been investigated by parametric space exploration.

The above mentioned physics-based simulation can serve as a high-fidelity tool to uncover clues about the underlying mechanism of dynamic interactions between robots and soft terrain. However, real-time navigation in a challenging terrain requires fast prediction of the dynamic response of the robot, which is useful for terrain identification and robot gait adaption. Therefore, a data-driven modeling framework has also been developed for the fast estimation of the slippage and sinkage of robots. The data-driven model leverages the high-quality data generated from the offline physics-based simulation for the training of a deep neural network founded on long short-term memory (LSTM) cells. The results are expected to form a good basis for online robot navigation and exploration in unknown and complex terrains.

Title: Reduction of Mixture Property Variation Through Control on Initial Mixing Dynamics

Author: Marcelo Arispe-Guzman

Date/Time: November 20, 2020 9:00am-11:00am

Examining Committee:
Professor David Bigio, Chair
Professor Balakumar Balachandran
Professor Peter Chung
Professor Ryan Sochol

Zoom Link: https://umd.zoom.us/j/5898885184

Abstract: Blend homogenization of a liquid-solid mixtures is achieved through mixer agitation which disperses the liquids and breaks up the agglomerates. Creating energetic or pharmaceutical blends requires a very low degree of mixture variation in the final product. Initial solid-liquid feeding protocols into the mixer greatly affect the ability to achieve low variation at minimal energy input. Experiments in a vertically oscillating mixer using dyed silicon oil and glass beads examined the effect of feed protocols, while varying acceleration and the number of cycles. A Central Composite Design (CCD) DOE revealed that the percent homogeneity and coefficient of variation measures of mixing are linearly dependent on acceleration and number of cycles. Experimental observations lead us to redefine the model for breakup of wet agglomerates. This study offers a starting point to developing feed protocols to improve the efficiency of oscillating mixers, such as the resonant acoustic mixer (RAM), for liquid-solid mixing.

Title: INTERFACE OF IN-SITU ADDITIVE MANUFACTURING AND PHASE CHANGE LIQUID METALS TO OPTIMIZE HIGH THERMAL DENSITY PROBLEM IN SPACECRAFT AVIONICS STRUCTURES

Author: Jerald Armen

Date/Time: November 6, 2020 – 1:00pm-3:00pm

Zoom Link: https://umd.zoom.us/j/6589422509?pwd=em1PbXRVRTNvaldSVkNHejl0NFRFUT09

Examining Committee:

• Professor Hugh Bruck, Chair
• Professor Abhijit Dasgupta 
• Professor David Bigio
• Professor Ryan Sochol
• Professor Kyu Yong Choi (Dean’s Representative)   

Title: Phospholipid Behavior and Dynamics in Curved Biological Membranes

Author: Haoyuan Jing

Date/Time: November 6, 2020 11:00am-1:00pm

Zoom Link: https://umd.zoom.us/j/2663589029

Committee Members
Dr. Siddhartha Das (Chair)
Dr. Silvina Matysiak (Dean’s Represenative)
Dr. Doron Levy
Dr. Amir Riaz
Dr. Peter Chung
Dr. Kumaran Ramamurthi (Special Member)

Abstract: Curvature in biological membranes defines the morphology of cells and organelles and serves key roles in maintaining a variety of cellular functions, enabling trafficking, recruiting and localizing shape-responsive proteins. For example, the bacterial protein SpoVM is a small amphipathic alpha-helical protein that localizes to the outer surface of a forespore, the only convex surface in the mother bacteria. Understanding several of these membrane curvature dependent events rely on a thorough understanding of the properties, energetics, and interactions of the constituent lipid molecules in presence of curvatures.  

In this dissertation, we have used molecular dynamics (MD) simulations to explore how the curvature of the lipid bilayer (LBL), a simplified mimic of the cell membrane, affects the packing fraction and diffusivity of lipid molecules in the LBL, energetics of lipid flip flop in the LBL, and lipid desorption from the LBLs. We have also investigated the interaction between LBLs and a small bacterial protein, SpoVM, which was previously shown to preferentially embed in positively curved membranes. Our work started with simulating convex surface, represented by the nanoparticle supported lipid bilayers (NPSLBLs) in MD. We first quantified the self-assembly, structure, and properties of a NPSLBL with a diameter of 20 nm and showed how the type of the nanoparticle (NP) affects the properties of the NPSLBLs. Second, we studied the energetics of lipid flip flop and desorption from LBLs for the cases of planar substrate supported lipid bilayer (PSSLBL) and NPSLBL. Finally, we investigated the energetics of SpoVM desorption from the PSSLBL and the NPSLBL providing clues to the fundamental driving forces dictating the curvature sensing of SpoVM.