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Minh Q. Tram

dblp:348/6366 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Systems, architecture and hardware · 2 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
robot programming
0.712023
Intuitive Robot Integration via Virtual Reality Workspaces · ICRA 2023
Virtual and augmented reality
immersive interaction
0.212023
Intuitive Robot Integration via Virtual Reality Workspaces · ICRA 2023

Methods — techniques the papers use, named apart from their topics

virtual reality simulation · 1.3
YearPublicationVenuePosition
2023 Intuitive Robot Integration via Virtual Reality Workspaces
abstract
As robots become increasingly prominent in di-verse industrial settings, the desire for an accessible and reliable system has correspondingly increased. Yet, the task of meaningfully assessing the feasibility of introducing a new robotic component, or adding more robots into an existing infrastructure, remains a challenge. This is due to both the logistics of acquiring a robot and the need for expert knowledge in setting it up. In this paper, we address these concerns by developing a purely virtual simulation of a robotic system. Our proposed framework enables natural human-robot interaction through a visually immersive representation of the workspace. The main advantages of our approach are the following: (i) independence from a physical system, (ii) flexibility in defining the workspace and robotic tasks, and (iii) an intuitive interaction between the operator and the simulated environment. Not only does our system provide an enhanced understanding of 3D space to the operator, but it also encourages a hands-on way to perform robot programming. We evaluate the effectiveness of our method in applying novel automation assignments by training a robot in virtual reality and then executing the task on a real robot.
Minh Q. Tram, Joseph M. Cloud, William J. Beksi
ICRA1
2023 Lunar Excavator Mission Operations Using Dynamic Movement Primitives
abstract
To support sustainable infrastructure on the Moon, NASA must leverage robots to extract lunar resources for in-situ processing and construction. As part of this effort, NASA is launching the in-situ resource utilization (ISRU) Pilot Excavator later this decade to validate a robotic regolith excavator based on the Regolith Advanced Surface Systems Operations Robot (RASSOR). RASSOR is designed to extract and transport regolith to meet the needs of ISRU architectures. During its mission, Pilot Excavator will be tasked with driving in test patterns to demonstrate the operational concept. One possible test pattern is a circular trajectory around the lander while avoiding surface hazards such as lunar rocks. To this end, we utilize dynamic movement primitives to represent navigation sequences as primitive trajectories. We introduce a novel obstacle avoidance parameter, which is configured to avoid rocks throughout testing exercises. We demonstrate the effectiveness our method in a newly developed simulation tool called the Simulated Excavation Environment for Lunar Operations (SEELO) using models based on the NASA RASSOR 2.0 excavator. Our results show that the robot is able to safety and robustly navigate the lunar surface with densely populated rock obstacles while retaining the desired circle pattern behavior.
Joseph M. Cloud, Minh Q. Tram, William J. Beksi, Michael A. DuPuis
IROS2