Professor Kenn Oldham and his team’s most recent work on robotics is tied to the attached series of related articles regarding testing piezoelectric/polymer small-scale robots along with a bit of battery modeling motivated by micro-robotic needs.
Article: Polymer Leg Mechanisms for Millimeter-scale Robotics
Article: Microrobot Locomotion
Article: Dynamic Structural and Contact Modeling for a Silicon Hexapod Microrobot
U-M Robotics Professor and Director of Michigan Robotics Jessy Grizzle has long said that his work in robotics could one day be used to help the disabled. Read on!
Dr. Jason Corso and Dr. Brent Griffin are extending prior work in bottom-up video segmentation to include depth information from RGBD video, which allows us to better train for specific tasks and adaptively update representations of objects in complex environments. For robotics applications, we are incorporating this into a framework that guides the processing of RGBD video using a kinematic description of a robot’s actions, thereby increasing the quality of observations while reducing the overall computational costs. Using kinematically-guided RGBD video, we are able to provide feedback to a robot in real-time to: identify task failure, detect external objects or agents moving into a workspace, and develop a better understanding of objects while interacting them.
“We are thrilled to become part of the ACM family of journals,” explained THRI Co-Editor-in-Chief Odest Chadwicke Jenkins of the University of Michigan. “ACM’s reputation as a publisher of computing research is unparalleled. At the same time, the broad representation of computing disciplines in the ACM, the organization’s global reach, and platforms such as the Digital Library, are a perfect complement to our own goals for THRI.
Jenkins, along with Co-Editor-in-Chief Selma Šabanović of Indiana University, have set three primary goals for the journal in the coming years, including: 1) Sustaining the intellectual growth of HRI as a field of study (both quantitatively and qualitatively), 2) Enabling timely and productive feedback from readers, and 3) Cultivating new and leading-edge ideas in both robotics and the human-centered sciences
The inaugural issue of the rebranded ACM Transactions on Human-Robot Interaction (THRI) is planned for March 2018. Those seeking to submit for the publication, or who have questions for the editors, are encouraged to visit the current HRI Journal website.
The full article.
The concept of “formal methods”, also know as “correct by construction” is applied to the trajectory planning for mobile robots/small autonomous vehicles. The main challenges are (i) the presence of multiple moving objects (pedestrians, other robots/vehicles), and (ii) plant uncertainties. We aim to address both in our research.
The example publications show a formal design process to deal with multiple moving objects (without considering plant uncertainties). Our method achieves better balance between safety (zero collision!) and performance (the robot does not keep stopping to avoid collisions) compared with other methods from the literature.
Mr. Yuxiao Chen is a U-M graduate student co-advised by Professors Huei Peng and Jessy Grizzle.
Michigan Robotics is proud to highlight that one of its founding members, Prof. Ed Olson, has made the inaugural list of the Google Scholar “Classic Papers That Have Stood The Test of Time.” You can find Ed’s paper listed here, and general background for this list is given here.
If you check out Ed on his website, you’ll find that he is withstanding the test of time rather well himself.
For her outstanding work in hybrid systems, a theoretical area very important to robotics, Professor Necmiye Ozay has received a major best paper award. The details of her award are here. Necmiye’s work on Correct-by-Design Control Software Synthesis is aimed at breaking down the barriers that have prevented this field from tackling important industrial problems. In the paper, she and her co-author develop finite abstractions that are equipped with robustness margins, allowing sensing and model imperfections to be addressed in a formally correct manner. They apply the results to Adaptive Cruise Control, an important Automated Drive Assist System, and point out other important applications in robotics and autonomous vehicles.
In dynamic environments crowded with people, robot motion planning becomes difficult due to the complex and tightly-coupled interactions between agents. Trajectory planning methods, supported by models of typical human behavior and personal space, often produce reasonable behavior. However, they do not account for the future closed loop interactions of other agents with the trajectory being constructed. As a consequence, the trajectories are unable to anticipate cooperative interactions (such as a human yielding), or adverse interactions (such as the robot blocking the way). We propose a new method – Multi-Policy Decision Making (MPDM) for navigation amongst pedestrians in which the trajectory of the robot is not explicitly planned, but instead, a planning process selects one of a set of closed-loop behaviors whose utility can be predicted through forward simulation.
Here is the MPDM presentation by Dhanvin Mehta, Gonzalo Ferrer and Edwin Olson, U-M, Ann Arbor.
Kevin French, a Ph.D. student in robotics, demonstrates a robotic arm, controlled with a Wii controller, that his group built for the Robotics Day technology showcase that took place Tuesday at the North Campus Research Complex. The event showcases state-of-the-art robotic technologies and educational efforts from universities, school districts, industry and government agencies across Michigan. (Photo by Akhil Kantipuly, College of Engineering)
CSE graduate students Qi Zhang and Shun Zhang will present exciting research papers at ICAPS 2017, the 27th International Conference on Automated Planning and Scheduling, taking place this June at Carnegie Mellon University in Pittsburgh PA.
Here are the papers:
Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making
Approximately-Optimal Queries for Planning in Reward-Uncertain Markov Decision Processes