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.
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.
Learn about the latest in Autonomous Cars through Professor Ryan Eustice’s guest lecture at the CMU Robotics Institute. In case you think that autonomous vehicles can just put-put around in slow moving city traffic, watch this amazing sequence on Ford Motor Company’s high-speed test track!