RESEARCH
Lookahead Navigation for High-Speed Mobile Robots
Students: Sterling Anderson and Steven Peters
Recent developments in both defense and commercial sectors have inspired a growing
interest in mobile robot navigation technologies. As look-ahead sensing capabilities
improve, mobile robots will be able to operate at higher speeds and in more varied
environments. This research aims to develop novel planning and control approaches
to meet the challenge.
Lookahead navigation project page
Omnidirectional Mobile Robots in Rough Terrain
Students: Genya Ishigami and Martin Udengaard
Mobile robots are finding increasing use in military, disaster recovery, and
exploration applications. These applications frequently require operation in
rough, unstructured terrain. This project focuses on the analysis, design, and
control of omnidirectional mobile robots for use in rough terrain. The robots in this study use active
split offset caster drive mechanisms that allow high thrust efficiency during
omnidirectional motion and low ground pressures over rough terrain. The design
guidelines developed in this research are scalable and applicable for a class of
omnidirectional mobile robots.
Omni-directional rough terrain project page
SQUISHBot
Students: Nadia Cheng
SQUISHBot (Soft QUIet Shape-shifting robot) is a soft meso-scale robot that can
climb walls, ceilings, and cross rough terrain. The robot is compliant and can morph, allowing
it to conform to irregular shapes and squeeze through holes much smaller than its nominal
cross-sectional area.
SQUISHBot project page
Terrain Sensing for Mobile Robots
Students: Chris Brooks
For mobile robots in rough terrain, the ability to safely traverse terrain is highly dependent on
mechanical properties of that terrain. For example, a robot may be able to climb up a rocky slope
with ease, but slide down a sandy slope the same grade. With mobile robots being employed for planetary
exploration and UGVs being developed for missions on Earth, the ability to predict these mechanical
terrain properties from a distance is becoming increasingly important.
Terrain Sensing Project Page
Mobility Prediction with Environmental Uncertainty
Students: Genya Ishigami and Gaurav Kewlani
The ability of autonomous unmanned ground vehicles to rapidly and effectively
predict terrain negotiability is a critical requirement for their use on
challenging terrain. Most of the work done on mobility prediction for such
vehicles, however, assumes precise knowledge about the vehicle/terrain
properties. In practical conditions though, uncertainties are associated with
the estimation of these parameters. This work focuses on developing efficient
methods that take into account environmental uncertainty while determining
vehicular mobility.
Mobility Prediction Project Page
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