Human-Inspired Autonomous Vehicle Highway Navigation
Leads: Junghee Park and Alexandre Constantin
Sponsors: Nissan, US Army Research Office

The focus of this research is to design active navigation systems for autonomous or semi-autonomous vehicles based on the notion of "fields of safe travel" or "homotopy classes". The motivation behind this approach is the assumption that human drivers tend to operate vehicles within a field of safe travel rather than rigidly follow a specific path. In other words, humans usually make decisions about high-level avoidance strategies before determining a specific path of travel.

Human-Inspired Autonomous Navigation project page

Terramechanics for Small, Lightweight, Robots
Lead: Carmine Senatore
Sponsors: Washington University, NASA, US Army TARDEC, US Army Research Office

Understanding the traction mechanics of manned or robotic vehicles running on deformable terrain is a crucial aspect of vehicle design, analysis, and simulation. A vehicle's ability to negotiate soft soil has strong implications for both its power efficiency and mobility. A key application of this work is the small wheeled rovers currently exploring the surface of Mars.

Terramechanics is the engineering science that studies the interaction between vehicles and deformable terrain. However, classical terramechanics methods were primarily developed for large, heavy (>2000lb) vehicles, and were not originally intended for application to small, lightweight robots. For vehicles in this class, discrepancies have been noted between predictions based on Bekker theory and experimental tests. This research aims to develop improved terramechanics models for small, lightweight ground vehicles.

Terramechanics for small robots project page

Classification and Modeling of Forested Terrain from Unmanned Ground Vehicles
Leads: Matt Mcdaniel, Takayuki Nishihata, Shengyan Zhou and Phil Salesses
Sponsors: US Army ERDC

To operate autonomously, unmanned ground vehicles (UGVs) must be able to identify the load-bearing surface of the terrain (i.e. the ground) and obstacles. Current classification, modeling and navigation techniques work well for structured environments such as urban areas, where there are roads and obstacles that are usually predictable and well-defined. However, autonomous navigation in forested terrain presents many new challenges due to the variability and lack of structure in natural environments. This work aims to develop methods for identifying obstacles in densely forested terrain, and accurately localizing a vehicle in the absence of GPS information.

Forested Terrain Classification Project Page

Design and Characterization of Efficient Micro-Scale Pumps
Leads: Youzhi Liang
Sponsors: DARPA

In large scale hydraulic systems, individual component efficiencies (pumps, valves, etc.) are reasonably good when operating under optimal conditions. However, these efficiencies decrease dramatically as components are scaled down, owing to viscous and volumetric losses. The goal of this work is to address inefficiencies associated with small-scale hydraulic systems.




Trajectory Tracking Control for Front-steered Ground Vehicles
Leads: Steven Peters

The ability to follow a desired trajectory is an important part of many autonomous vehicle navigation and hazard avoidance systems. An important requirement for trajectory tracking controllers is appropriate consideration of the vehicle dynamics, especially with regard to wheel slip. When wheel slip is small, the vehicle dynamics are greatly simplified. When wheel slip does occur, however, it can cause a loss of control.

Though wheel slip can lead to a loss of control for average drivers, expert drivers are able to precisely control vehicles around obstacles even with large amounts of skidding and wheel slip. This research aims to apply a similar level of control expertise towards compensating for wheel slip while tracking trajectories in the presence of obstacles.

Trajectory tracking control project

Leads: Nadia Cheng and Nick Wiltsie

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


Lookahead Navigation for High-Speed Mobile Robots
Leads: 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
Lead: 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

Terrain Sensing for Mobile Robots
Lead: 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
Leads: 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