Advanced Algorithms and Controls for Superior Robotic All-Terrain Mobility
Leaders: Karl Iagnemma, Ramon Gonzalez, ProtoInnovations, LLC
Our aim is to provide a novel approach for enhancing rover traction and reducing wheel slip that will not only
allow rovers to autonomously detect and avoid hazardous terrain regions, but also to travel with assured safety on
terrain that is steeper and rougher than is currently possible. The result of this work will be systems that can rove with a reduced risk of
catastrophic failure, while simultaneously increasing both the quantity and potential quality of science data products.
Retractable Robotic Anchor for Hard Rock and Granular Soils
Leaders: ProtoInnovations, LLC, Karl Iagnemma, Ramon Gonzalez
Development of an innovative retractable robotic anchor that works in hard rock and granular soils
permitting anchoring and subsequent repositioning of a lander, rover, or other equipment. In this regard,
Resistance Force Theory is being used for modeling the forces dealing with the anchor-soil interaction.
Next-Generation NATO Reference Mobility Model. Uncertainty Treatment
Leaders: Karl Iagnemma and Ramon Gonzalez
Sponsors: US Army TARDEC, US Army Research Office
This project is focused on a simple and efficient methodology to generate a mobility map accounting
for two sources of uncertainty, namely measurement errors (RMSE of a Digital Elevation Model) and interpolation
error (kriging method). This methodology means a general-purpose solution since it works with standard and publicly-available
Digital Elevation Models (DEMs). The different regions in the map are classified according to the geometry of the surface
(i.e. slope) and the soil type. A novel segmentation-based approach is also proposed to divide the regions of interest
into segments where stationarity is ensured.
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.
Autonomous Navigation project page
for Small, Lightweight, Robots
Lead: Carmine Senatore
Sponsors: Washington University, NASA, US Army TARDEC, US Army Research
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
Terramechanics for small robots project page
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
Characterization of Efficient Micro-Scale Pumps
Leads: Youzhi Liang
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.
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
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
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
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
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
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