The IGVC is a college level autonomous unmanned ground vehicle (UGV) competition that encompasses a wide variety of engineering professions – mechanical, electrical, computer engineering and computer science. It requires engineering students from these varied professions to collaborate in order to develop a truly integrated engineering product, a fully autonomous UGV. Students must overcome a large variety of engineering technical challenges in control theory, power requirements/distribution, cognition, machine vision, vehicle electronics, sensors, systems integration, vehicle steering, fault tolerance/redundancy, noise filtering, PCB design/analysis/selection, vehicle engineering analysis, design, fabrication, field testing, lane-following, avoiding obstacles, vehicle simulation/virtual evaluation, GPS/waypoint navigation, safety design, etc.
Legged Robots have tremendous mobility, but they can also be very inefficient. These inefficiencies can
be due to suboptimal control schemes, among other things. If your goal is to get from point A to point B
in the least amount of time, your control scheme will be different from if your goal is to get there using
the least amount of energy. In this paper, we seek a balance between these extremes by looking at both
efficiency and speed. We model a walking robot as a rimless wheel, and, using Pontryagin's Maximum
Principle (PMP), we find an "on-off" control for the model, and describe the switching curve between
these control extremes.
Using wide-angle or omnidirectional camera lenses to increase a mobile robot's field of view introduces nonlinearity
in the image due to the 'fish-eye' effect. This complicates distance perception, and increases image
processing overhead. Using multiple cameras avoids the fish-eye complications, but involves using more electrical
and processing power to interface them to a computer. Being able to control the orientation of a single camera,
both of these disadvantages are minimized while still allowing the robot to preview a wider area. In addition,
controlling the orientation allows the robot to optimize its environment perception by only looking where the
most useful information can be discovered. In this paper, a technique is presented that creates a two dimensional
map of objects of interest surrounding a mobile robot equipped with a panning camera on a telescoping shaft.
Before attempting to negotiate a difficult path planning situation, the robot takes snapshots at different camera
heights and pan angles and then produces a single map of the surrounding area. Distance perception is performed
by making calibration measurements of the camera and applying coordinate transformations to project
the camera's findings into the vehicle's coordinate frame. To test the system, obstacles and lines were placed to
form a chicane. Several snapshots were taken with different camera orientations, and the information from each
were stitched together to yield a very useful map of the surrounding area for the robot to use to plan a path
through the chicane.
Proc. SPIE. 7352, Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing
KEYWORDS: Detection and tracking algorithms, Sensors, Control systems, Computer programming, Simulink, Adaptive control, Motion models, Control systems design, Systems modeling, Filtering (signal processing)
In this paper, the controllability of a Mecanum omnidirectional vehicle (ODV) is investigated. An adaptive drive
controller is developed that guides the ODV over irregular and unpredictable driving surfaces. Using sensor
fusion with appropriate filtering, the ODV gets an accurate perception of the conditions it encounters and then
adapts to them to robustly control its motion. Current applications of Mecanum ODVs are designed for use
on smooth, regular driving surfaces, and don't actively detect the characteristics of disturbances in the terrain.
The intention of this work is to take advantage of the mobility of ODVs in environments where they weren't
originally intended to be used. The methods proposed in this paper were implemented in hardware on an ODV.
Experimental results did not perform as designed due to incorrect assumptions and over-simplification of the
system model. Future work will concentrate on developing more robust control schemes to account for the
unknown nonlinear dynamics inherent in the system.
An ultra-wideband (UWB) inter-radio ranging technology with measurement resolution of +/-0.5 ft and range up to
0.5 kilometer under certain FCC regulation was recently introduced. However, measurement data are extremely
erroneous due to stochastic variables in the device and multipath radio wave reflections. This paper presents fuzzy
logic tuned double tracking filters as a solution to remove misinformation in the data. The 1st tracker locates the
overall center of the data in the presence of the large sporadic noise. A fuzzy logic admits only neighborhood data
to a 2nd tracker which takes care of smaller deviation noise. The fuzzy neighborhood filter approach has been
successfully applied to clean up the UWB radio ranges. Experimental results are shown.
We present two methods for a localization system, defined as the "angle of arrival" scheme, which computes position and heading of an autonomous vehicle system (AVS) fusing both odometry data and the measurements of the relative azimuth angles of known landmarks (in this case, reflectors of a stabilized laser/reflector system). The first method involves a combination of a geometric transformation and a recursive least squares approach with forgetting factor. The second method presented is a direct approach using variants of the Unscented Kalman filter. Both methods are examined in simulation and the results presented.
This paper describes a method of acquiring behaviorist-based reactive control strategies for an autonomous skid-steer robot operating in an unknown environment. First, a detailed interactive simulation of the robot (including simplified vehicle kinematics, sensors and a randomly generated environment) is developed with the capability of a human driver supplying all control actions. We then introduce a new modular, neural-fuzzy system called Threshold Fuzzy Systems (TFS). A TFS has two unique features that distinguish it from traditional fuzzy logic and neural network systems; (1) the rulebase of a TFS contains only single antecedent, single consequence rules, called a Behaviorist Fuzzy Rulebase (BFR) and (2) a highly structured adaptive node network, called a Rule Dominance Network (RDN), is added to the fuzzy logic inference engine. Each rule in the BFR is a direct mapping of an input sensor to a system output. Connection nodes in the RDN occur when rules in the BFR are conflicting. The nodes of the RDN contain functions that are used to suppress the output of other conflicting rules in the BFR. Supervised training, using error backpropagation, is used to find the optimal parameters of the dominance functions. The usefulness of the TFS approach becomes evident when examining an autonomous vehicle system (AVS). In this paper, a TFS controller is developed for a skid-steer AVS. Several hundred simulations are conducted and results for the AVS with a traditional fuzzy controller and with a TFS controller are compared.