In this paper, we present <i>Argos</i>, an autonomous ground robot built for the 2009 Intelligent Ground Vehicle Competition (IGVC). Discussed are the significant improvements over its predecessor from the 2008 IGVC, <i>Kratos</i>. We continue to use stereo vision techniques to generate a cost map of the environment around the robot. Lane detection is improved through the use of color filters that are robust to changing lighting conditions. The addition of a single-axis gyroscope to the sensor suite allows accurate measurement of the robot's yaw rate and compensates for wheel slip, vastly improving state estimation. The combination of the D* Lite algorithm, which avoids unnecessary re-planning, and the Field D* algorithm, which allows us to plan much smoother paths,
results in an algorithm that produces higher quality paths in the same amount of time as methods utilizing A*. The successful implementation of a crosstrack error navigation law allows the robot to follow planned paths without cutting corners, reducing the chance of collision with obstacles. A redesigned chassis with a smaller
footprint and a bi-level design, combined with a more powerful drivetrain, makes <i>Argos</i> much more agile and maneuverable compared to its predecessor. At the 2009 IGVC, Argos placed first in the Navigation Challenge.
In this paper we present Kratos, an autonomous ground robot capable of static obstacle field navigation and
lane following. A sole color stereo camera provides all environmental data. We detect obstacles by generating a
3D point cloud and then searching for nearby points of differing heights, and represent the results as a cost map
of the environment. For lane detection we merge the output of a custom set of filters and iterate the RANSAC
algorithm to fit parabolas to lane markings. Kratos' state estimation is built on a square root central difference
Kalman filter, incorporating input from wheel odometry, a digital compass, and a GPS receiver. A 2D A* search
plans the straightest optimal path between Kratos' position and a target waypoint, taking vehicle geometry into
account. A novel C++ wrapper for Carnegie Mellon's IPC framework provides flexible communication between
all services. Testing showed that obstacle detection and path planning were highly effective at generating safe
paths through complicated obstacle fields, but that Kratos tended to brush obstacles due to the proportional law
control algorithm cutting turns. In addition, the lane detection algorithm made significant errors when only a
short stretch of a lane line was visible or when lighting conditions changed. Kratos ultimately earned first place
in the Design category of the Intelligent Ground Vehicle Competition, and third place overall.