The primary purpose for Gamblore is the Intelligent Ground Vehicle Competition (IGVC). Therefore the capabilities of Gamblore are designed to perform well at the tasks required at this event. These tasks involve two obstacle courses. The first, called the Autonomous Challenge requires the vehicle to drive along a path outlined by white lines that travels over grass, concrete, and dirt. This path also has several types of hazards the vehicle must avoid. Gamblore has the ability to "see" the lines and hazards using machine vision and a laser range finder and make drive decisions to follow the lane. The Navigation Challenge is the second course and is a waypoint finding task. Given a set of GPS waypoints, Gamblore can drive to within 2 meters of the points while avoiding hazards similar to those on the Autonomous Challenge course.

    Gamblore is also designed with speed in mind. From the beginning it was the designer's hopes the vehicle travel at the maximum allowed speeds of the IGVC, and be configurable for even higher speeds up to 10 MPH through its gearing. Gamblore is based off of a differential drive system so that it can turn in place and make sharp turns, but with high torque and traction on its wheels so that it can travel over different types of terrain. A treaded system is not used because of the complexity required in construction of the drive system.

    All of this means the vehicle not only drives fast, but needs to "think" fast too. Gamblore will use knowledge based systems in combination with fast path planning methods to make decisions at high speeds. Different type of search techniques and cost analysis on maps created from sensors will be used in combination with knowledge based systems like context based reasoning. This means the vehicle can make decisions on what actions to take very quickly, but also has the capabilities of making more complex decisions than simply turning left or right and picking variable speeds. Gamblore will be able to make decisions such as avoiding dead ends, turning around to go back and choose alternative paths, and maintain an overall goal. Systems that simply make decisions to not hit obstacles and drive towards waypoints are not capable of these higher level decisions.

    Optimized mapping systems that organize, sort, and analyze data quickly will assist in combining data from multiple sensors and in the decision making. This system, called Cartographer makes it possible to build and analyze large local maps very quickly so that more complex vision and decision making algorithms can be used. In addition to fast mapping, machine vision algorithms have been optimized and parallelized for increased performance and complexity without bogging down the rest of the system.

    To summarize, using its various sensors, mapping, and decision techniques, Gamblore can drive to DGPS waypoints, and perform lane following exercises. These are common tasks that unmanned vehicles must perform. The robust design of the system will also allow for additional capabilities being added in the future through software changes.