At this point you may be wondering why we built Calculon, and what can it do. As stated earlier, the idea for Calculon came about after the 12th Intelligent Ground Vehicle Competition, IGVC. Our previous vehicle, Black Knight, had hit its limitations with the current software and sensors for that competition. So Calculon was designed to exceed where Black Knight had failed. Calculon has the capabilities in performing in every aspect of the IGVC which includes the Autonomous and Navigation Challenges. The exact rules and requirements can be found on the IGVC website.
The Autonomous Challenge course is designed to test a vehicles lane following capabilities. The course is built on either grass or concrete and is a large circle with spray painted lines on either side making up the lane. Different types of road obstacles, ramps, and simulated terrain changes are common throughout the course. To complete this task Calculon uses a camera for machine vision software to detect lines and obstacles, and a SICK LIDAR unit is also used for detecting three dimensional obstacles. Different types of path planning algorithms are then used to navigate around detected obstacles, while staying on the lane.
The Navigation Challenge takes place on a large course about half the size of a football field. Nine DGPS waypoints are scattered around the course in between various obstacles. Vehicles must navigate to all nine waypoints while avoiding obstacles in the shortest amount of time. Calculon uses DGPS receiver and antenna and digital compass to navigate to these waypoints as quickly as possible.
In conclusion, Calculon has many of the capabilities required for any ground vehicle to navigate typical road conditions. Road conditions could be lane following and DGPS navigation combined if desired. The details of how we have constructed Calculon for these tasks are described throughout this website.