Nasa Summer Robotics Interns Perform Simulation Of Robotics Technology

This paper provides an overview of the NASA Robotics Academy and highlights the accomplishments of one of three robotics teams that participated in the summer robotics workshop in 2008. The program which is administered by NASA's Office of Education places student teams at several NASA Centers engaged in robotics work. Robotics Team 1 at the Marshall Space Flight Center located in Huntsville, Alabama was assigned to the Self-Aware Surface Networks (SASN) project. The SASN project is studies the use of networks to gather data from scientific instruments and to control robotic vehicles in scenarios similar to those anticipated when NASA returns to the moon or beyond. SASN project has a fleet of eight (8) small MARCbot robots acquired from the U.S. Army which have been modified with a prototype communications network. The project also has a larger robotic system, the iRobot/John Deere R-Gator capable of carrying large instrument payloads and robotic arms. During the 10 week tenure of the team the students developed computer graphics-based simulations of a lunar surface operational scenario in which satellite imagery is used to identify the location of a target of interest. Small scout robots are deployed to examine the target and a larger vehicle equipped with proximity sensors is used to retrieve the target. The simulations represent actual hardware demonstrations that have been performed or are planned for implementation in the near future. Their project required the students developed graphic models of all vehicles and to developed the control algorithms needed to execute the robotic scenarios. This work was completed early by the student team which volunteered for an additional assignment. This last assignment consisted of simulating a fleet of four (4) robots equipped with acoustic sensors positioned in a dispersed pattern. Upon detecting the initial arrival of a sound e.g., a gunshot and recording the time of arrival, a published algorithm was used to identify the point of origin. The students also performed sensitivity studies to identify error caused by tolerances in system components.