1. Active Simultaneous Localization and Mapping Stephen Tully, stully@andrew.cmu.edu 16-735: Robotic Motion Planning This project is to actively control the path of a mobile robot, while performing simultaneous localization and mapping, so as to minimize the mapping and localization error. Stephen Tully “Slammer”: a Nomad Scout robot with omnidirectional camera

2. Filtering and Path Planning Technique EKF: I will be using an extended Kalman filter whose state will include the robot pose (X,Y,Ө) and the X-Y locations of landmarks detected in the environment. Odometry will provide the prediction step of the EKF and bearing-only measurements to visual features in the environment will update the EKF. Value Iteration: I will be using a dynamic programming solution to search for an optimal path in a discretized space that would minimize the volume of the uncertainty ellipsoid defined by the covariance matrix of the EKF state (determinant of the covariance matrix).

3. Demonstrations I will make several videos of the robot mapping a set of landmarks. A control experiment will be shown where the robot drives a straight-line path. An accompanying video will show a visual representation of the landmarks being mapped with associated uncertainty ellipses. Videos of the optimized paths will then be shown to demonstrate better convergence on landmark locations. The comparison of a straight-line path with a planned path can also be shown with a real demonstration. A laptop will be used to show the videos of landmarks being mapped. People viewing the demonstration can visually compare the convergence of landmark locations between two different trials to see an improvement with the optimized path.