Challenges for Autonomous Mobile Robots

Summary form only given. This talk will have two parts. In part one, we will review recent progress in mobile robotics, focusing on the problems of simultaneous mapping and localization (SLAM) and cooperative navigation of mobile sensor networks. The problem of SLAM is stated as follows: starting from an initial position, a mobile robot travels through a sequence of positions and obtains a set of sensor measurements at each position. The goal is for the mobile robot to process the sensor data to compute an estimate of its position while concurrently building a map of the environment. We will present SLAM results for several scenarios including land robot mapping of large-scale environments and undersea mapping using optical imaging sensors. We will also describe work on cooperative navigation for networks of autonomous underwater vehicles (AUVs) and autonomous sea-surface vehicles (ASVs). In the second part of the talk, we will provide an overview of MIT's entry in the 2007 DARPA Urban Challenge. The goal of this effort is to produce a car that can drive autonomously in traffic. Our team has developed a novel strategy for using a large number of inexpensive sensors mounted on the vehicle periphery. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real-time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of well-proven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course.