Predictive Mover Detection and Tracking in Cluttered Environments

This paper describes the design and experimental evaluation of a system that enables a vehicle to detect and track moving objects in real-time. The approach investigated in this work detects objects in LADAR scan lines and tracks these objects (people or vehicles) over time. The system can fuse data from multiple scanners for 360° coverage. The resulting tracks are then used to predict the most likely future trajectories of the detected objects. The predictions are intended to be used by a planner for dynamic object avoidance. The perceptual capabilities of our system form the basis for safe and robust navigation in robotic vehicles, necessary to safeguard soldiers and civilians operating in the vicinity of the robot

Detection of Parking Spots Using 2D Range Data

<p>This paper addresses the problem of reliably detecting parking spots in semi-filled parking lots using onboard laser line scanners. In order to identify parking spots, one needs to detect parked vehicles and interpret the parking environment. Our approach uses a supervised learning technique to achieve vehicle detection by identifying vehicle bumpers from laser range scans. In particular, we use AdaBoost to train a classifier based on relevant geometric features of data segments that correspond to car bumpers. Using the detected bumpers as landmarks of vehicle hypotheses, our algorithm constructs a topological graph representing the structure of the parking space. Spatial analysis is then performed on the topological graph to identify potential parking spots. Algorithm performance is evaluated through a series of experimental tests.</p

A Robot Supervision Architecture for Safe and Efficient Space Exploration and Operation

Current NASA plans envision human beings returning to the Moon in 2018 and, once there, establishing a permanent outpost from which we may initiate a long-term effort to visit other planetary bodies in the Solar System. This will be a bold, risky, and costly journey, comparable to the Great Navigations of the fifteenth and sixteenth centuries. Therefore, it is important that all possible actions be taken to maximize the astronauts’ safety and productivity. This can be achieved by deploying fleets of autonomous robots for mineral prospecting and mining, habitat construction, fuel production, inspection and maintenance, etc.; and by providing the humans with the capability to telesupervise the robots’ operation and to teleoperate them whenever necessary or appropriate, all from a safe, “shirtsleeve” environment. This paper describes the authors’ work in progress on the development of a Robot Supervision Architecture (RSA) for safe and efficient space exploration and operation. By combining the humans’ advanced reasoning capabilities with the robots’ suitability for harsh space environments, we will demonstrate significant productivity gains while reducing the amount of weight that must be lifted from Earth – and, therefore, cost. Our first instantiation of the RSA is a wide-area mineral prospecting task, where a fleet of robots survey a pre-determined area autonomously, sampling for minerals of interest. When the robots require assistance – e.g., when they encounter navigation problems, reach a prospecting site, or find a potentially interesting rock formation – they signal a human telesupervisor at base, who intervenes via a high-fidelity geometrically-correct stereoscopic telepresence system (Figure 1a). In addition to prospecting, the RSA applies to a variety of other tasks, both on the surface: mining, transporting, and construction – and on-orbit: construction, inspection, and repair of large space structures and satellites (Figure 1b). This paper is structured as follows: In the following section we present related work and emphasize the contribution we bring to the state-of-the-art. Next, we describe the robot supervision architecture, which is the overarching paradigm under which all other modules function. We then turn our attention to the main focus of the paper, our current system implementation and results, where three rovers at Carnegie Mellon University (CMU), NASA Ames Research Center (ARC) and NASA Jet Propulsion Laboratory (JPL) visit targets simulating regions of interest for prospecting, both in autonomous and teleoperated modes. The paper closes with conclusions and plans for future work