The paper describes an ongoing effort to enable autonomous mobile robots to play soccer in unstructured, everyday environments. Unlike conventional robot soccer competitions that are usually held on purpose-built robot soccer "fields", in our work we seek to develop the capability for robots to demonstrate aspects of soccer-playing in more diverse environments, such as schools, hospitals, or shopping malls, with static obstacles (furniture) and dynamic natural obstacles (people). This problem of "Soccer Anywhere" presents numerous research challenges including: (1) Simultaneous Localization and Mapping (SLAM) in dynamic, unstructured environments, (2) software control architectures for decentralized, distributed control of mobile agents, (3) integration of vision-based object tracking with dynamic control, and (4) social interaction with human participants. In addition to the intrinsic research merit of these topics, we believe that this capability would prove useful for outreach activities, in demonstrating robotics technology to primary and secondary school students, to motivate them to pursue careers in science and engineering.
The goal of concurrent mapping and localization (CML) is to enable a mobile robot to build a map of an unknown environment, while simultaneously using this map to navigate. This paper discusses some of the challenges that are encountered in the development of practical real-time implementations of CML for one or more autonomous mobile robots operating in large-scale environments.
KEYWORDS: Sensors, Sensor fusion, Acoustics, Navigation systems, Environmental sensing, Data modeling, Control systems, Vehicle control, Space operations, Sensing systems
Many important applications of autonomous underwater vehicles (AUVs) require operations in close proximity to man-made objects or natural bottom topography. In these situations, the vehicle must adapt its trajectory on-line in response to current threats and mission objectives. To provide this capability, we are developing a sonar-based navigation technique that emulates the manner in which a person navigates through an unknown room in the dark: by reaching out for and establishing contact with walls, tables, and chairs, managing transitions from one object to the next as one moves across the room. Our intuition here is that, in many ways, sonar is more like touch than vision. It may be possible to build a vehicle that can effectively use its sonar to `grab' an object of interest, say a cylindrical post for docking, and then `reel itself in' by feeding back sonar range measurements from the object to its dynamic controller. We envision an AUV that can establish `virtual tethers' with arbitrary objects in the water column or on the seabed. Fast, local processing can maintain `contact' with the objects or surfaces of interest. Control laws can be established to utilize streams of measurements from these features to achieve local, feature-relative navigation. While our research is driven by the severe challenges of the subsea environment, we anticipate that the approach will also be useful in land robot applications.
This paper describes a geometric approach to underwater 3-D scene reconstruction using sonar range sensing. Our goal is to recover explicit geometric surface descriptions for man-made objects, by focusing the geometric constraints of multiple sonar returns obtained from different sensing locations by a moving autonomous underwater vehicle (AUV). We employ a simplified physical model of the sonar sensing process, based on the geometrical acoustics high-frequency approximation of acoustic scattering. Our current research effort is directed to support the task of locating and retrieving rigid objects from the deep ocean seafloor, using an untethered AUV. The key open problems concern the development of (1) robust methods for 3-D shape recovery, (2) an effective data association procedure, and (3) directed sensing strategies to control the data acquisition process.
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