Feature-Based Localization Using Fixed Ultrasonic Transducers

We describe an approach for mobile robot localization based on geometric features extracted from ultrasonic data. As is well known, a single sonar measurement using a standard POLAROIDTM sensor, though yielding relatively accurate information regarding the range of a reflective surface patch, provides scant information about the location in azimuth or elevation of that patch. This lack of sufficiently precise localization of the reflective patch hampers any attempt at data association, clustering of multiple measurements or subsequent classification and inference. In previous work [15, 16] we proposed a multi-stage approach to clustering which aggregates sonic data accumulated from arbitrary transducer locations in a sequential fashion. It is computationally tractable and efficient despite the inherent exponential nature of clustering, and is robust in the face of noise in the measurements. It therefore lends itself to applications where the transducers are fixed relative to the mobile platform, where remaining stationary during a scan is both impractical and infeasible, and where deadreckoning errors can be substantial. In the current work we apply this feature extraction algorithm to the problem of localization in a partially known environment. Feature-based localization boasts advantages in robustness and speed over several other approaches. We limit the set of extracted features to planar surfaces. We describe an approach for establishing correspondences between extracted and map features. Once such correspondences have been established, a least squares approach to mobile robot pose estimation is delineated. It is shown that once correspondence has been found, the pose estimation may be performed in time linear in the number of extracted features. The decoupling of the correspondence matching and estimation stages is shown to offer advantages in speed and precision. Since the clustering algorithm aggregates sonic data accumulated from arbitrary transducer locations, there are no constraints on the trajectory to be followed for localization except that sufficiently large portions of features be ensonified to allow clustering. Preliminary experiments indicate the usefulness of the approach, especially for accurate estimation of orientation

Cooperative Material Handling by Human and Robotic Agents:Module Development and System Synthesis

In this paper we present the results of a collaborative effort to design and implement a system for cooperative material handling by a small team of human and robotic agents in an unstructured indoor environment. Our approach makes fundamental use of human agents\u27 expertise for aspects of task planning, task monitoring, and error recovery. Our system is neither fully autonomous nor fully teleoperated. It is designed to make effective use of human abilities within the present state of the art of autonomous systems. It is designed to allow for and promote cooperative interaction between distributed agents with various capabilities and resources. Our robotic agents refer to systems which are each equipped with at least one sensing modality and which possess some capability for self-orientation and/or mobility. Our robotic agents are not required to be homogeneous with respect to either capabilities or function. Our research stresses both paradigms and testbed experimentation. Theory issues include the requisite coordination principles and techniques which are fundamental to the basic functioning of such a cooperative multi-agent system. We have constructed a testbed facility for experimenting with distributed multi-agent architectures. The required modular components of this testbed are currently operational and have been tested individually. Our current research focuses on the integration of agents in a scenario for cooperative material handling