George A. Bekey

Distributed multirobot localization

In this paper, we present a new approach to the problem of simultaneously localizing a group of mobile robots capable of sensing one another. Each of the robots collects sensor data regarding its own motion and shares this information with the rest of the team during the update cycles. A single estimator, in the form of a Kalman filter, processes the available positioning information from all the members of the team and produces a pose estimate for every one of them. The equations for this centralized estimator can be written in a decentralized form, therefore allowing this single Kalman filter to be decomposed into a number of smaller communicating filters. Each of these filters processes the sensor data collected by its host robot. Exchange of information between the individual filters is necessary only when two robots detect each other and measure their relative pose. The resulting decentralized estimation schema, which we call collective localization, constitutes a unique means for fusing measurements collected from a variety of sensors with minimal communication and processing requirements. The distributed localization algorithm is applied to a group of three robots and the improvement in localization accuracy is presented. Finally, a comparison to the equivalent decentralized information filter is provided.

Bayesian estimation and Kalman filtering: a unified framework for mobile robot localization

Decision and estimation theory are closely related topics in applied probability. In this paper, Bayesian hypothesis testing is combined with Kalman filtering to merge two different approaches to map-based mobile robot localization; namely Markov localization and pose tracking. A robot carries proprioceptive sensors that monitor its motion and allow it to estimate its trajectory as it moves away from a known location. A single Kalman filter is used for tracking the pose displacements of the robot between different areas. The robot is also equipped with exteroceptive sensors that seek for landmarks in the environment. Simple feature extraction algorithms process the incoming signals and suggest potential corresponding locations on the map. Bayesian hypothesis testing is applied in order to combine the continuous Kalman filter displacement estimates with the discrete landmark pose measurement events. Within this framework, also known as multiple hypothesis tracking, multimodal probability distribution functions can be represented and this inherent limitation of the Kalman filter is overcome.

Distributed Multi-Robot Localization

This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, different sensors and have different positioning capabilities. A single Kalman filter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman filter are treated so that this filter can be decomposed into M modified Kalman filters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.

A strategy for grasp synthesis with multifingered robot hands

This paper presents an approach to synthesizing the control required by a dextrous multifingered robot hand to reach and safely grasp an arbitrary target object. The synthesis is based on analysis of the grasping task as performed by human beings. We divide the task into (1) a target approach phase (including target identification, hand structure and grasp mode selection, selection of approach trajectory, hand preshaping and orientation) and (2) a grasp execution phase (including shape and force adaptation). Each aspect of the required control is discussed. Particular attention is paid to the role of geometric modeling in target identification, to preshaping during the approach trajectory and to the requirements for autonomy in completion of the grasping task. The underlying philosophy is that of reflex control; each aspect of the grasping task is initiated and terminated using sensory data and rules of behavior derived from human expertise in such tasks. The contents and organization of the knowledge base which codifies this expertise are discussed. The fundamental assumptions upon which this control philosophy is based are identified throughout the paper. Preliminary results of a graphical simulation of hand preshaping are given.

Collective localization: a distributed Kalman filter approach to localization of groups of mobile robots

This paper presents a new approach to the cooperative localization problem, namely collective localization. A group of M robots is viewed as a single system composed of robots that carry, in general, different sensors and have different positioning capabilities. A single Kalman filter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman filter are treated so that this filter can be decomposed in M modified Kalman filters each running on a separate robot. The collective localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.

Circumventing dynamic modeling: evaluation of the error-state Kalman filter applied to mobile robot localization

The mobile robot localization problem is treated as a two-stage iterative estimation process. The attitude is estimated first and is then available for position estimation. The indirect (error state) form of the Kalman filter is developed for attitude estimation when applying gyro modeling. The main benefit of this choice is that combined dynamic modeling of the mobile robot and its interaction with the environment is avoided. The filter optimally combines the attitude rate information from the gyro and the absolute orientation measurements. The proposed implementation is independent of the structure of the vehicle or the morphology of the ground. The method can easily be transferred to another mobile platform provided it carries an equivalent set of sensors. The 2D case is studied in detail first. Results of extending the approach to the 3D case are presented. In both cases the results demonstrate the efficacy of the proposed method.

Control architecture for the Belgrade/USC hand

This chapter describes design and control features of a five-fingered anthropomorphic end-effector designed primarily for grasping tasks. Advantages and limitations of the design are discussed, and special emphasis is placed on its suitability for autonomous, non-numerical or reflex control of grasp. Following a discussion of its mechanical design, we present the controller and sensor features incorporated into the current finger model. A knowledge-based control of hand preshape (prior to grasping) is then outlined, and the hand’s suitability as a testbed for the study of human and robot hand motion control is discussed. The final section of this chapter describes future directions.

The Human Operator as a Sampled-Data System

A sampled-data model of the human operator in compensatory tracking is proposed. The model assumes that the operators behavior is characterized by sampling, data reconstruction and extrapolation operations. The spectral characteristics of the new model are presented.

Knowledge-based control of grasping in robot hands using heuristics from human motor skills

The development of a grasp planner for multifingered robot hands is described. The planner is knowledge-based, selecting grasp postures by reasoning from symbolic information on target object geometry and the nature of the task. The ability of the planner to utilize task information is based on an attempt to mimic human grasping behavior. Several task attributes and a set of heuristics derived from observation of human motor skills are included in the system. The paper gives several examples of the reasoning of the system in selecting the appropriate grasp mode for spherical and cylindrical objects for different tasks. >

Computer control of space-borne teleoperators with sensory feedback

This paper presents the conceptual design, analysis, synthesis and software organization of an advanced teleoperator control system with sensory feedback. The design requirements for the system are discussed in detail and an implementation strategy is presented. The resulting system features maximum autonomy of the local hand controller and remote manipulator subsystems, along with kinematic and dynamic coordination between these subsystems. The final design emphasizes cooperation and interaction between the human operator and the computers in control of the sensor-based manipulator system. The hardware and software modules being used to implement the system at JPL are described.