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An assembly planning system that operates based on a recursive decomposition of assembly into subassemblies, and analyzes assembly cost in terms of stability, directionality, and manipulability to guide the generation of preferred assembly plans is presented. The planning in this system incorporates the special processes, such as cleaning, testing, labeling, etc. that must occur during the assembly, and handles nonreversible as well as reversible assembly tasks through backward assembly planning. In order to increase the planning efficiency, the system avoids the analysis of decompositions that do not correspond to feasible assembly tasks. This is achieved by grouping and merging those parts that can not be decomposable at the current stage of backward assembly planning due to the requirement of special processes and the constraint of interconnection feasibility. The invention includes methods of evaluating assembly cost in terms of the number of fixtures (or holding devices) and reorientations required for assembly, through the analysis of stability, directionality, and manipulability. All these factors are used in defining cost and heuristic functions for an AO* search for an optimal plan.

Optoelectronic proximity sensor comprises seven identical units functioning together to provide partially redundant data on spatial relationship between sensor and nearby diffusely reflecting surface. Provides data on distance, relative orientation, and curvature of surface. Sensor called "Hexeye" because its units exhibit hexagonal symmetry when viewed along optical axes and mounted together in hexagonal close packing. Because all six arrays of photodetectors in unit measure essentially identical distributions of light in operating mode, each provides redundant information averaged to increase accuracy or used to verify operation of others. Malfunctioning photodector identified, and output ignored or replaced by interpolated value. Developed for use in such robotic applications as noncontact probing or scanning of surfaces, localization and recognition of objects, and guiding "smart" robot hands in grasping objects.

Proposed teleoperator control system incorporates several advanced features to improve coordination between remote robotic manipulator (slave arm) and human operator at control manipulator (master arm). Provides superior performance in terms of error profiles and task-completion time in presence of human control errors varying over wide range and maintains stability under large signal-propagation delays. System highly robust in presence of nonlinearities and incorrect human commands in both position and rate modes of control.

Proposed two-arm robotic manipulator capable of changing its mechanical structure to fit given task. No reassembly or replacement in conventional sense: instead, arms commanded during operation to assume any of number of alternative configurations, described briefly in "Dynamic Coordination of a Two-Arm Robotic Manipulator" (NPO-18816), NASA Tech Briefs.

Report presents study of dynamical and kinematical considerations guiding selection of configuration of self-reconfigurable, two-arm robotic manipulator. Two multiple-link arms cooperate in manipulating single object, reconfiguring their mutual, cooperative structure according to changing task requirements.

System of algorithms developed to automate planning of sequences of manufacturing or field operations in which components are put together into assemblies. Results of DFA analysis also used to modify designs to optimize them with respect to assembly considerations.

A theoretical framework is presented for a 'sensing-knowledge command-fusion' paradigm of interactive and cooperative sensing and control in advanced teleoperators, which takes advantage of both current and projected robotic dexterousness and sensor-based autonomy capabilities. Attention is given to (1) a method for the achievement of a sensing-knowledge-command computational mechanism that implements the intended cooperative/interactive system, and (2) the system architecture and man/machine-interface protocols entailed by this implementation.

Teleoperation experiments at JPL have shown that advanced features in a telerobotic system are a necessary condition for good results, but that they are not sufficient to assure consistently good performance by the operators. Two or three operators are normally used during training and experiments to maintain the desired performance. An alternative to this multi-operator control station is a man-machine interface embedding computer programs that can perform some of the operator's functions. In this paper we present our first experiments with these concepts, in which we focused on the areas of real-time task monitoring and interactive path planning. In the first case, when performing a known task, the operator has an automatic aid for setting control parameters and camera views. In the second case, an interactive path planner will rank different path alternatives so that the operator will make the correct control decision. The monitoring function has been implemented with a neural network doing the real-time task segmentation. The interactive path planner was implemented for redundant manipulators to specify arm configurations across the desired path and satisfy geometric, task, and performance constraints.

This paper presents the paradigm of interactive and cooperative sensing and control as a fundamental mechanism of integrating and fusing the strengths of man and machine for advanced teleoperation. The interactive and cooperative sensing and control is considered as an extended and generalized form of traded and shared control. The emphasis of interactive and cooperative sensing and control is given to the distribution of mutually nonexclusive subtasks to man and machine, the interactive invocation of subtasks under the man/machine symbiotic relationship, and the fusion of information and decisionmaking between man and machine according to their confidence measures. The proposed interactive and cooperative sensing and control system is composed of such major functional blocks as the logical sensor system, the sensor-based local autonomy, the virtual environment formation, and the cooperative decision-making between man and machine. The Sensing-Knowledge-Command (SKC) fusion network is proposed as a fundamental architecture for implementing cooperative and interactive sensing and control. Simulation results are shown.

An assembly planning system that operates based on a recursive decomposition of assembly into subassemblies is presented. The planning system analyzes assembly cost in terms of stability, directionality, and manipulability to guide the generation of preferred assembly plans. The planning in this system incorporates the special processes, such as cleaning, testing, labeling, etc., that must occur during the assembly. Additionally, the planning handles nonreversible, as well as reversible, assembly tasks through backward assembly planning. In order to decrease the planning efficiency, the system avoids the analysis of decompositions that do not correspond to feasible assembly tasks. This is achieved by grouping and merging those parts that can not be decomposable at the current stage of backward assembly planning due to the requirement of special processes and the constraint of interconnection feasibility. The invention includes methods of evaluating assembly cost in terms of the number of fixtures (or holding devices) and reorientations required for assembly, through the analysis of stability, directionality, and manipulability. All these factors are used in defining cost and heuristic functions for an AO* search for an optimal plan.

This paper presents the paradigm of interactive and cooperative sensing and control as a fundamental mechanism of integrating and fusing the strengths of man and machine for advanced teleoperation. The interactive and cooperative sensing and control is considered as an extended and generalized form of traded and shared control. The emphasis of interactive and cooperative sensing and control is given to the distribution of mutually nonexclusive subtasks to man and machine, the interactive invocation of subtasks under the man/machine symbiotic relationship, and the fusion of information and decision-making between man and machine according to their confidence measures. The proposed interactive and cooperative sensing and control system is composed of such major functional blocks as the logical sensor system, the sensor-based local autonomy, the virtual environment formation, and the cooperative decision-making between man and machine. A case study is performed to demonstrate the feasibility of implementing the fundamental theory and system architecture of interactive and cooperative sensing and control, proposed for the new generation of teleoperation.

A novel optical proximity sensor system, capable of measuring the distance to the orientation and the discontinuity at a local area of an object surface, was designed and evaluated for robotic applications. The sensor system gets its name, Hexagonal Eye, (HexEYE) from its shape where seven identical hexagonal sensing units were configured hexagonally into a compact geometric structure. The seven sensing units were grouped into multiple combinations of three sensing units to measure the surface orientation as well as surface discontinuity. The distinctive features of HexEYE are an order of magnitude increased in distance sensitivity by optimizing the curvature of a conical mirror, the enhanced measurement accuracy based on multiple levels of sensor fusion, and the compactness in size due to a sensing mechanism based on the Gaussian lens law. A prototype of single sensing unit has been built and was evaluated experimentally.

An approach to robust recognition of handwritten numerals using two operating parallel networks is presented. The first network uses inputs in Cartesian coordinates, and the second network uses the same inputs transformed into polar coordinates. How the proposed approach realizes the robustness to local and global variations of input numerals by handling inputs both in Cartesian coordinates and in its transformed Polar coordinates is described. The required network structures and its learning scheme are discussed. Experimental results show that by tracking only a small number of distinctive features for each teaching numeral in each coordinate, the proposed system can provide robust recognition of handwritten numerals.

The design goal of an advanced teleoperator control system is twofold: 1) to allow the operator's manual control to be robust to system nonlinearities such as time delays and operator's control errors, and 2) to support the high performance of teleoperation while reducing the operator's control burden by providing the master and slave arms with desirable dynamic properties and by allowing the slave arm to automatically perform such control tasks as compliance and force control in the form of task sharing. The authors present a novel teleoperator control system achieving the above design goal by taking the following into consideration: the human dynamics involved in generating control command based on visual and forced feedback is modeled and incorporated into the controller design and evaluation; the dynamic characteristics of slave and master arms are actively modified in such a way as to implement the desirable dynamic characteristics; and the force feedback is redefined in terms of the combination of opposition and force discrepancies in order to establish the required man/machine dynamic coordination under shared control. The proposed control system with human dynamics in the control loop is simulated and compared with a number of conventional methods in the presence of human control errors and time delays.

A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

An optimal sensing strategy for an optical proximity sensor system engaged in the recognition and localization of 3-D natural quadric objects is presented. The optimal sensing strategy consists of the selection of an optimal beam orientation and the determination of an optimal probing plane that compose an optimal data collection operation known as an optimal probing. The decision of an optimal probing is based on the measure of discrimination power of a cluster of surfaces on a multiple interpretation image (MII), where the measure of discrimination power is defined in terms of a utility function computing the expected number of interpretations that can be pruned out by a probing. An object representation suitable for active sensing based on a surface description vector (SDV) distribution graph and hierarchical tables is presented. Experimental results are shown.

Sensing-knowledge-command (SKC) fusion is presented as a fundamental paradigm of implementing cooperative control for an advanced man-machine system. SKC fusion operates on the 'SKC fusion network,' which represents the connection between sensor data to commands through knowledge. Sensing, knowledge, and command of a human and a machine are tapped into the network to provide inputs, or stimuli, to the network. Such stimuli automatically invoke an SKC fusion process and generate a fused output for cooperative control. Once invoked by stimuli, the SKC fusion process forces the network to converge to a new equilibrium state through the network dynamics composed of data fusion, feature transformation, and constraint propagation. The SKC fusion process thus integrates redundant information, maintains network consistency, identifies faulty data and concepts, and specifies those concepts to be strengthened through sensor planning.

The authors present the dynamic coordination of a self-reconfigurable manipulator system capable of changing its mechanical structure according to given task requirements. The self-reconfiguration is achieved by reconfiguring the topology of a dual-arm system through serial, parallel, and bracing structures. Particular emphasis is placed on the dynamic coordination of two arms having three different dual-arm topologies. The authors develop the Cartesian space dynamic models of a dual-arm system of three dual-arm topologies and derive the kinematic and dynamic constraints imposed on two arms in cooperation. Dual-arm dynamic manipulabilities are defined to quantify the dynamic performance of three dual-arm topologies in terms of the efficiency of generating Cartesian accelerations. A methodology of selecting serial, parallel, and bracing structures based on dual-arm dynamic manipulabilities is provided.

The authors present a dextrous bracing structure of a dual-arm system as a means of implementing a self-reconfigurable manipulator system. The dextrous bracing structure allows a dual-arm system to relocate the bracing point between two arms, select the type of contact as the bracing point, and lock or release the joints of individual arms. The self-reconfigurable dual-arm system is now capable of reconfiguring its topology through serial, parallel and bracing structures, supporting general types of contact, rigid or sliding, as well as locking or releasing some of its joints as needed. The focus is on an analysis of the effects of the dextrous bracing on the performance of a dual-arm system in terms of dual-arm manipulabilities and resistivities. The analysis shows that a self-reconfigurable dual-arm system with a dextrous bracing structure gives continuously varying performance characteristics ranging from serial to parallel structures.