Typical multi-robot systems consist of robots cooperating to maximize global fitness functions. However, in some scenarios, the set of interacting robots may not share common goals and thus the concept of a global fitness function becomes invalid. This work examines multi-robot communities (MRC), in which individual robots have independent goals. Within the MRC context, we present a task allocation architecture that optimizes individual robot fitness functions over long time horizons using reciprocal altruism. Previous work has shown that reciprocating altruistic relationships can evolve between two willing robots, using market-based task auctions, while still protecting against selfish robots aiming to exploit altruism. As these relationships grow, robots are increasingly likely to perform tasks for one another without any reward or promise of payback. This work furthers this notion by considering cases where an imbalance exists in the altruistic relationship. The imbalance occurs when one robot can perform another robot's task, thereby exhibiting altruism, but the other robot cannot reciprocate since it is physically unable (e.g. lack of adequate sensors or actuators). A new altruistic controller to deal with such imbalances is presented. The controller permits a robot to build altruistic relationships with the community as a whole (one-to-many), instead of just with single robots (one-to-one). The controller is proven stable and guarantees altruistic relationships will grow, if robots are willing, while still minimizing the effects of selfish robots. Results indicate that the one-to-many controller performs comparable to the one-to-one on most problems, but excels in the case of an unbalanced altruistic relationship.

One of the major developments in robotics in recent years has been their increasing closeness to humans. In this talk we will review three aspects of this trend: Robots in and on our bodies, robots in the home, and robots in society. In the latter part of the talk we will discuss some of the emerging psychological, sociological and ethical issues which may arise as a result of this coexistence.

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.

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.

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.

A layered algorithm for extracting features in an indoor environment from planar range data is presented. At the lower (signal processing) level, the SEGMENTS algorithm exploits the speed and accuracy of two extended Kalman filters working in tandem and processes sequentially the distance measurements within each scan. The first Kalman filter is responsible for initiating straight line segments and detecting clutter while the second estimates the parameters of each line segment (such as distance and orientation) and determines when this is interrupted. The dual filter combination is capable of detecting edges and straight line segments within the scene in front of the robot. At the higher (post-processing) level, the identified segments are combined to form more complex features such as extended walls, corners (concave and convex), doors and corridors. During the composition cycle, SEGMENTS makes use of (i) the parametric representation of the straight line segments and (ii) the prespecific topological models of the features this algorithm seeks. A list of the identified features along with their location with respect to the laser sensor is finally available to the user. The cluttered regions in the scene are also marked on a polar representation of the environment. The presented algorithm has been tested on a Pioneer 2 DX mobile robot equipped with a SICK LMS 200 proximity laser scanner performing map-based localization. Low computational requirements, accuracy and robustness to uncertainty and noise characterize the performance of this new method for feature extraction.

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.

The mobile robot localization problem is decomposed into two stages; attitude estimation followed by position estimation. The innovation of our method is the use of a smoother, in the attitude estimation loop that outperforms other Kalman filter based techniques in estimate accuracy. The smoother exploits the special nature of the data fused; high frequency inertial sensor (gyroscope) data and low frequency absolute orientation data (from a compass or sun sensor). Two Kalman filters form the smoother. During each time interval one of them propagates the attitude estimate forward in time until it is updated by an absolute orientation sensor. At this time, the second filter propagates the recently renewed estimate back in time. The smoother optimally exploits the limited observability of the system by combining the outcome of the two filters. The system model uses gyro modeling which relies on integrating the kinematic equations to propagate the attitude estimates and obviates the need for complex dynamic modeling. The indirect (error state) form of the Kalman filter is developed for both parts of the smoother. The proposed approach is independent of the robot structure and the morphology of the ground. It can easily be transferred to another robot which has an equivalent set of sensors. Quaternions are used for the 3D attitude representation mainly for practical reasons discussed in the paper. Proposed innovative algorithm is tested in simulation and the overall improvement in position estimation is demonstrated.

This paper introduces a method to detect and identify faults in wheeled mobile robots. The idea behind the method is to use adaptive estimation to predict (in parallel) the outcome of several faults. Models of the system behavior under each type of fault are embedded in the various parallel estimators (each of which is a Kalman filter). Each filter is thus tuned to a particular fault. Using its embedded model each filter predicts values for the sensor readings. The residual (the difference between the predicted and actual sensor reading) is an indicator of how well the filter is performing. A fault detection and identification module is responsible for processing the residual to decide which fault has occurred. As an example the method is implemented successfully on a Pioneer I robot. The paper concludes with a discussion of future work.

Multiple model adaptive estimation (MMAE) is used to detect and identify sensor failures in a mobile robot. Each estimator is a Kalman filter with a specific embedded failure model. The filter bank also contains one filter which has the nominal model embedded within it. The filter residuals are postprocessed to produce a probabilistic interpretation of the operation of the system. The output of the system at any given time is the confidence in the correctness of the various embedded models. As an additional feature the standard assumption that the measurements are available at a constant, common frequency, is relaxed. Measurements are assumed to be asynchronous and of varying frequency. The particularly difficult case of 'soft' sensor failure is also handled successfully. A system architecture is presented for the general problem of failure detection and identification in mobile robots. As an example, the MMAE algorithm is demonstrated on a Pioneer I robot in the case of three different sensor failures.

The tropism system cognitive architecture provides an intuitive formalism for colonies of agents, either hardware or software. We present a fine-grained implementation of the architecture on a colony of software agents for the interpretation of human tactile gestures for robotic trajectory specification and modification. The fine-grained nature of the architecture and the use of the port-based object framework for agent instantiation allows the manual construction of a capable agent set that is reconfigurable and reusable across different gesture-based interaction tasks.

This paper describes a multiple criteria, statistical technique for mobile robot evaluation. The evaluation method measures the time and energy costs of a particular class of exploratory missions. The method is implemented in the special case of one wheeled mobile robot in the laboratory, which was made to execute several (approximately 100) instances of the mission. It is proposed that a validated, dynamic model of the robot embedded in a simulated mission scenario is to be used to study alternate robot designs in a small 'neighborhood' of the existing physical system. Such a simulation was built and is described here. Results from trials with the physical robot and its simulated counterpart are compared and shown to agree well. The simulation is used to evaluate designs not constructed in the laboratory, and results are discussed.

A novel architecture for agents in colonies has been developed in order to investigate certain forms of group interaction. Specifically, we are interested in the extent to which overall goals for a colony can be achieved when each agent is only aware of limited local goals, whether the architecture allows for emergence of unexpected behaviour, and whether explicit communication among agents facilitates or hinders task performance. The architecture supports several forms of learning. Large colonies of agents (as many as 100) have been studied in simulation experiments, where they carried out fetch-and-carry tasks in the presence of predators and with limited energy reserves. In addition, a physical colony of four agents has been fabricated with the same architecture, to ensure that the behaviours observed in simulation were also present in the hardware implementations.

Fixtures are devices used in manufacturing to immobilize a part far operations such as assembly, inspection or machining. Modular fixtures are configurations of standard components. The goal of a automatic modular fixture design is to find algorithms to determine a set of fixturing contact points automatically, given a set of modular components and the fixturing requirements. This paper considers the problem of generating valid assembly sequences for a given modular future configuration. This is called assembly planning for modular fixtures. Because the application domain is known, we can devise an efficient assembly planning system taking advantage of the characteristics of the fixture domain. In this paper, we propose a fastener-based approach which considers the functionality of fasteners (to hold the fixture components together) to achieve both the efficiency in assembly planning and the stability of the generated assembly sequences. We also describe an accessibility measure that can be used to select preferred assembly sequences. This work complements automatic fixture design such that complete automation can be achieved from the design of a fixture to its assembly, which can be performed manually or with the aid of robots.

The main objective of assembly planning is to determine a sequence of assembling a product with respect to its geometric and resource constraints. Recent strides toward concurrent engineering have called for a tighter integration of assembly planning with design, because often during assembly planning, one realizes that a significant amount of assembly cost can be cut by redesigning the product itself. Instead of "retroactively" redesigning a product upon assembly planning, a concurrent engineering platform must allow its users to evaluate designs, and "proactively" explore different alternatives at various levels of abstractions so that one can zoom in on "promising" design paths and design right within the first few design cycles. This paper presents such an integrated system in which assembly planning becomes an essential part of the design exploration process. An algorithm for assembly planning during design for such an environment is developed. Designing for assembly is more effective and correct because, for a particular sequence of assembly, different design alternatives can be created and evaluated.

Many current avenues of vision research involve fully analyzing an image with expensive, high powered computers. This approach has major implications in terms of cost, size, and power consumption. Other methods have involved sub-sampling an image to reduce cost and complexity. This has the disadvantage of information loss. We present a low cost, low powered, reduced complexity vision system capable of intelligently sampling an image to reduce this information loss. The design philosophy and methodology is discussed, along with sample applications. Primarily we demonstrate how the reduced complexity vision system will be used to aid in navigation of an autonomous flying vehicle. This is quantified by showing how having multiple sampling schemes result in increased robustness and accuracy of our helicopter line tracking algorithm.

A methodology for the mobility evaluation and mission oriented performance assessment of an autonomous mobile robot is developed. The evaluation strategy uses both time and energy based measures resulting in the development of isochronal/isoenergy contours which characterize the expected mobility of the robot over novel terrain using sample trials on patches of land prior to the mission. This work has a basis in the evaluation of a robot for extraterrestrial exploration and demonstrates the feasibility of the evaluation methodology proposed using simulations. Simulation results are reported for two designs of micro-rovers viz. legged and wheeled on a simulated Martian terrain. Future work is discussed in the context of increased complexity due to hardware implementations, better terrain models and statistical simulations all of which are currently under development.

This paper presents a novel cognitive architecture for simulated autonomous robots in an artificial world. The design principle of the architecture enables the robotic system to both react to the world and perform premeditated actions. The cognitive architecture is a modular structure composed of a number of multilevel interconnected components, each with distributed, module-specific memory. The modular structure allows for simple portability of the architecture across a variety of robotic platforms. The behavior of the robot is based on the automatic creation of composite behavior from a set of predefined atomic behaviors. Facts about the world are discovered as the robot performs its tasks. An autonomous robot embedded within an artificial world (named Machina Cogitans) is equipped with this architecture, and a number of experiments are presented.>

Within the field of robotics, much recent attention has been given to control techniques that have been termed reactive or behavior-based. The design of such control systems for even a remotely interesting task is typically a laborious effort, requiring many hours of experimental "tweaking" as the actual behavior of the system is observed by the system designer. In this paper, the authors present a neural-based reinforcement learning approach to the design of reactive control policies in which the designer specifies the the desired behavior of the system, rather than the control program that produces the desired behavior.>

The human hand is a complex end-effector capable of a large variety of postures. Multifingered robot hands, such as the Belgrade/USC hand, can approximate human hand functionality, and it is possible to consider their use in prosthetics. The authors have developed a system, PRESHAPE, that translates user commands into motor signals using the virtual finger concept. For control, electromyographic (EMG) signals from forearm muscles are used. The authors describe PRESHAPE and its use of EMG signals. Simulation results are presented.>

A control system architecture is described for an autonomous flying vehicle. The vehicle, equipped with fourteen sensors, uses a model helicopter as an airframe. The control system utilizes these sensors to: remain aloft and in stable flight; navigate to a target; and manipulate a physical object. The overall approach to the problem is based on a behavioral paradigm. The key contribution of this paper is the demonstration of a situated agent under these severe circumstances; as the craft is airborne, it is in constant risk of crashing. Unlike terrestrial mobile robots, the craft must constantly make sound decisions to maintain its integrity.

Multi-fingered robotic hands are attempts to approximate human hand characteristics and functionality, and it is reasonable to consider their possible adaptation and use in prosthetics and rehabilitation. The Belgrade/USC robot hand is used as a prototype prosthetic hand in order to evaluate a system that translates task-level commands into motor commands. The system, PRESHAPE, uses the virtual finger concept for generating the free and guarded motions that occur during the phases of hand movements in prehensile and nonprehensile tasks. This paper describes the control philosophy of PRESHAPE and presents simulation results for various tasks.

A description of gait is presented which enables modeling and visualization of intra-limb leg and body motions, and control of these motions by rider inputs. The rider inputs feed into a neural network, the outputs of which feed into a gait simulator which controls the gait by means of a finite state machine controller. Simulation results are presented for gaits seen in exhibition horse riding. The simulation runs in real time on a Silicon Graphics IrisCrimson VGXT.

An approach to overcoming the slow convergence problems often associated with learning complex nonlinear mappings is presented. The mappings are learned in a context-dependent manner so that complex problems are decomposed into simpler subproblems corresponding to different contexts. While no general conditions for determining applicability the method have been found, its power is illustrated through experiments in controlling simulated robot manipulators in two and three degrees of freedom. The experiments also indicate that the method shows promising scale-up properties.

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.>

The majority of prosthetic hands available today make no pretense of approaching the versatility and functionality of the human hand. Available prostheses generally possess only one degree of freedom, which greatly reduces the manipulative ability of the device. Additional degrees of freedom are rarely added, because of the difficulty in controlling them naturally. The authors have developed a control philosophy based on minimum information transfer from the amputee and maximum local autonomy of the device. To test this philosophy, they have developed a graphical simulation of a five-fingered anthropomorphic device. The results presented show that this is a viable method for the control of a multifingered prosthetic hand.>

Preliminary research on applying a novel control scheme to a medical expert system is presented. The solution of most diagnosis problems requires reasoning at multiple abstraction levels. Adaptive abstraction (AA) is a control scheme that enables an expert system to switch automatically to an appropriate level of abstraction to solve the task at hand. The overall knowledge organization, the control mechanisms, and the knowledge base update method of the AA scheme are described along with examples in automated gait analysis. The advantages of the AA scheme are mentioned. Several theoretical issues concerning the design of expert systems are raised, including: determination of a knowledge base's granularity, the downward inclusion property, which is needed for the AA scheme to work, and the necessity of common-sense knowledge for expert systems to overcome brittleness.>

The authors introduce a quantitative shape metric for planar parts, called feedability, based on a stochastic parts feeding algorithm reported by K. Goldberg et al., (1990,1991). Although feedability is only one possible metric for design-for-assembly (DFA), it relates one aspect of assembly cost directly to part geometry. Therefore, based on feedability, a set of geometric redesign strategies can be produced. The application of feedability to DFA is illustrated and compared with other DFA methods.>

The behavior of a colony of p-robots, with the common goal of destroying malignant tissue in the brain, is simulated. The behavior of each robot is gov- erned by local rules of interaction with the environ- ment. Each robot alone has a low probability of achiev- ing its purpose. Collectively, the synergistic effect of cooperation significantly improves the likelihood of an individual achieving its purpose. The behavioral strat- egy to achieve such cooperation (by means of chemical communication signals) is described. The method is il- lustrated by simulation results. I. INTRODUCTION

R/sup 2/AD, a flexible robotics application development environment, combines powerful graphical display features with tools that support the rapid development of application programs. The graphical interface allows researchers to monitor the system state variables during the dynamic execution of robot programs. This feature enhances the researcher's efficiency in interpreting experimental results. Rapid development is supported by a virtual machine through the use of software engineering principles and object-oriented techniques. In addition to these features, the user is able to emulate a wide variety of robot computational paradigms. This allows experimentation with, and comparison of, various computational techniques (e.g., hierarchical versus heterarchical control process topologies). The utility of the R/sup 2/AD virtual machine is demonstrated through applications currently under development.>

A unified framework is proposed for constructing embedded robotic assembly systems that integrate plan generation and plan execution (scheduling). The salient feature of this framework is that robotic assembly planning and scheduling are organized into two orthogonal problem-solving processes that can be carried out in two distinctive modes: an interleaved or a temporarily separated manner. The utility and advantages of this two-dimensional framework are demonstrated in the context of a fully integrated planning and scheduling system which has been implemented and tested in the domain of robotic assembly.>

The authors identify four important task requirements for dextrous robot hand control. These requirements are stability, manipulability, torquability, and radial rotatability. High-level task descriptions, supplied by the user, are refined into detailed task descriptions that can be used to drive a robot hand. A knowledge-based approach for refining a reasonable set of tasks is used to infer values for a set of task attributes, which trigger several heuristics. Those heuristics are applied using a set of metaheuristics to determine good grasp postures and poses for the task. How to use this multidimensional grasping quality in grasp mode selection and performance evaluation is shown in an industrial assembly domain.>

The design of an expert system for diagnosis of gait pathology is described. Knowledge is represented by means of frames representing particular abnormal gait patterns. A divide and conquer matching technique is used for inferring the action of specific dysfunctional muscles. Input data include: (1) muscle activation, from electromyographic signals measured by fine wire electrodes inserted in leg muscles, (2) the foot-floor contact pattern, measured by switches embedded in a special insole placed in shoes or taped to the subject's foot, (3) the angular motion at the hip, knee and ankle, measured by a 3-D computerized motion analysis system, and (4) ground reaction forces, measured by a force plate embedded in the floor along the test track. output data include a processed and summarized version of the input data, diagnoses, and explanations.>

A heuristic random search technique for finding a near-optimum binary input sequence to a linear dynamical system is presented. The problem requires the optimization of a multimodal real function that depends on a string of binary integers. The algorithms combine a global random search procedure with a local (neighborhood) search which examines all sequences within a prescribed Hamming distance. The algorithm is applied to the determination of the sequence of air and oxygen breaths that are optimal for estimating lung parameter values. Simulation studies show that the algorithm finds an optimum input sequence 10 bits in length in 100% of the trials, and 20 bits in length in 97% of the trials. Near-optimum values are also located with strings 30 and 40 bits in length using approximately 1000 iterations.>

The successful execution of grasps by a robot hand requires a translation of visual information into control signals to the hand which produce the desired spatial orientation and preshape for an arbitrary object. An approach to this problem, based on separation of the task into two modules, is presented. A vision module is used to transform an image into a volumetric shape description using generalized cones. The data structure containing this geometric information becomes an input to the grasping module, which obtains a list of feasible grasp modes and a set of control signals for the robot hand. Various features of both modules are discussed.>

The authors present a program, SROMA, which is an adaptive scheduler for robots functioning in a mechanical assembly domain. In particular, they describe: (1) the underlying scheme for representing an assembly task and the assembly plan for that task; and (2) the underlying control scheme for combining goal-directed and data-driven mechanisms into a coherent process. The feasibility of the authors' approach to the online scheduling for assembly robots based on the schemes is demonstrated by some preliminary simulation results.>

We present a method for the estimation of the filtering properties of living tissue from the experimental output data stimulated by inputting sinusoidal electric signals through intramuscular electrodes in the lower leg muscle. This method is based on approximating the tissue filter by a rational transfer function. The estimation procedure consists of three parts: estimation of frequency response from sinusoidal input and output data using a correlation method, recursive weighted least square (RWLS) estimation to synthesize a continuous transfer function from the frequency response data and discretization of the transfer function. The estimated "tissue filter" is then used in recovering the intramuscular signal from the surface signal. An example of inverse filtering real EMG waveforms using the estimated tissue filter is given.

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.

In this paper the motion planning problem for multiple mobile robots is addressed. Conventional methods of planning the motion for a single moving object are based on the assumption of a static environment, and so they cannot be used here because each of the robots is in a dynamic environment consisting of other moving robots. Centralized approaches to the multiple moving objects problem were shown to be intractable. In order to find a practical solution for the problem, it is necessary to reduce the complexity of it by decomposing the problem and introducing various heuristic techniques. We are proposing here a decentralized approach which is based on the decomposition of the problem into two subproblems: the global path planning problem and the local path replanning problem. This approach is based on a framework of problem solving using a group of intelligent agents.

We present a method for the estimation of intramuscular electromyographic (EMG) signals from the given surface EMG signals. This method is based on representing the surface EMG signal as an autoregressive (AR) time series model with a delayed intramuscular EMG signal as an input. Assuming that prototypes of intramuscular and surface EMG signals are available, the parameters of the time series model that transforms the intramuscular signal to the surface signal are identified. The identified model is then used in estimating the intramuscular signal from the surface signal. This method is illustrated using real EMG waveforms.

This paper describes the principles of a new method of robot control which mimics the reflex control of human motion. The concept of an artificial or synthetic reflex arc is defined, using the biological reflex as reference. The view of reflex control as an "experiential expert system" is explained. Hardware and software elements necessary for the implementation of external reflex control are described, The set of artificial reflex arcs which can reproduce the desired functional motion is identified on the basis of singular events occurring during the execution of the the motion. Applications of the theory to control of knee and leg motion in rehabilitative robotics are discussed in detail.

Algorithms for the identification of the following robot dynamics parameters are presented: link center of mass positions, link inertia matrices, joint friction coefficients, load mass, load center of mass position and load inertia matrix. Required measurements are the joint forces/torques and the resulting joint motion (joint position, velocity and acceleration). Software implementations are used to show how the algorithms perform on simulated measurement data.

Large variations in the acoustic patterns of successive utterances of the same word may occur with speaker-independent speech recognition system or with single speakers afflicted with certain pathologies, such as cerebral palsy. If the variability is sufficiently large , the performance of time-warping algorithm based on dynamic programming may be unsatisfactory. This paper describes the theoretical development , implementation and a test of an algorithm designed to overcome this difficulty in situations where most of the variability occurs in successive utterances of the consonants. The results obtained using the test data showes (90%) correct recognition rate while Dynamic Programming algorithm gave only (55.5%) using the same data.

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.

An algorithm for the estimation of parameters in robots with rotary joints is presented. The parameters are the center of mass and inertia matrix for each link, and Coulomb friction and viscous damping in each joint. We also present an algorithm for identification of the center of mass and inertia matrix for the load. Data required are joint angles and torques, and the first and second derivatives of the joint angles. The algorithm for the load also requires force sensing such that net force and torque on the load can be computed.

In this paper we present a new approach to the recognition of consonants. This approach is based on the modelling of the vocal tract by two acoustic cavities with the voicing source at the input of the first cavity and the fricative noise or pulse source at the junction of the two cavities. The separation of the system into these dual cavities results in an ARMA structure for the acoustic signal.

The response of human operators in control systems to electrocutaneous stimulation inputs was investigated. Human subjects were asked to track two-dimensional tactile inputs (applied at the waist) by means of force applied to a two-axis isometric control stick. Stimulation and force signals recorded from the experiment were used to obtain mathematical models of the subjects' input-output characteristics. Coherence analysis showed that the operators' behavior could be represented by linear models in the frequency range of 0 to 0.8 rad/s and that cross-coupling between the two axes is negligible. The implications of the results in the design of artificial kinesthetic feedback systems to aid in the rehabilitation of traumatic paraplegics are discussed.

An approach to modeling human driver behavior in single-lane car following which is based on a finite-state decision structure is considered. The specific strategy at each point in the decision tree was obtained from observations of typical driver behavior. The synthesis of the decision logic is based on position and velocity thresholds and four states defined by regions in the phase plane. The performance of the resulting assumed intuitively logical model was compared with actual freeway data. The match of the model to the data was optimized by adapting the model parameters using a modified PARTAN algorithm. The results indicate that the heuristic model behavior matches actual car-following performance better during deceleration and constant velocity phases than during acceleration periods.

Two heuristic methods for locating the global optimum of a multimodal performance index surface are described. One method is based on a modified random creep procedure which first locates a local minimum and then searches the parameter space with vector steps whose mean length gradually increases. The second is a modification of the Kiefer-Wolfowitz stochastic approximation procedure, in which a random perturbation is added to each measurement. Both algorithms are compared by applying them to finding the roots of a nonlinear algebraic equation and to a constrained dynamic optimization problem.

One of the major limitations of the electronic analog computer is its inability to perform directly an integration with respect to a dependent variable. This paper reviews the usual methods of overcoming this limitation, describes the results on an attempt to use Padé time-delay units in generalized integration, and presents the development of a new analog integrator based on a simple numerical integration formula. The integrator can be instrumented using standard analog computer components. The performance of the device is illustrated with several examples.