Swarm robots can achieve effective task execution via closed-loop motion control. However, such a goal can only be realized through accurate localization of the swarm. Past approaches have focused on addressing this issue using external sensors, static sensor networks, or through active localization—requirements that may restrict the motion of the swarm or may not be achievable in practice. We present a tether-based strategy that achieves closed-loop swarm-motion control by using a secondary team of mobile sensors. These sensors form a wireless tether that allows the swarm to indirectly sense a home base or a landmark, and to compensate for the accumulated motion errors via a closed-loop control strategy. The proposed strategy is the first to use a tether of mobile sensors that can dynamically reshape and reconnect to various points in the environment to achieve closed-loop motion control. The novelty of the strategy is in its ability to adapt to any swarm motion considered, and to be applied to swarms with limited sensing capabilities and knowledge of their environment. The performance of the proposed strategy was validated through extensive experiments.

For social robots to effectively engage in human-robot interaction (HRI), they need to be able to interpret human affective cues and to respond appropriately via display of their own emotional behavior. In this article, we present a novel multimodal emotional HRI architecture to promote natural and engaging bidirectional emotional communications between a social robot and a human user. User affect is detected using a unique combination of body language and vocal intonation, and multimodal classification is performed using a Bayesian Network. The Emotionally Expressive Robot utilizes the user's affect to determine its own emotional behavior via an innovative two-layer emotional model consisting of deliberative (hidden Markov model) and reactive (rule-based) layers. The proposed architecture has been implemented via a small humanoid robot to perform diet and fitness counseling during HRI. In order to evaluate the Emotionally Expressive Robot's effectiveness, a Neutral Robot that can detect user affects but lacks an emotional display, was also developed. A between-subjects HRI experiment was conducted with both types of robots. Extensive results have shown that both robots can effectively detect user affect during the real-time HRI. However, the Emotionally Expressive Robot can appropriately determine its own emotional response based on the situation at hand and, therefore, induce more user positive valence and less negative arousal than the Neutral Robot.

Locating a mobile target, untrackable in real-time, is pertinent to numerous time-critical applications, such as wilderness search and rescue. This paper proposes a hybrid approach to this dynamic problem, where both static and mobile sensors are utilized for the goal of detecting a target. The approach is novel in that a team of robots utilized to deploy a static-sensor network also actively searches for the target via on-board sensors. Synergy is achieved through: 1) optimal deployment planning of static-sensor networks and 2) optimal routing and motion planning of the robots for the deployment of the network and target search. The static-sensor network is planned first to maximize the likelihood of target detection while ensuring (temporal and spatial) unbiasedness in target motion. Robot motions are, subsequently, planned in two stages: 1) route planning and 2) trajectory planning. In the first stage, given a static-sensor network configuration, robot routes are planned to maximize the amount of spare time available to the mobile agents/sensors, for target search in between (just-in-time) static-sensor deployments. In the second stage, given robot routes (i.e., optimal sequences of sensor delivery locations and times), the corresponding robot trajectories are planned to make effective use of any spare time the mobile agents may have to search for the target. The proposed search strategy was validated through extensive simulations, some of which are given in detail here. An analysis of the method's performance in terms of target-search success is also included.

Regular exercise has immediate and long-term benefits for people of all ages. Maintaining an adequate amount of daily exercise is important to overall health and wellbeing. Our research focuses on the development of a socially assistive robot, Salt, to facilitate different upper body exercises. During the exercises, the robot is uniquely able to autonomously detect a user's affect and engagement as well as measure their heart rate to prevent overexertion. A robot emotion model using an n th order Markov Chain is used to determine the robot's appropriate emotions during interactions based on user affect and engagement, and its own emotion history. Human-robot interaction experiments were conducted to investigate perceived usefulness and acceptance. The results showed that most users were engaged and had positive valence towards the robot during the interactions. Post-experiment questionnaire results also showed they were able to detect the robot's emotions and enjoyed interacting with it.

Time-phased sensor-network deployment refers to the delivery of a set of sensors to their predetermined locations at exact times by a fleet of vehicles. Applications for such network deployments include wilderness search and rescue (WiSAR) and wildfire monitoring, where desirable resource management would imply allowing the vehicles to perform other tasks between deliveries. The goal of this paper is, thus, to formulate and solve a vehicle-routing problem (VRP) for such just-in-time time-phased sensor-network deployments. The proposed optimization method for the modified VRP outlined herein has two primary novelties: 1) the consideration of spare time as the objective function and 2) the use of a targeted local-search (LS) method. The spare-time objective function was formulated to address the uniqueness of the modified routing problem at hand. The targeted LS algorithm, on the other hand, was developed to tangibly improve the efficiency of the search for the optimal values of the chosen objective function. The proposed vehicle-route-planning method was validated via a range of simulated WiSAR scenarios, some of which are included herein. The robustness of the method to variations in problem parameters was also investigated.

This paper discusses the time-phased deployment of wireless sensor networks, applied to surveillance areas growing in time. The focus herein is on the planning of the time-efficient delivery of static sensors to their designated nodes, given a network configuration. The novelty of the proposed strategy is in that it determines optimal delivery plans for spatio-temporally constrained static-sensor networks using multi-robot teams. The proposed sensor delivery planning strategy starts with an already determined (optimal) network plan specified by sensor placement locations (i.e., nodes) and deployment times. Thus, the goal at hand is to determine the optimal routes for the robots delivering the sensors to their intended locations just-in-time. The travel routes are, thus, determined to maximize spare time for the robots between the nodes. The problem is similar to the multiple travelling salesperson problem, but, with temporal constraints. Namely, sensors must be delivered to their designated nodes at designated (optimized) times in order to maintain the optimal deployment of the network configuration. Furthermore, the strategy is designed to be adaptive to new information that can become available during the search for the mobile target, allowing for replanning of the sensor network (i.e., new sensors locations and new deployment times). Numerous simulated experiments were conducted to validate the proposed strategy.

Millirobots have increasingly become popular over the past several years, especially for swarm-behavior studies, allowing researchers to run experiments with a large number of units in limited workspaces. However, as these robots have become smaller in size, their sensory capabilities and battery life have been reduced. A number of these have also been customized, with few off-the shelf components, exhibiting integral (i.e., non-modular) designs. In response to the above concerns, this paper presents a novel open-source millirobot with a modular design based on the use of easily sourced elements and off-the-shelf components. The proposed milli-robot-Toronto (mROBerTO), is a 16×16 mm2robot with a variety of sensors (including proximity, IMU, compass, ambient light, and camera). mROBerTO is capable of formation control using an IR emitter and detector add-on. It can also communicate via Bluetooth Smart, ANT+, or both concurrently. It is equipped with an ARM processor for handling complex tasks and has a flash memory of 256 KB with over-the-air programming capability.

This paper presents a novel strategy for the on-line planning of optimal motion-paths for a team of autonomous ground robots engaged in wilderness search and rescue (WiSAR). The proposed strategy, which forms part of an overall multirobot coordination (MRC) methodology, addresses the dynamic nature of WiSAR by: 1) planning initial, time-optimal, and piecewise polynomial paths for all robots; 2) implementing and regularly evaluating the optimality of the paths through a set of checks that gauge feasibility of path-completion within the available time; and 3) replanning paths, on-line, whenever deemed necessary. The fundamental principle of maintaining the optimal deployment of the robots throughout the search guides the MRC methodology. The proposed path-planning strategy is illustrated through a simulated realistic WiSAR example, and compared to an alternative, nonprobabilistic approach.

This paper presents a novel method for the optimal deployment of multi-robot teams for autonomous, coordinated wilderness search and rescue. The new concept of iso-probability curves, used to represent the time-varying prediction of a lost person's probable location within the search area, is utilized to effectively distribute the search effort. The proposed method can be used for initial deployment, as well as subsequent on-line re-deployment to address the dynamic nature of the search for a moving lost person in a growing search area with varying terrain. The modularity of the proposed method allows the user to define and utilize different objective functions and weigh them according to the goal at hand. The two specific objective functions considered in this paper are (minimizing) search time and (maximizing) the probability of success. A simulated realistic wilderness search scenario demonstrates the integration of optimal deployment within the overall search methodology.

This paper presents a novel modular methodology for predicting a lost person's (motion) behavior for autonomous coordinated multirobot wilderness search and rescue. The new concept of isoprobability curves is introduced and developed, which represents a unique mechanism for identifying the target's probable location at any given time within the search area while accounting for influences such as terrain topology, target physiology and psychology, clues found, etc. The isoprobability curves are propagated over time and space. The significant tangible benefit of the proposed target-motion prediction methodology is demonstrated through a comparison to a nonprobabilistic approach, as well as through a simulated realistic wilderness search scenario.

A novel line-of-sight sensing-based modelless guidance strategy is presented for the autonomous docking of robotic vehicles. The novelty of the proposed guidance strategy is twofold: 1) applicability to situations that do not allow for direct proximity measurement of the vehicle and 2) ability to generate short-range docking motion commands without a need for a global sensing-system (calibration) model. Two guidance -based motion-planning methods were developed to provide the vehicle controller with online corrective motion commands: a passive-sensing-based and an active-sensing-based scheme, respectively. The objective of both proposed guidance methods is to minimize the accumulated systematic errors of the vehicle as a result of the long-range travel, while allowing it to converge to its desired pose within random-noise limits. Both techniques were successfully tested via simulations and experiments, and are discussed herein, in terms of convergence rate and accuracy, in addition to the types of localization problems for which each method could be specifically more suitable.

This paper presents a novel agent-based method for the dynamic coordinated selection and positioning of active-vision cameras for the simultaneous surveillance of multiple objects-of-interest as they travel through a cluttered environment with a-priori unknown trajectories. The proposed system dynamically adjusts not only the orientation but also the position of the cameras in order to maximize the system's performance by avoiding occlusions and acquiring images with preferred viewing angles. Sensor selection and positioning are accomplished through an agent-based approach. The proposed sensing-system reconfiguration strategy has been verified via simulations and implemented on an experimental prototype setup for automated facial recognition. Both simulations and experimental analyses have shown that the use of dynamic sensors along with an effective online dispatching strategy may tangibly improve the surveillance performance of a sensing system.

In this paper, a neural-network-based guidance methodology that utilizes line-of-sight based task-space sensory feedback is proposed for the localization of autonomous robotic vehicles. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation). Herein, the proposed neural-network (NN) based guidance methodology is implemented on-line during the final stage of the vehicle's motion (i.e., docking). The systematic motion errors of the vehicle are reduced iteratively by executing the corrective motion commands, generated by the NN, until the vehicle achieves its desired pose within random noise limits. The guidance methodology developed was successfully tested via simulations for a 6-dof (degree-of-freedom) vehicle and via experiments for a 3-dof high-precision planar platform

In this paper, a novel reconfigurable surveillance system that incorporates multiple active-vision sensors is presented. The proposed system has been developed for visual-servoing and other similar applications, such as tracking and state estimation, which require accurate and reliable target surveillance data. In the specific implementation case discussed herein, the position and orientation of a single target are surveyed at predetermined time instants along its unknown trajectory. Dispatching is used to select an optimal subset of dynamic sensors, to be used in a data-fusion process, and maneuver them in response to the motion of the object. The goal is to provide information of increased quality for the task at hand, while ensuring adequate response to future object maneuvers. Our experimental system is composed of a static overhead camera to predict the object's gross motion and four mobile cameras to provide surveillance of a feature on the object (i.e., target). Object motion was simulated by placing it on an xy table and preprogramming a path that is unknown to the surveillance system. The selected cameras are independently and optimally positioned to estimate the target's pose (a circular marker in our case) at the desired time instant. The target data obtained from the cameras, together with their own position and bearing, are fed to a fusion algorithm, where the final assessment of the target's pose is determined. Experiments have shown that the use of dynamic sensors, together with a dispatching algorithm, tangibly improves the performance of a surveillance system

This paper presents a novel method for the scheduling and control of flexible manufacturing cells (FMCs). The approach employs automata, augmented by time labels proposed herein, for the modeling of machines, transportation devices, buffers, precedence constraints, and part routes. Ramadge-Wonham's supervisory-control theory is then used to synthesize a deadlock-free controller that is also capable of keeping track of time. For a given set of parts to be processed by the cell, A/sup */ search algorithm is subsequently employed using a proposed heuristic function. Three different production configurations are considered: Case 1) each part has a unique route; Case 2) parts may have multiple routes, but same devices in each route; and Case 3) parts may have multiple routes with different devices. The proposed approach yields optimal deadlock-free schedules for the first two cases. For Case 3, our simulations have yielded effective solutions but in practice, optimal deadlock-free schedules may not be obtainable without sacrificing computational time efficiency. One such nontime-efficient method is included in this paper. The proposed approach is illustrated through three typical manufacturing-cell simulation examples; the first adopted from a Petri-net-based scheduling paper, the second adopted from a mathematical-programming-based scheduling paper, and the third, a new example that deals with a more complex FMC scenario where parts have multiple routes for their production. These and other simulations clearly demonstrate the effectiveness of the proposed automata-based scheduling methodology.

This correspondence presents a novel online trajectory-planning method for the autonomous robotic interception of moving targets in the presence of dynamic obstacles, i.e., position and velocity matching (also referred to as rendezvous). The proposed time-optimal interception method is a hybrid algorithm that augments a novel rendezvous-guidance (RG) technique with the velocity-obstacle approach, for obstacle avoidance, first reported by Fiorini and Shiller. The obstacle-avoidance algorithm itself could not be used in its original form and had to be modified to ensure that the online planned path deviates minimally from the one generated by the RG algorithm. Extensive simulation and experimental analyses, some of which are reported in this correspondence, have clearly demonstrated the tangible time efficiency of the proposed interception method.

This paper presents a novel method for the time-optimal interception of moving objects in the presence of obstacles. The proposed method not only provides time-optimal positional interception, but, also simultaneously matches the velocity at which the object is moving in a dynamic environment with static and/or moving obstacles: A rendezvous-guidance technique is utilized in conjunction with a modified cell-decomposition method. Simulation and experimental results clearly demonstrate the efficiency of the proposed interception method.

In this paper, an active line-of-sight-sensing (LOS) methodology is proposed for the docking of autonomous vehicles/robotic end-effectors. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation). In such instances, a guidance-based technique must be employed to move the vehicle to its desired pose using corrective actions at the final stages of its motion. The objective of the proposed guidance method is, thus, to successfully minimize the systematic errors of the vehicle, accumulated after a long-range motion, while allowing it to converge within the random noise limits via a three-step procedure: active LOS realignment, determination of the new (actual) location of the vehicle, and implementation of a corrective action. The proposed system was successfully tested via simulation for a three degree-of-freedom (dof) planar robotic platform and via experiments.

This paper presents a novel method for the coordinated selection and positioning of groups of active-vision cameras for the surveillance of an object-of-interest as it travels through a multi-object environment with an a priori unknown trajectory. Dynamic sensing-system reconfiguration is achieved through the use of multiple real and virtual agents. The proposed reconfiguration algorithm is implemented on an experimental prototype setup for automated facial recognition of a single subject in a cluttered environment.

This paper presents a novel method for the time-optimal rendezvous of an autonomous vehicle with moving targets in the presence of dynamic obstacles. This interception problem is addressed by utilizing the velocity obstacle approach of Fiorni and Shiller augmented with a rendezvous-guidance algorithm. However, the velocity obstacle approach had to be modified to ensure that the real-time planned path deviates minimally from the one generated by the rendezvous-guidance algorithm. Simulation and experimental results, some of which are presented in this paper, clearly demonstrate the efficiency of the proposed interception method.

Recent research has considered robotic machining as an alternative to traditional computer numerical control machining, particularly for prototyping applications. However, unlike traditional machine tools, robots are subject to relatively larger dynamic disturbances and operate closer to their torque limits. These factors, combined with inaccurate manipulator and machining process models, can cause joint actuator saturation during operation. This paper presents a trajectory planner that will reduce torques that are near saturation by generating trajectories with a weighted pseudoinverse. Using a relative Jacobian, the tool path is resolved into joint trajectories at the acceleration level. This paper presents a new method for selecting the weighting matrix based on the proximity of the joint torques to saturation limits. This weighting reduces the joint accelerations contributing the most to the torques near saturation, thereby reducing the joint torques. The accelerations of other joints increase to satisfy the increased demand. The effectiveness of the acceleration and torque redistribution algorithm has been demonstrated via extensive simulations.

This paper outlines an approach to the coordination of multiple mobile sensors for the surveillance of a single moving target. A real-time dispatching algorithm is used to select and position groups of sensors in response to the observed object motion. The aim is to provide robust, high-quality data while ensuring that the system can react to unexpected object manoeuvres. Sensors are assigned to collect data at specific points on the object trajectory. A dispatching strategy learned via reinforcement learning is used to control the sensor poses with respect to these points. In using the learned strategy, each sensor adopts an egocentric view of the system state to determine the most appropriate action. Simulations demonstrate the performance of the RL-based dispatcher, in comparison to similar static-sensor systems.

A real-time trajectory planner is presented. A two-pronged approach provides alternative trajectories in response to disturbances encountered during a two-manipulator machining application. First, based on the current torque demand, a weighted pseudo-inverse technique is used to reduce the joint accelerations contributing the most to the saturated joint torques. Second, the null space of the relative Jacobian is exploited to minimize the compliance of the system. Influence coefficients are used to share the null space between the cost function and penalty functions.

This paper presents a novel method for the scheduling and control of flexible manufacturing cells (FMCs). The approach employs automata. Augmented by time labels proposed in the paper, for the modeling of machines, transportation devices, buffers, part types, precedence constraints, and part routes. The Ramadge-Wonham's supervisory-control theory is then used to synthesize a controller for the workcell representing its deadlock-free behaviour and capable of keeping track of time. For any given batch of parts to be processed by the FMC, a best-first (BF) graph-search strategy is subsequently employed to determine an optimal, or near-optimal, deadlock-free schedule using a novel heuristic function. The proposed approach is illustrated through a typical FMC example, adopted from [HH Xiong, M Zhou, and RJ Caudil IEE Conf. On Robotics and Automation, Albuquerque, USA, Vol. 3, pp. 2793-2797, 1996] to demonstrate the significant reduction in node generation.

This paper addresses the accurate positioning (localization) of a robotic end-effector, undertaking high-precision tasks, by introducing a novel proximity sensing-based point-to-point motion guidance algorithm. The proposed task-space sensing system is only employed at the final stages of motion after the long-range positioning of the end-effector fails to move it to its desired location. Three identical sub-systems that are spatially placed are incorporated into the sensing system, each consisting of a laser emitter, a galvanometer, and a corresponding PSD (position sensitive diode) that is placed directly on the end-effector. The three-step guidance algorithm uses the laser beams and the offsets they produce along the PSDs to guide the end-effector from its actual pose (position and orientation) to its desired pose. The proposed system (sensing and guidance algorithm) was successfully tested via simulation on a three degree-of-freedom (dof) planar parallel manipulator.

A novel rendezvous-guidance algorithm is proposed in this paper for the robotic interception of moving objects. The object is assumed to be moving on an a priori unknown trajectory, though, its instantaneous state is visually observed and fed to the proposed algorithm. The output of the guidance algorithm is an instantaneous end-effector velocity command with the largest possible closing velocity component while not violating the rendezvous criteria and the dynamic limitations of the actuators of the robot. The proposed method reduces the interception-times achieved via conventional tracking methods as demonstrated by simulation examples.

In this paper, a multi line-of-sight (LOS) task-space sensing methodology is presented for guidance-based localization of mobile devices (e.g., autonomous vehicles and robots). The mobility requirement of the localization/docking application dictates the minimum number and the type (planar or spatial) of the lines of sight. It is envisioned that, a multi-LOS sensing system will be configured for the task at hand using several, one or two degree-of-freedom (dof), sensing modules. One such module is also proposed in this paper: it comprises a laser source, a (1 or 2 dof) galvanometer mirror and a photodetector. A guidance algorithm would only be invoked at the final stages of vehicle/robotic-end-effector motion after the long-range positioning phase has failed to locate the vehicle at its desired pose (position and orientation). By utilizing a multi-LOS based sensing system the guidance algorithm would successfully minimize the systematic errors of the vehicle, while allowing it to converge to its desired pose within the random noise limits. This has been verified in both simulation and experiments, as presented herein.

In this paper, the implementation of a novel surveillance system that incorporates multiple active-vision sensors controlled by a real-time dispatching algorithm is presented. The proposed system improves reliability and accuracy of target surveillance-tracking systems used for visual-servoing and other similar applications. Experiments using a dispatched system have shown that the use of dynamic sensors can improve the performance of a surveillance system, primarily, due to the following factors: (i) decrease in the uncertainty associated with the object's estimated pose, (ii) increase in robustness of the system due to its ability to cope with a wider range of a priori unknown object trajectories, and (iii) increase in reliability through sensory fault tolerance.

A typical problem in flexible manufacturing cells (FMCs) capable of producing multiple parts through multiple routes is optimal routing, where decisions regarding choosing alternative production routes have to be made at certain system states. This paper presents a novel method for determining deadlock-free decisions that optimize a given performance criterion. The approach employs automata, augmented by time labels, for the modeling of machines, transportation devices, buffers, part types, precedence constraints, and part routes. The Ramadge-Wonham's supervisory-control theory is then used to synthesize a controller for the workcell representing its maximally deadlock-free behavior and one that is capable of keeping track of time. This supervisor is utilized to determine the set of optimal decisions. The proposed approach is illustrated through a typical FMC simulation example.

The paper presents a methodology for determining the initial configuration of a set of sensors for a surveillance task. It serves to complement a dynamic dispatching methodology, which selects and maneuvers subsets of sensors to achieve optimal data acquisition in real-time. Specifically, given a priori information about the expected object trajectory, the initial sensor poses are determined such that the sensing-system effectiveness is maximized. This is achieved using a constrained, nonlinear, direct search method in combination with simulations of the sensing-system performance (i.e., dynamic dispatching to adjust the sensor poses in response to the object motion).

Flexible manufacturing systems (FMSs) may comprise a number of workcells where production resources are shared among the cells for a variety of practical reasons. Although the utilization of independent workcells with sharing resources improves the flexibility of FMSs, avoiding deadlocks is essential for their successful implementation. The paper introduces a methodology for the synthesis of a set of conflict- and deadlock-free supervisors to individually control every workcell within a FMS inter-related by common (shared) resources. The proposed methodology is based on extended Moore automata and controlled-automata theories.

An Ideal Proportional Navigation Guidance (IPNG) technique is applied to the problem of intercepting a fast maneuvering object in real time using an industrial robotic manipulator. A hybrid method is utilized, in which IPNG is combined with a conventional tracking method to ensure smooth interception. The method is suited for the interception of both fast maneuvering as well as slow maneuvering targets, and does not require long term target trajectory prediction. Experimental results are reported.

Sensory data must be collected in real time for the majority of autonomous decision making tasks, such as target tracking, surveillance and navigation. The use of multiple sensors may significantly improve the quality and robustness of the data. Given an environment containing a set of mobile sensors, capable of altering their position and orientation, this work addresses the problem of selecting and maneuvering subsets of these sensors for optimal data acquisition in realtime. A heuristic approach to the dispatching problem suitable for on-line implementation is illustrated by a computer simulated example.

The paper presents a novel and generic PC/PLC-based software/hardware architecture for the control of flexible manufacturing workcells. The proposed implementation methodology is based on the utilization of any one of the available formal discrete-event-system control theories in conjunction with state-of-the-art industrial programmable-logic controllers (PLCs). The specific control theory used is a combination of extended Moore automata and Ramadge-Wonham automata that has been developed by our research group. The modular control software architecture has been developed for MS-Windows environments. The effective graphical user interface provides a transparent programming environment.

Traditionally, the function of a workcell supervisor has been to coordinate the various elements comprising the workcell for the production of a specific a-priori set of parts using a predetermined set of machines. Correspondingly, current workcell supervisor synthesis methodologies assume that the part production mix remains constant. In practice, however, the product mix may change over time. New part types are required to be processed, while other (nominal) part types may be temporarily discontinued. The workcell supervisory control approach proposed in this paper is that of utilizing a pair of non-communicating independent supervisors, working in concert to achieve the production of nominal and a-priori unplanned-for new part types: nominal and complementary supervisors. The nominal supervisor is responsible for controlling the behavior of the nominal workcell, producing the set of a-priori planned part types, while the complementary supervisor controls the flow of the unplanned-for new part types, where each supervisor is synthesized individually.

The utilization of flexible manufacturing workcells (FMCs) to produce families of parts in many possible orders of operations and choices of different machines is advantageous. The modeling and control of such discrete-event systems (DESs) have generally been performed in a hierarchical structure. Despite intensive research on the theoretical control of DESs, however, current techniques such as controlled automata can still primarily be used for the supervisory control of simple cells. In the paper, a modeling and control synthesis technique is presented for FMCs that allow part-routing flexibility. The proposed methodology combines extended Moore automata (EMA) and controlled-automata theories to synthesize supervisors for such FMCs. EMA-based supervisors have the capability to read (receive) multiple pieces of information regarding the behavior of the FMC. Based on this information they can explicitly generate control commands issued to cell devices. In order to significantly reduce the state-space of EMA-based supervisors without affecting the behavior enforced on the (controlled) FMC, a minimization procedure is also outlined.

Presents an approach to online, robot-motion planning for moving-object interception. The proposed approach utilizes a navigation-guidance-based technique, that is robust and computationally efficient for the interception of fast-maneuvering objects. Navigation-based techniques were originally developed for the control of missiles tracking free-flying targets. Unlike a missile, however, the end-effector of a robotic arm is connected to the ground, via a number of links and joints, subject to kinematic and dynamic constraints. Also, unlike a missile, the velocity of the robot and the moving object must be matched for a smooth grasp, thus, a hybrid interception scheme, which combines a navigation-based interception technique with a conventional trajectory tracking method is proposed herein for intercepting fast-maneuvering objects. The implementation of the proposed technique is illustrated via numerous simulation examples.

Flexible-manufacturing systems (FMSs) may comprise a number of workcells where production resources are shared among the workcells for a variety of practical reasons. Although the utilization of independent workcells with sharing resources improves the flexibility of FMSs, avoiding deadlocks is essential for their successful implementation. This paper introduces a novel methodology for the synthesis of a set of conflict- and deadlock-free supervisors to individually control every workcell within a FMS inter-related by common (shared) resources. The proposed methodology is based on Extended Moore Automata (EMA) and Controlled-Automata theories. A new algorithmic procedure to analyze the concurrent operation of supervisors is also introduced in order to check for the existence or absence of deadlock states.

This paper presents a novel approach to online robot-motion planning for moving-object interception. A hybrid scheme, which combines a navigation-based technique with a conventional trajectory tracking method, is proposed for intercepting fast-manoeuvring objects. The implementation of the proposed technique is discussed via a simulation example.

The modeling and control of flexible-manufacturing workcells (FMCs) has generally been performed in a hierarchical structure, where at the highest level they have been modeled as discrete-event systems (DESs). In this paper, a new DES control synthesis technique that combines Moore automata and controlled-automata theories to synthesize supervisors is presented. The obtained supervisors can monitor multiple pieces of information regarding the behavior of the FMC and can explicitly generate control commands issued to individual cell devices.

A number of active prediction planning and execution (APPE) systems have recently been proposed for robotic interception of moving objects. The cornerstone of such systems is the selection of a robot-object rendezvous-point on the predicted object trajectory. Unlike tracking-based systems, which minimize the state difference between the object and the robot at each control period, in this methodology the robot is sent directly to the selected rendezvous-point. A fine-motion tracking strategy would then be employed for grasping the moving object. Herein, a novel strategy for selecting the optimal (earliest) rendezvous-point is presented. For objects with predictable trajectories, this is a significant improvement over previous APPE strategies which select the rendezvous-point from a limited number of non-optimally chosen candidates.

Circular features have been commonly used in numerous computer vision application areas for 3D pose estimation. However, although the 3D position of the circle's center can be uniquely identified, the solution process yields two different feasible orientations, of which only one is the true solution. In this paper, two methods are presented for the solution of the orientation-duality problem for circular features that are in motion. The first approach is applicable to those features moving on a 3D line with constant orientation or to those which are moving on a plane with general motion. The second approach relies on the existence of additional object features, such as points or lines, which are coplanar to the circular feature. In this case, the circular feature can undergo an arbitrary 3D motion. Experimental results verify the validity of the proposed methods.

An electrooptical proximity sensor capable of measuring the distance and two-dimensional orientation of an object's surface is presented. The robustness of the sensor, targeted for utilization in robotic active sensing, is achieved via the development of a novel amplitude-modulated-based electrooptical transducer, an electronic-interface circuit that provides very good noise immunity and a wide dynamic operating range, and an effective multi-region calibration process that significantly improves pose-estimations at near proximities. An experimental setup was designed and implemented for the development and verification of the proposed proximity sensor in a simulated robotic environment. Experimental results using a variety of calibrated surfaces and materials are presented and discussed. It is shown that average accuracies of 0.01 mm and 0.03/spl deg/ can be achieved. The robustness of the proximity sensor is also verified for potential use in grasping objects with a priori noncalibrated surfaces.

This paper presents an active-vision system for the recognition of 3D objects moving along predictable trajectories. The novelty of this system lies in its unique approach to deal with the problem of moving-object recognition, by integrating object pre-marking, object-trajectory-prediction and time-optimal robot-motion techniques developed in our laboratory. The recognition technique is an extension of our earlier work on static-object recognition. Therein, objects were pre-marked optimally using circular markers, which are utilized during run time for guiding a robot-mounted camera to acquire 2D standard-views for efficient matching purposes. The Kalman-filter based prediction of the object trajectory and the time-optimal movement of the mobile camera for image acquisition are also based on our earlier research results on moving-object interception.

In this paper, a generalized implementation of DES-based supervisory controller methodology, that utilizes recent theoretical advances in conjunction with programmable-logic-controller (PLC) technology, is presented. The two primary advantages of the proposed methodology are: 1) the utilization of limited-size control strategies that can be efficiently generated online, and which are conflict and deadlock free by construction; and 2) the use of PLCs, which are currently the most suitable and widely employed industrial process-control technology. In our proposed methodology, a host personal computer (PC) possesses an online capability for the automatic generation of supervisory-control strategies, and their downloading to a PLC as required. The PLC, in turn, is responsible for monitoring the workcell reacting to events and enforcing device behaviour based on the current control strategy residing in its processor. A supervisory controller developed based on this approach, was successfully implemented for a manufacturing workcell in our laboratory.

Because of their nondeterministic nature of behaviour, the supervisory control of manufacturing systems must be carried out in closed loop. This trait greatly increases the size and complexity of the discrete-event system (DES)-based supervisory-controllers of manufacturing systems. In an attempt to cope with this complexity, a hybrid supervisory controller (HSC) is proposed. It splits operations between its three elements: (i) a DES supervisory controller, which contains the nominal control strategy, (ii) a diagnostic system, which monitors the workcell, and (iii) an alternate strategy driver (ASD), that asserts control by revising the nominal strategy of the DES supervisory controller, whenever events diverge from the states of the DES supervisory controller. This modified approach to DES control provides a more efficient controller than could reasonably be attained using solely a DES supervisory controller.>

This paper investigates the kinematic singularities of a general mechanism (arbitrary kinematic chain), considered as a non-redundant input-output device with equal number of inputs and outputs. The instantaneous kinematics of a mechanism is described by the orientation of a linear subspace, the motion space, inside the velocity space of all potential instantaneous motions. The definition of singularity for a general mechanism is provided. On the basis of the six types of singular configurations introduced in the paper a comprehensive singularity classification is developed.>

This paper presents a new generalized approach to the singularity analysis of a general mechanism (arbitrary kinematic chain), considered as a non-redundant input-output device with equal number of inputs and outputs. The instantaneous kinematics of a mechanism is described by means of a velocity equation, explicitly including not only the input and output velocities but also the passive-joint velocities. A precise definition of singularity of a general mechanism is provided. On the basis of the six types of singular configurations introduced in the paper singularity classifications are proposed.>

A new 3-D object recognition method for robotic assembly workcells is presented. The proposed method is focused on two basic concepts, i.e., active vision and object preconditioning. The main aspects of the proposed system are presented.>

The development of a robust fiber-optic proximity sensor is presented. A method is proposed by which to solve the problem of robustness of amplitude- and phase-modulated electrooptical proximity sensors to variations in surface-reflection characteristics, and to reduce the influence of distance and orientation measurements on one other. The method comprises three different methodologies, which are combined for better efficiency: integration of distance and orientation sensors, a novel polarization-based optical-filtering approach, and active sensing. Emphasis is given to unique solutions for background-noise interference and dynamic-range-limitation problems.

Estimation of 3D information from 2D-image coordinates is a fundamental problem in both machine vision and computer vision. A closed-form analytical solution is presented to the problem of 3D-location estimation of quadratic-curved features based on two images. Compared to previous solution methods, the proposed method has the advantages that it is a closed-form solution method, it is mathematically simpler, and a priori knowledge of the size and shape of the feature is not required. The method is demonstrated for an elliptical feature, using a simulated experimental set-up.>

The authors propose an approach to workcell supervisory control strategy design based on the control theory for discrete event systems (DES). A framework for the modeling and supervisory control of timed DES is presented, in which the concept of forcing as a means of control is introduced, and where hard temporal behavior specifications may be treated. Within this framework, supervisory control strategies are designed according to production specifications and subject to control enforcement constraints.>

In this paper, a new 2D-Shape-encoding scheme is introduced which is based on the idea of the Angle-Of-Sight (AOS). Using this scheme, a shape can be efficiently transformed into a 1D signature by recording the AOS-vs-distance of each boundary point with'respect to a shape-specific Chord-Of-Sight (COS). The COS is selected by using an extension of the notion of shape boundary, to the idea of shape-specific points and the characteristic ellipse (CE). The AOS signature has many important properties including: It is information-preserving, and thus unique; It does not require boundary smoothing; It has its own select- able smoothing property; It can provide a set of multi-scale representations by means of a simple operation; It is transformation-invariant; It is defined at all points; It preserves symmetries. As well, for matching purposes, a two-level matching process is proposed using a global meas- ure (the eccentricity of the CE of a shape) and a dissimilarity measure based on the AOS signature. The encoding and matching techniques developed have been tested %with 35 manufactured objects. The results obtained show that the AOS signature and the two-level-matching technique are quite effective and reliable for the recognition of 2D-shapes of typical manufactured objects.

The design and analysis of a noncontact electrooptical orientation sensor is addressed. The sensor is based on the intensity measurement of modulated light signals reflected from the surface of an object. The experimental setup of the sensor used in this research was designed and manufactured such that two mechanical-design parameters could be investigated. The operation principle of the 1D inclination-angle measurement sensor was verified using the experimental setup as well as through simulation. Experiments undertaken yielded an optimal sensor design that can effectively operate within a range from +20 degrees to -20 degrees independent of its distance from the object (within the operational-distance range of the sensor).>

A closed-form analytical solution to the problem of 3D estimation of circular-feature location is presented. Two different cases are considered: 3D orientation and position estimation when the radius is known and when it is not known. Extension of the method to general 3D quadratic features is also addressed. Simulated experimental results obtained for all three cases verified the analytical method. In the case of real experiments, a set of circles located on a calibration plate, whose locations were known with respect to a reference frame, were used for camera calibration as well as for the application of the method. A sequential compensation procedure was applied to the input gray-level image to compensate for distortion. These results also showed the validity of the process and the applicability of the analytical method.>

The problem of accurate parameter estimation of elliptical shapes is addressed. Three methods based on elliptical Fourier descriptors, area moments, and perimeter moments of 2-D elliptical shapes are presented. The mathematics of these methods is discussed and, for area moments, a set of new formulas based on Gauss's theorem is given. To implement a comparative performance study, a method developed by R. Safaee-Rad et al. (1990), the weighted minimum-square-error (MSE) function, is included, and an objective and independent measure of goodness of fit is used. The performance of these methods is compared for two different cases, that of a perfect simulated ellipse and an imperfect imaged ellipse.>

A new 2D shape-encoding scheme is introduced which is based on the idea of the angle-of-sight. The shape can be efficiently transformed into a 1D signature by recording the angle-of-sight vs. distance of each boundary point with respect to a shape-specific chord-of-sight. The chord-of-sight is selected by using an extension of the motion of shape to geometrical entities, which are not part of the shape boundary-to the idea of shape-specific points and the characteristic ellipse. The angle-of-sight signature is a unique, information-preserving, transformation-invariant shape representation. It is demonstrated that the angle-of-sight shape-encoding scheme is capable of deriving well-behaved noise-tolerant signals, convenient for shape matching through alignment of signatures.>

The main sources of errors that cause distortion in the shape of an ellipse are presented, and methods for compensation of these distortion factors are formulated. A method for estimation of ellipse center coordinates is proposed. A method for the determination of an optimal elliptical fit to a set of input-edge-points is presented. An error function, based on a new geometrical interpretation of an existing error function, for estimation of all five parameters of an ellipse is defined, and an objective and independent measure for the goodness of various fits is given. The proposed error function and two other previously developed error functions are applied to six different situations. The results of the comparative study show that when all the edge points are used for the fit, the performance of the proposed error function is better than the other two.>

Two premarking methods are proposed for a new 3D object recognition system under development at the University of Toronto. In this system, an object is modeled using only a small number of 2D distinct perspective views (standard views) predefined wit the help of markers placed on the object. During the recognition process, a standard view is acquired by first determining its surface normal (standard-view axis), and then aligning the camera's optical axis with it. Standard-view axes are obtained by analyzing the images of the markers. A morphological skeleton transform (MST) is used for the extraction of required marker features. This work presents the analytical solution for the two proposed premarking schemes, based on circular markers, that can be used in acquiring standard views of objects. Specific issues addressed include: the determination of the perspective distortion and its relative importance, the determination of the transformation parameters required for camera alignment, and the use of a class of MST, pseudo-Euclidean skeletons, for feature extraction.>

The development of an experimental tactile sensor utilizing optical fibers is analyzed. In the first stage of this research, a single-point experimental force sensor was designed and manufactured. In the single-point sensor, light from an emitting fiber is reflected from the surface of a cantilever beam into a receiving fiber, and the intensity of the light reflected is continuously measured as the beam deflects due to applied force. The experimental sensor is designed such that it allows variation in design parameters to determine the best set of parameter values for optimal performance of the sensor. In the second stage of the research the optimal design parameters obtained from the single-point sensor experiments were used to design and manufacture a four-point prototype tactile sensor. This prototype sensor makes use of electrical amplification and digital conversion which can enable the force signals to provide tactile information to the controller of a multifingered hand.>

A kinematic position control algorithm is presented for the point-to-point (PTP) motion of two-arm manipulators cooperating on a task. Simple trajectories are assumed for joint displacements between two manipulator configurations by defining joint velocity profiles. The task space errors are continuously monitored during the motion to take corrective action, when necessary, to prevent those errors from exceeding the given tolerance limits. The main objective of this method is to minimize the number of interferences, and, thus, minimize the number of inverse kinematics solutions during the real-time control of the two-arm system. To further increase the efficiency of the algorithm, approximate Jacobian matrices are utilized in the inversion of the kinematic model, as often as possible, to reduce the computational burden on the controller. Computer simulation results are presented for an eight-degree-of-freedom kinematically redundant planar two-arm system.>

Various definitions of task-space tolerances, using geometric pseudoenvelopes, are introduced, and new approaches to "direct" and "inverse" joint-error analysis of robots are formulated. These error analyses lead to the development of a "feasible joint-tolerance domain" concept for use in computer-aided design of robots.

The kinematic transformation between task space and joint configuration coordinates is nonlinear and configuration dependent. A solution to the inverse kinematics is a vector of joint configuration coordinates that corresponds to a set of task space coordinates. For a class of robots closed form solutions always exist, but constraints on joint displacements cannot be systematically incorporated in the process of obtaining a solution. An iterative solution is presented that is suitable for any class of robots having rotary or prismatic joints, with any arbitrary number of degrees of freedom, including both standard and kinematically redundant robots. The solution can be obtained subject to specified constraints and based on certain performance criteria. The solution is based on a new rapidly convergent constrained nonlinear optimization algorithm which uses a modified Newton-Raphson technique for solving a system nonlinear equations. The algorithm is illustrated using as an example a kinematically redundant robot.