Social isolation is highly prevalent among autistic adults and is associated with elevated rates of depression. Sensory sensitivities to smell, lights, and noise can make face-to-face interactions in unfamiliar environments stressful, further limiting opportunities for social engagement. Augmented Reality (AR) telepresence offers a promising alternative by enabling avatar-based social interaction that enhances social presence (the sense of being with others) while allowing users to regulate sensory input. However, little is known about how autistic adults experience engaging with others’ avatars or perceive their own avatar representation. We conducted a three-phase study with eight autistic adults to: (1) explore their preferences for self-representation through their avatars which supports feeling authentically represented during AR interactions, (2) involve them in the participatory design of dyadic and group AR activities, and (3) evaluate social presence in both contexts. Our findings highlight design considerations for AR telepresence systems that can better support the social needs and preferences of autistic adults.

While at first glance one might assume haptic devices for Virtual Reality (VR) would directly translate to use in Augmented Reality (AR), key differences in how users interact with these environments create distinct design constraints. AR allows users to see their surroundings and interact with both physical and virtual elements, requiring virtual objects to convey the same sense of weight as their physical counterparts. However, force feedback solutions developed for the more mature field of VR are not compatible with this blended environment as they occlude the user's hand and prevent interaction with physical objects. We present a novel exoskeleton, Wriality, that utilizes a minimal feedback design to render weight-related forces while maintaining natural engagement with an AR environment, providing a more immersive experience than current state-of-the-art solutions. We present the design and characterization of Wriality and then validate its effectiveness with a human subject experiment. We show that, with force compensation, kinesthetic feedback alone can allow a virtual object to be perceived as equal in weight to its real twin. We show that this force compensation varies with both grip method and with the weight of an object. We then present the subjective feedback on user experience, specifically comfort level, and the effects of the device on realism and immersiveness in AR. Our findings show that the minimal force feedback approach exemplified in Wriality is still effective in providing high levels of immersion by incorporating a force compensation scheme based on intended grip and the object's weight. The results of our user feedback also inform design considerations for future haptic devices targeting AR.

User-Avatar interaction within augmented reality applications is rapidly increasing in frequency. Applications routinely place users in rooms with other, remote users embodied by photorealistic avatars, or require users to work with an avatar of a remote user to complete a task. During these types of interactions, it is often required to modify or redirect the posture of an avatar to achieve goals such as contact with or pointing at an object or maintaining eye gaze with the local user. A key limitation of modern redirection techniques is successfully preserving body posture, a critical component of nonverbal communication. This article presents a new pose-preserving objective function to be used in the multi-objective optimization of an avatar's kinematic configuration. This objective function not only mimics the correct placement of body joints, but also preserves their orientation in space. We have tested this approach against several commonly used and current state-of-the-art redirection techniques and have found that our new approach achieves a significant reduction in targeted redirection error while simultaneously reducing body posture error. Additionally, human subject testing has shown that our new technique provides both a significantly more natural looking redirection and a significantly more realistic and believable overall body posture.

Over four million older adults living in long-term care (LTC) communities experience loneliness, adversely impacting their health. Increased contact with friends and family is an evidence-based intervention to reduce loneliness, but in-person visits are not always possible. Augmented Reality (AR)-based telepresence activities can offer viable alternatives with increased immersion and presence compared to video calls. However, its feasibility as an interaction technology for older adults is not known. In this paper, we detail the design of two dyadic collaborative AR activities that accommodate diminished physical and cognitive abilities of older adults. The findings include a general design framework based on an iterative participatory design focusing on preferred activities, modes of interaction, and overall AR experience of eight older adults, two family members, and five LTC staff. Results demonstrate the potential of collaborative AR as an effective means of interaction for older adults with their family, if designed to cater to their needs.

One in 36 children in the United States has autism. Numerous robotic intervention systems have been proposed for children with autism. It is widely acknowledged that engagement with the robotic system is important for intervention success. Visual attention toward the robot can be used as a proxy for engagement. However, less is known about how to maintain and enhance visual attention within a robotic system, and how the variation of visual attention may influence adaptation of dynamic intervention protocols to improve outcomes. Therefore, in this work, we propose a new metric, System Capture Ratio (SCR), that can be automatically quantified in real-time, to measure a participant’s visual attention within a robotic system designed for joint attention intervention for children with autism. Then, we demonstrate that compared to a static intervention protocol, a dynamic intervention protocol can help sustain visual attention and thus achieve a significant performance improvement. The results support the implementation of adaptive and dynamic robotic intervention protocols for autistic children that are based on SCR, offering suggestions on designing effective multi-session studies for autism intervention.

Remote mixed reality (RMR) allows users to be present and interact in other users’ environments through their photorealistic avatars. Common interaction objects are placed on surfaces in each user's environments and interacting with these objects require users to walk towards them. However, since the user's and their avatar's room's spatial configuration are not exactly similar, for a particular user's walking path, an equivalent path must be found in the avatar's environment, according to its environment's spatial configuration. In this work, we use the concept of mesh deformation to obtain this path, where we deform the mesh associated with the user's environment to fit to the spatial configuration of the avatar's environment. This gives us the corresponding mapping of every point between the two environments from which the equivalent path can be generated.

Multidomain activities that incorporate physical, cognitive, and social stimuli can enhance older adults’ overall health and quality of life. Several robotic platforms have been developed to provide these therapies in a quantifiable manner to complement healthcare personnel in resource-strapped long-term care settings. However, these platforms are primarily limited to one-to-one human–robot interaction (HRI) and thus do not enhance social interaction. In this article, we present a novel HRI framework and a realized platform called socially assistive robotic (SAR)-Connectto foster robot-mediated social interaction among older adults through carefully designed tasks that also incorporate physical and cognitive stimuli. SAR-Connect seamlessly integrates a humanoid robot with a virtual reality-based activity platform and a multimodal data acquisition module including game interaction, audio, visual, and electroencephalography responses of the participants. Results from a laboratory-based user study with older adults indicates the potential ofSAR-Connectthat showed this system could 1) involve one or multiple older adults to perform multidomain activities and provide dynamic guidance, 2) engage them in the robot-mediated task and foster human–human interaction, and 3) quantify their social and activity engagement from multiple sensory modalities.

This study explored the feasibility of automated characterization of functional mobility via an Instrumented Cane System (ICS) within an older adult sample of cane users. An off-the-shelf offset cane was instrumented with inertial, force, and ultrasound sensors for noninvasive data collection. Eighteen patients from a neurological out-patient rehabilitation clinic and nine independently mobile controls participated in standard clinical evaluations of mobility using the ICS while under the care of an attending physical therapist. Feasibility of the ICS was gauged through two studies. The first demonstrated the capability of the ICS to reliably collect meaningful usage metrics, and the second provided preliminary support for the discriminability of high and low falls risk from system-reported metrics. Specifically, the cane significantly differentiated patients and controls (p < 0.05), and a measure of the variation in rotational velocity was associated with total scores on the Functional Gait Assessment (partial r = 0.61, p < 0.01). These findings may ultimately serve to complement and even extend current clinical assessment practices.

Autism Spectrum Disorders (ASD) are characterized by deficits in social communication skills, such as response to joint attention (RJA). Robotic systems have been designed and applied to help children with ASD improve their RJA skills. One of the most important goals of robot-assisted intervention is helping children generalize social interaction skills to interact with other people. Thus predicting children's human-human interaction (HHI) performance based on their human-robot interaction (HRI) process is an important task. However, to the best of our knowledge, little research exists exploring this topic. The Early Social-Communication Scales (ESCS) test is a measurement of nonverbal social skills, including RJA, for young children. We conducted two longitudinal user studies with a robot-mediated RJA system in young children with ASD, followed by HHI sessions consisting of ESCS administration. In this paper, we present findings regarding how to predict participants' RJA performance in HHI based on their head pose patterns in HRI, under a semi-supervised machine learning framework. As a three-class classification problem, we achieved a micro-averaged accuracy of 73.5%, which indicates the potential effectiveness of the proposed method.

The population around the globe is aging rapidly. People are living longer due to an increase in life expectancy and less young people become available to help the elderly population. Therefore, elderly people are facing functional and mental declines that affect their everyday activities and quality of life. Emergence of Socially Assistive Robots (SAR) in recent years and application of animal-like SARs in particular for elder care have shown positive effects including reduced stress, improved communication and social interaction among older adults. However, the existing animal-like SARs are generally passive and limited in terms of gesture-based interaction while the existing humanoid SARs have hard exteriors that prevent them from being in close proximity to the people to have touch-based interaction. In this paper, we have designed and developed a novel active SAR, Rassle, with whole body tactile sensing and movable limbs to take full advantage of touch-based interactions with older adults. Touching can create social bonding and it involves upper limb movement. We believe Rassle can encourage gross motor activity and deliver mental stimuli during interaction. In addition, we have conducted a system validation study with twelve unimpaired adults. Experimental results demonstrate Rassle's ability to deliver mental stimuli with a variety of difficulty levels and show that subjects enjoyed interacting with Rassle.

Children with autism spectrum disorders (ASD) often exhibit impairments in communication and social interaction, and thus face various social challenges in collaborative activities. Given the cost of ASD intervention and lack of access to trained clinicians, technology-assisted ASD intervention has gained momentum in recent years. In this paper, we present a novel collaborative virtual environment (CVE)-based social interaction platform for ASD intervention. The development of CVE technology for ASD intervention may lead to the creation of a novel low-cost intervention environment that will foster collaboration with peers and provide flexibility in communication. The presented Communication-Enhancement CVE system, hand-in-hand, allows two children to play a series of interactive games in a virtual reality environment by using simple hand gestures to collaboratively move virtual objects that are tracked in real time via cameras. Furthermore, these games are designed to promote natural communication and cooperation between the users via the presented Communication-Enhancement mode that allows users to share information and discuss game strategies using gaze and voice-based communication. The results of a feasibility study with 12 children with ASD and 12 typically developing peers show that this system was well accepted by both the children with and without ASD, improved their cooperation in game play, and demonstrated the potential for fostering their communication and collaboration skills.

Research indicates that human-robot interaction can help children with Autism Spectrum Disorder (ASD). While most early robot-mediated interaction studies were based on free interactions, recent studies have shown that robot-mediated interventions that focus on the core impairments of ASD such as joint attention deficit tend to produce better outcomes. Joint attention impairment is one of the core deficits in ASD that has an important impact in the neuropsychological development of these children. In this work, we propose a novel joint attention intervention system for children with ASD that overcomes several existing limitations in this domain such as the need to use body-worn sensors, non-autonomous robot operation requiring human involvement and lack of a formal model for robot-mediated joint attention interaction. We present a fully autonomous robotic system, called NORRIS, that can infer attention through a distributed non-contact gaze inference mechanism with an embedded Least-to-Most (LTM) robot-mediated interaction model to address the current limitations. The system was tested in a multi-session user study with 14 young children with ASD. The results showed that participants' joint attention skills improved significantly, their interest in the robot remained consistent throughout the sessions, and the LTM interaction model was effective in promoting the children's performance.

Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes-feature level fusion, decision level fusion and hybrid level fusion-were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

The population in the US is aging rapidly. By 2030, twenty percent of the US population will be 65 years or older. Both physical and mental health conditions impact older adults' overall quality of life. Recently, Socially Assistive Robotic (SAR) systems have been developed to augment the existing resource-strained healthcare facilities. Several SAR systems were developed to maintain and/or improve older adults' physical, cognitive functioning and social well-being. However, there is limited work on closed-loop SAR systems that can simultaneously engage more than one older adult. In this paper, we developed a robotic coach architecture, RAMU, for interacting with two older adults. In addition, a preliminary study was conducted with four pairs of older adults with and without cognitive impairment. Survey results indicated that participants enjoyed interacting with the robot as well as with each other.

Difficulties in social interaction, verbal and non-verbal communications as well as repetitive and atypical patterns of behavior, characterizes Autism spectrum disorders (ASD). A number of studies indicated that many children with ASD prefer technology and this preference can be explored to develop systems that may alleviate several challenges of traditional treatment and intervention. As a result, recent advances in computer and robotic technology are ushering in innovative assistive technologies for ASD intervention. The current work presents design, development and a usability study of an adaptive multimodal virtual reality-based social interaction platform for children with ASD. It is hypothesized that endowing a technological system that can detect the processing pattern and mental state of the child using implicit cues from eye tracking and electrophysiological, including peripheral physiological and electroencephalography (EEG), signals and adapt its interaction accordingly is of great importance in assisting and individualizing traditional intervention approaches. The presented VR system is based on a virtual reality based social environment, a school cafeteria, where an individual with ASD interacts with virtual characters. An eye tracker, an EEG monitor and biosensors to measure peripheral electrophysiological signals are integrated with the VR task environment to obtain gaze, EEG signals and several peripheral physiological signals in real-time. In the current work, we show how eye gaze and task performance can be used in real-time to adapt intervention in VR. The other signals are collected for offline analysis. The results from a usability study with 12 subjects with ASD are presented to demonstrate the viability of the proposed concepts within the VR system.

In addition to social and behavioral deficits, individuals with Autism Spectrum Disorder (ASD) often struggle to develop the adaptive skills necessary to achieve independence. Driving intervention in individuals with ASD is a growing area of study, but it is still widely under-researched. We present the development and preliminary assessment of a gaze-contingent adaptive virtual reality driving simulator that uses real-time gaze information to adapt the driving environment with the aim of providing a more individualized method of driving intervention. We conducted a small pilot study of 20 adolescents with ASD using our system: 10 with the adaptive gaze-contingent version of the system and 10 in a purely performance-based version. Preliminary results suggest that the novel intervention system may be beneficial in teaching driving skills to individuals with ASD.

Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities with a high prevalence rate. While much research has focused on improving social communication deficits in ASD populations, less emphasis has been devoted to improving skills relevant for adult independent living, such as driving. In this paper, a novel virtual reality (VR)-based driving system with different difficulty levels of tasks is presented to train and improve driving skills of teenagers with ASD. The goal of this paper is to measure the cognitive load experienced by an individual with ASD while he is driving in the VR-based driving system. Several eye gaze features are identified that varied with cognitive load in an experiment participated by 12 teenagers with ASD. Several machine learning methods were compared and the ability of these methods to accurately measure cognitive load was validated with respect to the subjective rating of a therapist. Results will be used to build models in an intelligent VR-based driving system that can sense a participant's real-time cognitive load and offer driving tasks at an appropriate difficulty level in order to maximize the participant's long-term performance.

Independent mobility is an important aspect of an individual's life and must sometimes be augmented by use of an assistive device such as a wheeled walker or cane following a fall, injury, or functional decline. Physical therapists perform functional gait assessments to gauge the probability of an individual experiencing a fall and often recommend use of a walker, cane, or walking stick to decrease fall risk. Our team has developed a clinical assessment tool centered on a standard walking cane embedded system that can enhance a therapist's observation-based gait assessment with use of additional objective and quantitative data. This system can be utilized to detect timing and speed of cane placement, angular acceleration of the cane, and amounts of weight borne on the cane. This system is designed to assist physical therapists at the basic level in collection of objective data during gait analysis, to facilitate appropriate assistive gait device prescription, to provide patients and therapists feedback during gait training, and to reduce wrist and shoulder injuries with cane usage. However, more importantly, using the plethora of objective data that can be obtained from this cane, automated gait analysis and gait pattern classification can be performed to understand a patient's walking performance.

In this paper, we study an unsupervised learning problem where the aim is to cluster the emotional state (excitedness, boredom, or stress) using the biofeedback sensor data while subjects perform tasks under different difficulty levels on the robot assisted rehabilitation system-RehabRoby. The dimension of the training vectors has been reduced by using the Principal Component Analysis (PCA) algorithm after collecting the biofeedback sensor measurements from different subjects under different task difficulty levels to better visualize the sensor data. The reduced dimension vectors are fed into a K-means clustering algorithm. Numerical results have been given to demonstrate that for each training vector, the emotional state decided by the clustering algorithm is consistent with the subjects declaration of his/her emotional state obtained via surveys after performing the task.

Autism Spectrum Disorders (ASD) impact 1 in 88 children in the United States. The cost of ASD intervention is tremendous with huge individual and social consequences. In recent years, robotic systems have been introduced with considerable success for ASD intervention because of their potential to engage children with ASD. In this work, we present a novel closed-loop autonomous robotic system for imitation skill learning for ASD intervention. Children with ASD show powerful impairment in imitation, which has been associated with a host of neurodevelopmental and learning challenges over time. The presented robotic system offers dynamic, adaptive and autonomous interaction for learning of imitation skills with real-time performance evaluation and feedback. The system has been tested in a user study with young children with ASD and typically developing (TD) control sample. Further, the performance of the system was compared with that of a human therapist in the user study. The results demonstrate that the developed robotic system is well-tolerated by the target population, engaged the children with ASD more than a human therapist, and produced performances that were relatively better than that of a human therapist.

Steerable needles can potentially increase the accuracy of needle-based diagnosis and therapy delivery, provided they can be adequately controlled based on medical image information. We propose a novel sliding mode control law that can be used to deliver the tip of a flexible asymmetric-tipped needle to a desired point, or to track a desired trajectory within tissue. The proposed control strategy requires no a priori knowledge of model parameters, has bounded input speeds, and requires little computational resources. We show that if the standard nonholonomic model for tip-steered needles holds, then the control law will converge to desired targets in a reachable workspace, within a tolerance that can be defined by the control parameters. Experimental results validate the control law for target points and trajectory following in phantom tissue and ex vivo liver. Experiments with targets that move during insertion illustrate robustness to disturbances caused by tissue deformation.

Autism Spectrum Disorders (ASD) are characterized by atypical patterns of behaviors and impairments in social communication. Among the fundamental social impairments in the ASD population are challenges in appropriately recognizing and responding to facial expressions. Traditional intervention approaches often require intensive support and well-trained therapists to address core deficits, with many with ASD having tremendous difficulty accessing such care due to lack of available trained therapists as well as intervention costs. As a result, emerging technology such as virtual reality (VR) has the potential to offer useful technology-enabled intervention systems. In this paper, an innovative VR-based facial emotional expression presentation system was developed that allows monitoring of eye gaze and physiological signals related to emotion identification to explore new efficient therapeutic paradigms. A usability study of this new system involving ten adolescents with ASD and ten typically developing adolescents as a control group was performed. The eye tracking and physiological data were analyzed to determine intragroup and intergroup variations of gaze and physiological patterns. Performance data, eye tracking indices and physiological features indicated that there were differences in the way adolescents with ASD process and recognize emotional faces compared to their typically developing peers. These results will be used in the future for an online adaptive VR-based multimodal social interaction system to improve emotion recognition abilities of individuals with ASD.

Multi-robot formation control has been studied in the literature assuming ideal surface that can provide sufficiently large friction as needed. However, in reality the friction is constrained by terrain characteristic and slip occurs due to insufficient friction. In this paper we investigate the effect of slip on the formation control problem of wheeled mobile robots (WMR). We explicitly model the slip-traction characteristic and integrate it into the WMR dynamics. Input output linearization technique is applied to each WMR such that the entire formation is subject to stable l - ψ control.

Chronic hand impairment is common following stroke. This paper presents an actuated thumb exoskeleton (ATX) to facilitate research in hand rehabilitation therapy. The ATX presented in this work permits independent bi-directional actuation in each of the 5 degrees-of-freedom (DOF) of the thumb using a mechanism that has 5 active DOF and 3 passive DOF. The ATX is able to provide considerable joint torques for the user while still allowing backdrivability through flexible shaft transmission. A prototype has been built and experiments were conducted to evaluate the closed-loop position control. Further improvement and future work are discussed.

With Centers for Disease Control and Prevention prevalence estimates for children with autism spectrum disorder (ASD) at 9.1 per 1,000 (1 in 110), identification and effective treatment of ASD is often characterized as a public health emergency. Emerging technology, especially robotic technology, has been shown to be appealing to these children and such interest can be harnessed to address the limitations while providing intervention services to young children with ASD. Generally the spectrum nature of autism calls for intensive, individualized intervention. However, existing robot-mediated systems tend to have limited adaptive capability that limits individualization. Our current work seeks to bridge this gap by developing a novel adaptive and individualized robot-mediated technology for children with ASD. The system is composed of a humanoid robot with its vision being augmented by several wall-mounted cameras for real-time head tracking using a distributed architecture. Based on the cues from the child's head movement, the robot intelligently adapts itself in an individualized manner to promote joint attention. The developed system is validated with two typically developing children. The validation results of the head tracker and the closed-loop nature of interaction are presented.

Manipulative operation of a target point inside a deformable object by a robotic system is necessary in many medical and industrial applications. However, this is a challenging problem because of the difficulty of imposing the motion of the target point by a finite number actuation points located at the boundary of the deformable object. In this paper, an approach towards positioning operation of an internal target point of a deformable object to the desired location by a system of three actuators is presented. First, we design an optimization technique that minimizes the total force applied to the object to determine the location of actuation points to effect the desired motion. Then a position-based PI controller is developed to control the motion of the actuators. A passivity observer and a passivity controller are developed to guarantee the stability of the whole system. The simulation results demonstrate the efficacy of the proposed method.

A new approach to autonomous pursuit evasion by a wheeled mobile robot in the presence of wheel slip is presented. Classical pursuit evasion problem, such as the Homicidal Chauffeur problem, considers the kinematic model of the pursuer and does not consider slip in its trajectory, and thus cannot predict a realistic pursuit evasion scenario. In this work we present a new dynamics-based approach to pursuit evasion problem in the presence of wheel slip. We first show how a feedback linearization controller can achieve capture with wheel slip. We then improve the capture time by designing a new extremum seeking controller that maximizes lateral traction force to effect a sharper but stable turn. The simulation results show the efficacy of our proposed control approach.

In order to model a Wheeled Mobile Robot (WMR) system to improve its maneuverability in a real environment, wheel dynamics, which may violate no-slipping and pure rolling constraints, needs to be studied. In this paper, wheel dynamics with slip is modeled and introduced into the robot overall dynamics. Wheel slip phenomenon is captured and at the same time accommodated. Considering both lateral and longitudinal slip phenomena due to traction, a WMR system becomes an underactuated dynamic system. A time-invariant discontinuous feedback law is developed to asymptotically stabilize the system to the desired configuration with exponential convergence rate. Simulation results are provided to validate the theoretical results.

Shape control of a deformable object by a robotic system is a challenging problem because of the difficulty of imposing shape change by a finite number actuation points to an essentially infinite dimensional object. In this paper, a new approach to shape changing of deformable objects by a system of manipulators is presented. First, an integrated dynamic equation of motion for a system of multiple manipulators handling a deformable object is developed. The initial and the final shapes of the deformable object are specified by curves that represent the boundary of the object. We design an optimization-based planner that minimizes an energy-like criterion to determine the locations of the contact points on the desired curve representing the final shape of the object. The motion of each manipulator is controlled independently without any communication between them. The simulation results demonstrate the efficacy of the proposed method.

This paper presents the evaluation of a robot-assisted rehabilitation system with assist-as-needed and visual error augmentation training methods. In this robot-assisted rehabilitation system, an assistive controller provides robotic assistance to the participant as and when needed. In addition, the position errors that are visually fed back to the participant are amplified to heighten the participant's motivation to improve tracking accuracy. Experimental results on unimpaired participants are presented to demonstrate the efficacy of the enhanced rehabilitation robotic system.

A number of studies in recent years have investigated the dynamic difficulty adjustment (DDA) mechanism in computer games to automatically tailor gaming experience to individual player's characteristics. Although most of these existing works focus on game adaptation based on player's performance, affective state experienced by the players could play a key role in gaming experience and may provide a useful indicator for a DDA mechanism. In this article, an affect-based DDA was designed and implemented for computer games. In this DDA mechanism, a player's physiological signals were analyzed to infer his or her probable anxiety level, which was chosen as the target affective state, and the game difficulty level was automatically adjusted in real time as a function of the player's affective state. Peripheral physiological signals were measured through wearable biofeedback sensors and several physiological indices were explored to determine their correlations with anxiety. An experimental study was conducted to evaluate the effects of the affect-based DDA on game play by comparing it with a performance-based DDA. This is the first time, that is known, that the impact of a real-time affect-based DDA has been demonstrated experimentally.

Breast biopsy guided by imaging techniques such as ultrasound is widely used to evaluate suspicious masses within the breast. The current procedure allows the clinician to determine the location and extent of a tumor in the patient breast before inserting the needle. However, there are several problems with this procedure: the complex interaction dynamics between the needle force and the breast tissue will likely displace the tumor from its original position, necessitating multiple insertions, causing clinicianspsila fatigue, patient's discomfort, and compromising the integrity of the tissue specimen. In this paper, we present a new concept for real-time manipulation of a tumor using a robotic controller that monitors the image of the tumor to generate appropriate external force to position the tumor at a desired location. The idea here is to demonstrate that it is possible to manipulate a tumor in real time by applying controlled external force in an automated way such that the tumor does not deviate from the path of the needle. Experiments on breast phantoms are presented to demonstrate the essence of this concept. The success of this approach has the potential to reduce the number of attempts a clinician makes to capture the desired tissue specimen, minimize tissue damage, improve speed of biopsy, reduce patient discomfort, and eliminate false negative results.

Breast biopsy guided by imaging techniques such as ultrasound is widely used to evaluate suspicious masses within the breast. The current procedure allows the physician to determine the location and extent of a tumor in the patient breast before inserting the needle. However, there are several problems with this procedure: the complex interaction dynamics between the needle force and the breast tissue will likely displace the tumor from its original position necessitating multiple insertions, causing surgeons' fatigue, patient's discomfort, and compromising the integrity of the tissue specimen. In this work, we present a new concept for real-time manipulation of a tumor using a robotic controller that monitors the image of the tumor to generate appropriate external force to position the tumor at a desired location. The idea here is to demonstrate that it is possible to manipulate a tumor in real-time by applying controlled external force in an automated way such that the tumor does not deviate from the path of the needle. A laboratory experiment has been presented on a phantom that demonstrates the essence of this concept. The success of this approach has the potential to reduce the number of attempts a surgeon makes to capture the desired tissue specimen, minimize tissue damage, improve speed of biopsy, and reduce patient discomfort.

This paper presents an overview of our work on the investigation of an affect-sensitive system to be applied in future autism intervention. A physiology-based affect-inference and adaptation framework was proposed, which could endow the assistive intervention technology with the capability of detecting the affective states of a child with autism spectrum disorder (ASD) and responding to them accordingly. Given the importance of affective cues in human-machine interaction and its significant role in autism intervention practice, this work marks an important step towards intelligent intervention systems that embody human-like functionality - affect recognition and adaptation. To account for the spectrum nature of autism and the differences of emotional expression, an individual-specific approach was employed for affective modeling. Two computer-based cognitive tasks were designed for eliciting target affective states considered important in autism intervention. Experimental results on real-time affect recognition and adaptation are presented based on a study of 6 children with ASD in a proof-of-concept experiment (i.e., robot-based basketball game). The preliminary results demonstrated that such an affect-sensitive adaptive system could hold promise for computer/robot-assisted autism intervention.

Investigation into robot-assisted intervention for children with autism spectrum disorder (ASD) has gained momentum in recent years. Therapists involved in interventions must overcome the communication impairments generally exhibited by children with ASD by adeptly inferring the affective cues of the children to adjust the intervention accordingly. Similarly, a robot must also be able to understand the affective needs of these children-an ability that the current robot-assisted ASD intervention systems lack-to achieve effective interaction that addresses the role of affective states in human-robot interaction and intervention practice. In this paper, we present a physiology-based affect-inference mechanism for robot-assisted intervention where the robot can detect the affective states of a child with ASD as discerned by a therapist and adapt its behaviors accordingly. This paper is the first step toward developing ldquounderstandingrdquo robots for use in future ASD intervention. Experimental results with six children with ASD from a proof-of-concept experiment (i.e., a robot-based basketball game) are presented. The robot learned the individual liking level of each child with regard to the game configuration and selected appropriate behaviors to present the task at his/her preferred liking level. Results show that the robot automatically predicted individual liking level in real time with 81.1% accuracy. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via a physiology-based affect recognition technique in real time. This is also the first time that the impact of affect-sensitive closed-loop interaction between a robot and a child with ASD has been demonstrated experimentally.

Breast biopsy guided by imaging techniques such as ultrasound is widely used to evaluate suspicious masses within the breast. Currently, ultrasound imaging does not allow live guidance during breast biopsy. The current procedure allows the physician to determine the location and extent of a tumor in the patient breast before inserting the needle. However, there are several problems with this procedure: the complex interaction dynamics between the needle force and the breast tissue will likely displace the tumor from its original position necessitating multiple insertions, causing surgeons' fatigue, and compromising the structural integrity of the tissue specimen. In this work, we present a new concept for real-time manipulation of a tumor using a robotic force controller that monitors the image of the tumor to generate appropriate force to position the tumor at a desired location. The idea here is to demonstrate that it is possible to manipulate a tumor in real-time by applying controlled external force in an automated way such that the tumor does not deviate from the path of the needle. A laboratory experiment has been presented on a phantom that demonstrates the essence of this concept. The success of this approach has the potential to reduce the number of attempts a surgeon makes to capture the desired tissue specimen, minimize tissue damage, improve speed of biopsy, and reduce patient discomfort.

This paper presents a new approach to robot assisted rehabilitation for stroke patients. The control architecture is represented in terms of hybrid system model combining a high-level and a low-level controller. The main focus of this paper is to present an intelligent controller, which is the high-level controller in the control architecture. The high-level controller is designed to monitor the progress and safety of the rehabilitation task. It also makes decisions on the modification of the task that might be needed for the therapy. Experimental results on unimpaired subjects are presented to demonstrate the efficacy of the high-level controller.

This paper presents a novel affect-sensitive human-robot interaction framework for rehabilitation of children with autism spectrum disorder (ASD). The overall aim is to enable the robot to detect and respond to the affective cues of the children in order to help them explore social interaction dynamics in a gradual and adaptive manner. The first part of the proposed framework, namely the 'affect recognition' module is developed in detail in this paper. Two tasks are designed to elicit the affective states of liking, anxiety, and engagement that are considered important in autism rehabilitation. Affective cues are inferred from psychophysiological analysis that uses subjective reports of the affective states from a therapist, a parent, and the child himself/herself. Comprehensive physiological indices are investigated that may correlate with the affective states of children with ASD. A support vector machines based affect recognizer is designed that yielded reliable prediction with approximately 83% success when using the therapist's reports. This is the first time, to our knowledge, such a human-robot interaction framework for autism rehabilitation is proposed. This is also the first time that the affective states of children with ASD have been experimentally detected via physiology-based affect recognition technique.

This paper presents a novel affect-sensitive human-robot interaction framework for rehabilitation of children with autism spectrum disorder (ASD) where the robot can detect the affective cues of the children implicitly and response to them appropriately. Psychophysiological analysis is performed that uses subjective reports of the affective states from a clinical observer. Comprehensive physiological indices are investigated that may correlate with the affective states of children with ASD. A robot uses a support vector machines based affect model to detect the affective cues. A reinforcement learning based adaptation mechanism is employed to allow the robot to adjust its behaviors autonomously as a function of the predicted children's affective state. Four adolescents diagnosed with high-functioning autism participated in the experiments. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via physiology-based affective modeling technique in real-time. This is also the first time that impact of affect-sensitive interaction between a robot and children with ASD in closed loop has been demonstrated experimentally.

No abstract available.

This paper proposes a new approach, called robust nonlinear analytic redundancy (RNLAR) technique, to sensor and actuator fault detection for input-affine nonlinear multivariable dynamic systems that include most robotic systems. In this approach, both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The proposed RNLAR can be used to design primary residual vectors (PRV) for nonlinear systems to detect faults. A nonlinear PRV design method to detect sensor and actuator faults is proposed where the PRVs are made highly sensitive to the faults and less sensitive to the MPM and process disturbance. Experimental results on a PUMA 560 manipulator are presented to justify the effectiveness of the proposed RNLAR technique

It is argued that a robotic system that is capable of implicit communication with a human and can modify its behavior appropriately based on such communication could be useful. This paper presents a closed loop human-robot interaction framework where the robot can recognize the affective state of the human implicitly and adapt to it appropriately. Affective cues are inferred by a robot in real-time using psychophysiological analysis where the physiological signals are measured through wearable biofeedback sensors. A robot-based basketball game is designed where a robotic "coach" monitors the participant's anxiety to alter the difficulty level of the game in a real-time closed loop manner. The results are compared with situations when anxiety is not monitored and the game is adapted only according to the performance. Results show that monitoring and responding to affective cues led to higher performance improvement of the participants under lower anxiety. This is the first time, to our knowledge, that the impact of such implicit communication between a robot and a human has been demonstrated experimentally

We propose a new control technique for force control on unknown environments. In particular, the proposed approach overcomes the need for precise estimation of environment parameters, which are needed in many system identification-based force control approaches. This framework uses an artificial neural network (ANN)-based proportional-integral (PI)-gain scheduling direct force controller to track the desired force by adjusting control gains based on online parameter estimation. However, the ANN is tolerant to imprecise estimation of environment parameters. Experimental results are presented to demonstrate the efficacy of the proposed control framework. Finally, the advantages and limitations of the proposed controller are discussed

This paper presents a closed loop human-robot interaction framework where a robot can infer the implicit affective cues of the human and respond to them appropriately. Affective cues are inferred by the robot in realtime using psychophysiological analysis where the physiological signals are measured through wearable biofeedback sensors. A robot-based basketball game is designed where a robotic "coach" monitors the participant's anxiety to alter the difficulty level of the game in a real-time closed loop manner according to each participant's performance and anxiety. The results are compared with situations when anxiety is not monitored and the game is adapted only according to the performance. Results show that monitoring and responding to affective cues led to higher performance improvement of the majority of the participants under lower anxiety

It is well known that in human-robot interaction, the effectiveness of a robot varies inversely with the operator engagement in the task. Given the importance of maintaining optimal task engagement when working with a robot, it would be immensely useful to have a robotic system that can detect the level of operator engagement and modify its behavior if required. This paper presents a framework for human-robot interaction in which operator's physiological signals were analyzed to infer his/her engagement level and the robot behavior was adapted as a function of the operator affective state. Peripheral physiological signals were measured through wearable biofeedback sensors and a control architecture inspired by Riley's original information-flow model was developed to implement such human-robot interaction. The results from affect-elicitation tasks for human participants showed that it is possible to detect engagement through physiological sensing in real-time. A teleoperation-based robotic experiment was also conducted to demonstrate that the presented control architecture allowed the robot to adapt its behavior based on operator engagement level.

Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human-robot interaction. Several machine learning techniques have been successfully employed in affect-recognition to predict the affective state of an individual given a set of physiological features. However, a systematic comparison of the strengths and weaknesses of these methods has not yet been done. In this paper we present a comparative study of four machine learning methods - k-nearest neighbor, regression tree, Bayesian network and support vector machine as applied to the domain of affect recognition using physiological signals. The results showed that support vector machine gave the best classification accuracy even though all the methods performed competitively. Regression tree gave the next best classification accuracy and was the most space and time efficient.

Human-robot interaction poses great challenges to disabled people who must be able to control the robot via a limited physical ability. A novel semi-autonomous human-robot system where the robot requires only high-level commands from the human to perform its tasks is proposed. In this system a Morse code inspired but relatively more flexible and robust communication technique generates such high-level commands from electromyogram (EMG) signals. The proposed framework has been implemented to develop a prototypical human-robot interaction system. Initial results from preliminary human subject experiments are encouraging and demonstrative of the advantages of such a human-robot framework.

A novel approach to agile motion coordination for a mobile robot is presented. Agile maneuvering is represented by the ability of the mobile robot to track sharply discontinuous trajectories. A supervisory control framework is developed that orchestrates switching among multiple controllers to track nonsmooth trajectories. The stability of the individual controllers and the internal dynamics are proved. The stability of the switching scheme is analyzed using multiple Lyapunov functions. Results from a detailed computer simulation are presented to demonstrate the efficacy of this new approach.

A novel affect-sensitive human-robot cooperative framework is presented in this paper. Peripheral physiological indices are measured through wearable biofeedback sensors to detect the affective states of the human. Affect recognition is performed through both quantitative and qualitative analyses. A subsumption control architecture that is sensitive to the affective states of the human is proposed for a mobile robot. Human-robot cooperation experiments are performed where the robot senses the affective state of the human and responds appropriately. The results presented here validate the proposed framework and demonstrates a new way of achieving implicit communication between a human and a robot.

A novel implicit communication framework in human-machine interaction that is sensitive to human affective states is presented in this paper. The focus is to achieve detection and recognition of human affect based on physiological signals. This involves building an affect recognition system that accepts as input various physiological parameters and predicts the probable related affective state. Both decision tree and fuzzy logic methodologies have been applied to this problem. This paper presents the results of the two methods and discusses their comparative merit. Three human subject experiments were designed and trials were conducted with six participants. The experimental results demonstrate the feasibility of the proposed implicit human-machine interaction framework.

The use of robots is expected to be pervasive in many spheres of society: in hospitals, homes, offices and battlefields, where the robots will need to interact and cooperate closely with a variety of people. The paper proposes an innovative approach to human-robot cooperation where the robot will be able to recognize the psychological state of the interacting human and modify its (i.e., robot's) own action to make the human feel comfortable in working with the robot. Wearable biofeedback sensors are used to measure a variety of physiological indices to infer the underlying psychological states (affective states) of the human. The eventual idea is to correlate the psychological states with the actions of the robot to determine which action(s) is responsible for a particular affective state. The robot controller will then modify that action if there is a need to alter the affective state. A concept of such a control architecture, a requirement analysis, and initial results from human experiments for stress detection are presented.

A new approach to the fault-accommodating allocation of thruster forces of an autonomous underwater vehicle (AUV) is investigated in this paper. This paper presents a framework that exploits the excess number of thrusters to accommodate thruster faults during operation. First, a redundancy resolution scheme is presented that considers the presence of an excess number of thrusters along with any thruster faults and determines the reference thruster forces to produce the desired motion. This framework is then extended to incorporate a dynamic state feedback technique to generate reference thruster forces that are within the saturation limit of each thruster. Results from both computer simulations and experiments are provided to demonstrate the viability of the proposed scheme.

Describes a biologically-inspired control architecture for the McKibben actuated limbs of a humanoid robot. The antagonistically driven joints are actuated using a biological control model observed in the measurement of human muscle electromyograms (EMG) during reaching movements in the vertical plane. The paradigm uses the summation of tonic and phasic EMG signals to activate the human muscles. The humanoid robot's muscles, actuated by pressure control, are controlled with feedforward pressure patterns analogous to the tonic and phasic activation in the human model. Proprioceptive feedback is utilized in the control architecture to correct for misperceived loading conditions and time variance of the actuators. The control architecture, initial experimental results, and experiments are discussed in the paper. A result of this control paradigm is the realization of actuation with lower stiffness and therefore safer operation for human-humanoid interaction. It is expected that such a motion of the humanoid will closely resemble human motion and will facilitate a more human-friendly human-robot interaction.

The interaction of underwater vehicle-manipulator systems (UVMSs) with the environment is affected by several design constraints such as uncertainty in the model knowledge, presence of hydrodynamic effects, kinematic redundancy of the system, and poor performance of vehicle's actuating system. The paper presents an external force control scheme for UVMSs that does not require dynamic compensation; however, it can benefit from the knowledge of part of the dynamic model. The possible occurrence of loss of contact due to vehicle movement during the task, is also taken into account. A numerical case study shows the application of the proposed technique in a given task.

A unified force control scheme for an autonomous underwater robotic system is proposed. This robotic system is composed of a six degree-of-freedom autonomous underwater vehicle (AUV) and a robotic arm that is mounted on the AUV. First, a dynamic model for the whole underwater manipulator system considering the hydrodynamic effects is derived. This model is then used to implement the proposed unified force control approach, which combines impedance control with hybrid position/force control by means of fuzzy switching to perform autonomous underwater manipulation. This approach combines the advantages of impedance control with hybrid control and has the potential to be effective in underwater environment. Extensive computer simulations are performed to verify the efficacy of the proposed control scheme, and the results are presented.

An approach to the allocation of thruster forces of an autonomous underwater vehicle (AUV) is investigated. Generally, the number of thrusters in an AUV is more than what is minimally required to produce the desired motion. We investigate how to exploit the excess number of thrusters to accommodate thruster faults during operation. First, a redundancy resolution scheme is presented that takes into account the presence of excess number of thrusters along with any thruster faults and determines the reference thruster forces to produce the desired motion. These reference thruster forces are utilized in the thruster controller to generate the required motion. This approach resolves the thruster redundancy in the Cartesian space and allows the AUV to track the task-space trajectories with asymptotic reduction of the task-space errors. Results from both computer simulations and experiments are provided to demonstrate the viability of the proposed scheme. The paper is built upon the preliminary concept proposed earlier by Podder and Sarkar (1999).

An approach to dynamics-based trajectory planning for systems composed of subsystems with different dynamic responses is presented. This approach requires that the task-space trajectory be represented in terms of Fourier series. Various frequency components from this series are then used to generate the reference joint-space trajectories based on the natural frequencies of the respective subsystems. This approach is further extended for trajectory planning of an autonomous underwater vehicle manipulator system, where the underwater vehicle has much slower response than the on-board manipulator. The advantage of this proposed dynamics-based planning methodology is its ability to generate both kinematically admissible and dynamically feasible trajectories. Results from computer simulations are presented to demonstrate the efficacy of the proposed scheme.

This paper describes how fuzzy control can be applied to a sonar-based mobile robot. Behavior-based fuzzy control for HelpMate behaviors was designed using sonar sensors. The fuzzy controller provides a mechanism for combining sensor data from all sonar sensors which present different information. The behavior-based approach is implemented as an individual high priority behavior. The highest level behavior is called the task-oriented behavior, which consists of two subtasks, wall following and goal seeking. The middle level behavior is obstacle avoidance. The lowest level is an emergency behavior. Each behavior was built as an atomic agent based on the intelligent machine architecture (IMA). The results demonstrate that each behavior works correctly. The HelpMate robot can follow the wall, go to the goal, and avoid obstacles detected by the sonar sensors.

An approach to the decomposition of thruster forces of an autonomous underwater vehicle (AUV) is investigated. Generally, the number of thrusters is more than what is minimally required to produce the desired motion. We investigate how to exploit the excess number of thrusters to accommodate thruster faults during operation. First, a redundancy resolution scheme is presented that takes into account the presence of excess number of thrusters and distributes the thruster forces to produce the desired motion. Then we propose a general framework that accommodates thruster faults and allows the AUV to trade task-space trajectories with asymptotic reduction of task-space errors. Results from computer simulations are provided to demonstrate the viability of the proposed scheme.

A motion coordination algorithm for an autonomous underwater vehicle-manipulator system (UVMS) is proposed. This algorithm generates the desired trajectories for both the vehicle and the manipulator in such a way that the total drag effect on the system is minimized. Resolution of kinematic redundancy of the system is performed at the acceleration level so that this algorithm can be incorporated into the system dynamics. The dynamics of the UVMS is modeled using quasi-Lagrange approach. A state-space formulation of the system along with a model-based controller design for a trajectory following task is also presented. The results from computer simulations are used to demonstrate the effectiveness of this proposed method in reducing the drag on the system.

An explicit force control scheme for underwater vehicle-manipulator systems is presented. Several major problems of underwater robotics are taken into account; namely, uncertainty in the model knowledge, presence of hydrodynamic effects, kinematic redundancy of the system, and poor performance of the vehicle's actuating system. The possible occurrence of loss of contact due to vehicle movement during the task is also discussed. Extensive dynamic simulations prove the effectiveness of the proposed control algorithm.

An impedance control scheme for an autonomous underwater robotic system is proposed. This robotic system is composed of a six degree-of-freedom autonomous underwater vehicle (AUV) and a robotic arm that is mounted on the AUV. First, a dynamic model for the whole underwater vehicle-manipulator system (UVMS) considering various hydrodynamic effects is derived using a quasi-Langrange method. This model is later used to implement the proposed impedance controller. The impedance controller is designed considering the whole UVMS as one dynamic system. Extensive computer simulations are performed to verify the efficacy of the proposed control scheme and the results are presented in the paper.

The control of underwater vehicle-manipulator system (UVMS) is a challenging task because the dynamics of such a system is highly nonlinear, coupled, time-varying, and subject to hydrodynamic uncertainties and external disturbances. The paper presents a non-regressor based adaptive control for UVMS where centralized and coordinated trajectory planning is followed by decentralized control-one for the slower subsystem (vehicle) and other for the faster subsystem (manipulator). To avoid joint limits, kinematic redundancy of the system is resolved using the weighted pseudoinverse of the Jacobian matrix. The results from computer simulations demonstrate the effectiveness of this proposed method.

Introduces graph-rewriting methodology for robotic task planning. An approach to represent a high-level task plan in the form of a graph and modify it to accommodate various planning strategies is proposed. Explicitly delineated graph-rewrite rules and their ordered applications are used to reflect the change in plans by changing the graph topology. A framework for modeling complex manipulation tasks as interconnection of simpler subtasks and events connecting them is presented. Using a simple example, it is demonstrated how various plans can dynamically evolve as a function of events.

A dynamic traction control scheme for mobile robots/vehicles with two steerable and drivable wheels is presented. The kinematic modeling and position/orientation control of such a mobile robot/vehicle was studied previously by the authors (1996). In this paper, a dynamic feedback control algorithm is developed based on the dynamics of the vehicle, which not only enables the vehicle to move from any initial configuration (position and orientation) to any final configuration, but also strives to optimally distribute the tractive efforts between the two wheels for better performance. It is also shown that the same cannot be achieved by any static feedback controller. Such a control algorithm will be useful where surface conditions vary, and tractive torques between the wheels need to be dynamically allocated to maintain the required dynamic performance.

Many manipulation tasks involve transition from unconstrained to constrained motion marked by the contact with a constrained surface. This phenomenon divides the task into more than one phase, each of which requires a different control strategy. Switching from one control strategy to another leads to control discontinuities. The paper seeks to design a controller that avoids such discontinuities. The principle is based on the analysis of impulsive constraints. It is argued that, in theory, such a discontinuity can be avoided by modeling the cause of the force discontinuity of the end-effector as the velocity discontinuity of the constraint surface as opposed to the discontinuity of the Lagrange multiplier. This velocity discontinuity can then be dealt with by a continuous control strategy. A controller is designed based on this principle. Input-output linearization is performed to linearize and decouple the system. Simulation and experimental results are presented to demonstrate the effectiveness of this new approach.

Many robotic systems are subject to nonholonomic as well as holonomic constraints. Rolling contact between two rigid bodies is a typical example of such a system. In the study, a unified state space formulation of robotic systems subject to both holonomic and nonholonomic constraints is presented. The position-level holonomic constraints are first replaced by a set of velocity-level constraint equations that asymptotically converge to the original holonomic constraints. Having represented both holonomic and nonholonomic constraints in a common form, a state space representation of the constrained systems is then developed. A numerical algorithm for implementing the state space representation is also described. The proposed formulation eliminates the need to solve holonomic constraints either analytically or numerically, and ensures that holonomic constraints are always satisfied, particularly in computer simulations. The formulation makes it possible to treat systems with holonomic constraints, with nonholonomic constraints, or with both holonomic and nonholonomic constraints in a unified framework. Two examples are presented to illustrate the application of the unified formulation.

When two or more arms are used to manipulate a large object, it is preferable not to have a rigid grasp in order to gain more dexterity in manipulation. It may therefore be necessary to control contact motion between the object and the effector(s) on one or more arms. This paper addresses the dynamic control of two arms cooperatively manipulating a large object with rolling contacts. In the framework presented here, the motion of the object as well as the loci of the contact point either on the surface of each effector or on the object can be directly controlled. The velocity and acceleration equations for three-dimensional rolling contacts are derived in order to obtain a dynamic model of the system. A nonlinear feedback control algorithm that decouples and linearizes the system is developed. This is used to demonstrate the control of rolling motion along each arm and the adaptation of grasps to varying loads.

Many manipulation tasks involve transition from unconstrained to constrained motion marked by the contact with a constrained surface. This phenomenon divides the task into more than one control strategies plagued with control discontinuity. This paper seeks to design a controller which avoids such discontinuity. The principle is based on the analysis of impulsive constraints. It is shown that, in theory, such discontinuity can be avoided by replacing the force discontinuity of the end-effector by velocity discontinuity of the constraint surface. This velocity discontinuity can then be dealt with using a continuous control strategy. A controller has been designed based on this principle. Input-output linearization has been performed to linearize and decouple the system.

The kinematic modeling and feedback control of a mobile robot/vehicle with two independently steerable wheels is studied. Two-wheel steer vehicles are highly maneuverable in confined space and their orientation can be made independent of the position trajectory. However, the control of such vehicle is less intuitive and difficult. A dynamic feedback control algorithm is developed, which enables the vehicle to move from any initial configuration (position and orientation) to any final configuration. It is also shown that the same cannot be achieved by any static feedback controller. Simulation results are presented to verify the independent control of the two position variables and the orientation variable.

In a decentralized multi-robot system, each robot is controlled by an independent controller and the information obtained by each robot through its proprioceptive sensing devices is not shared with other robots. If the coordination of multiple robots in a cooperative manipulation task is to be accomplished, each robot must be able to exhibit controlled interaction with other robots and objects with minimal information. In this paper, the authors address the problem of maintaining rolling contact between a robot arm and an external, moving object. The authors assume that a nominal model of the motion of the moving object is available. The dynamic system that characterizes the motion of the robot arm and the moving object is acatastatic and nonholonomic. The authors design a nonlinear feedback for such a system that successfully maintains rolling contact. Simulation results are presented to demonstrate the effectiveness of the system.>

In a versatile, intelligent, multirobot system, each robot is controlled by independent controllers and information obtained by a robot through proprioceptive sensing (joint position, velocity, torques etc.) is not shared with other robots. If the coordination of robots in a cooperative manipulation task is to be accomplished, each robot must be able to exhibit controlled interaction with other robots and objects with minimal information. The authors address the problem of maintaining rolling contact between am actively controlled mechanical linkage and an external moving surface, assuming that a nominal model of the gross motion of the moving surface is available. Thus the control problem involves a dynamic system that is acatastic and nonholonomic. Nonlinear feedback is used to decouple and linearize the time-varying system. Examples and results from computer simulations are used to show that the normal and tangential forces as well as the relative rolling motion can be controlled satisfactorily.

The authors present a unified formulation for the control problem of multiple arm systems which accommodates both holonomic and nonholonomic constraints. Several unique control properties of nonholonomic systems are discussed. Several useful results regarding input-output linearization and the zero dynamics in such systems are proved. The analysis and controller design are discussed for multi-arm systems. Results from computer simulations are presented to demonstrate the control algorithms. Simulation results illustrate that rolling and sliding can be effectively controlled.>

An object much larger than a robot end-effector can not be rigidly grasped. Instead, it is necessary to support the object with multiple effectors. The effectors need not be grippers-they may be surfaces on the arm(s). Multiple arm manipulation without rigid grasps is investigated. The interaction between a robot and the object is characterized by unilateral constraints. The closed chain topology, redundancy in actuation, and the nonlinear coupled equations of motion make the problem formidable. The approach used is to utilize a minimal set of inputs to control the trajectory. The surplus inputs are used to control the contact conditions (rolling, sliding, etc.).>