Frontiers in Robotics and AI
EISSN : 2296-9144
Published by: Frontiers Media SA (10.3389)
Total articles ≅ 969
Latest articles in this journal
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.700465
Robots are an opportunity for interactive and engaging learning activities. In this paper we consider the premise that haptic force feedback delivered through a held robot can enrich learning of science-related concepts by building physical intuition as learners design experiments and physically explore them to solve problems they have posed. Further, we conjecture that combining this rich feedback with pen-and-paper interactions, e.g., to sketch experiments they want to try, could lead to fluid interactions and benefit focus. However, a number of technical barriers interfere with testing this approach, and making it accessible to learners and their teachers. In this paper, we propose a framework for Physically Assisted Learning based on stages of experiential learning which can guide designers in developing and evaluating effective technology, and which directs focus on how haptic feedback could assist with design and explore learning stages. To this end, we demonstrated a possible technical pathway to support the full experience of designing an experiment by drawing a physical system on paper, then interacting with it physically after the system recognizes the sketch, interprets as a model and renders it haptically. Our proposed framework is rooted in theoretical needs and current advances for experiential learning, pen-paper interaction and haptic technology. We further explain how to instantiate the PAL framework using available technologies and discuss a path forward to a larger vision of physically assisted learning.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.723780
Lower-limb exoskeletons are a promising option to increase the mobility of persons with leg impairments in a near future. However, it is still challenging for them to ensure the necessary stability and agility to face obstacles, particularly the variety that makes the urban environment. That is why most of the lower-limb exoskeletons must be used with crutches: the stability and agility features are deferred to the patient. Clinical experience shows that the use of crutches not only leads to shoulder pain and exhaustion, but also fully occupies the hands for daily tasks. In November 2020, Wandercraft presented Atalante Evolution, the first self-stabilized and crutch-less exoskeleton, to the powered exoskeleton race of the Cybathlon 2020 Global Edition. The Cybathlon aims at promoting research and development in the field of powered assistive technology to the public, contrary to the Paralympics where only participants with unpowered assistive technology are allowed. The race is designed to represent the challenges that a person could face every day in their environment: climbing stairs, walking through rough terrain, or descending ramps. Atalante Evolution is a 12 degree-of-freedom exoskeleton capable of moving dynamically with a complete paraplegic person. The challenge of this competition is to generate and execute new dynamic motions in a short time, to achieve different tasks. In this paper, an overview of Atalante Evolution system and of our framework for dynamic trajectory generation based on the direct collocation method will be presented. Next, the flexibility and efficiency of the dynamic motion generation framework are demonstrated by our tools developed for generating the important variety of stable motions required by the competition. A smartphone application has been developed to allow the pilot to choose between different modes and to control the motion direction according to the real situation to reach a destination. The advanced mechatronic design and the active cooperation of the pilot with the device will also be highlighted. As a result, Atalante Evolution allowed the pilot to complete four out of six obstacles, without crutches. Our developments lead to stable dynamic movements of the exoskeleton, hands-free walking, more natural stand-up and turning moves, and consequently a better physical condition of the pilot after the race compared to the challengers. The versatility and good results of these developments give hope that exoskeletons will soon be able to evolve in challenging everyday-life environments, allowing patients to live a normal life in complete autonomy.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.730330
Soft continuum robots have been accepted as a promising category of biomedical robots, accredited to the robots’ inherent compliance that makes them safely interact with their surroundings. In its application of minimally invasive surgery, such a continuum concept shares the same view of robotization for conventional endoscopy/laparoscopy. Different from rigid-link robots with accurate analytical kinematics/dynamics, soft robots encounter modeling uncertainties due to intrinsic and extrinsic factors, which would deteriorate the model-based control performances. However, the trade-off between flexibility and controllability of soft manipulators may not be readily optimized but would be demanded for specific kinds of modeling approaches. To this end, data-driven modeling strategies making use of machine learning algorithms would be an encouraging way out for the control of soft continuum robots. In this article, we attempt to overview the current state of kinematic/dynamic model-free control schemes for continuum manipulators, particularly by learning-based means, and discuss their similarities and differences. Perspectives and trends in the development of new control methods are also investigated through the review of existing limitations and challenges.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.715849
Social Robots are coming. They are being designed to enter our lives and help in everything from childrearing to elderly care, from household chores to personal therapy, and the list goes on. There is great promise that these machines will further the progress that their predecessors achieved, enhancing our lives and alleviating us of the many tasks with which we would rather not be occupied. But there is a dilemma. On the one hand, these machines are just that, machines. Accordingly, some thinkers propose that we maintain this perspective and relate to Social Robots as “tools”. Yet, in treating them as such, it is argued, we deny our own natural empathy, ultimately inculcating vicious as opposed to virtuous dispositions. Many thinkers thus apply Kant’s approach to animals—“he who is cruel to animals becomes hard also in his dealings with men”—contending that we must not maltreat robots lest we maltreat humans. On the other hand, because we innately anthropomorphize entities that behave with autonomy and mobility (let alone entities that exhibit beliefs, desires and intentions), we become emotionally entangled with them. Some thinkers actually encourage such relationships. But there are problems here also. For starters, many maintain that it is imprudent to have “empty,” unidirectional relationships for we will then fail to appreciate authentic reciprocal relationships. Furthermore, such relationships can lead to our being manipulated, to our shunning of real human interactions as “messy,” to our incorrectly allocating resources away from humans, and more. In this article, I review the various positions on this issue and propose an approach that I believe sits in the middle ground between the one extreme of treating Social Robots as mere machines versus the other extreme of accepting Social Robots as having human-like status. I call the approach “The Virtuous Servant Owner” and base it on the virtue ethics of the medieval Jewish philosopher Maimonides.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.724798
Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.735566
Minimally invasive robotic surgery copes with some disadvantages for the surgeon of minimally invasive surgery while preserving the advantages for the patient. Most commercially available robotic systems are telemanipulated with haptic input devices. The exploitation of the haptics channel, e.g., by means of Virtual Fixtures, would allow for an individualized enhancement of surgical performance with contextual assistance. However, it remains an open field of research as it is non-trivial to estimate the task context itself during a surgery. In contrast, surgical training allows to abstract away from a real operation and thus makes it possible to model the task accurately. The presented approach exploits this fact to parameterize Virtual Fixtures during surgical training, proposing a Shared Control Parametrization Engine that retrieves procedural context information from a Digital Twin. This approach accelerates a proficient use of the robotic system for novice surgeons by augmenting the surgeon’s performance through haptic assistance. With this our aim is to reduce the required skill level and cognitive load of a surgeon performing minimally invasive robotic surgery. A pilot study is performed on the DLR MiroSurge system to evaluate the presented approach. The participants are tasked with two benchmark scenarios of surgical training. The execution of the benchmark scenarios requires basic skills as pick, place and path following. The evaluation of the pilot study shows the promising trend that novel users profit from the haptic augmentation during training of certain tasks.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.692180
Supervising and controlling remote robot systems currently requires many specialised operators to have knowledge of the internal state of the system in addition to the environment. For applications such as remote maintenance of future nuclear fusion reactors, the number of robots (and hence supervisors) required to maintain or decommission a facility is too large to be financially feasible. To address this issue, this work explores the idea of intelligently filtering information so that a single user can supervise multiple robots safely. We gathered feedback from participants using five methods for teleoperating a semi-autonomous multi-robot system via Virtual Reality (VR). We present a novel 3D interaction method to filter the displayed information to allow the user to read information from the environment without being overwhelmed. The novelty of the interface design is the link between Semantic and Spatial filtering and the hierarchical information contained within the multi robot system. We conducted a user study including a cohort of expert robot teleoperators comparing these methods; highlighting the significant effects of 3D interface design on the performance and perceived workload of a user teleoperating many robot agents in complex environments. The results from this experiment and subjective user feedback will inform future investigations that build upon this initial work.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.737500
Developing highly sensitive flexible pressure sensors has become crucially urgent due to the increased societal demand for wearable electronic devices capable of monitoring various human motions. The sensitivity of such sensors has been shown to be significantly enhanced by increasing the relative dielectric permittivity of the dielectric layers used in device construction via compositing with immiscible ionic conductors. Unfortunately, however, the elastomers employed for this purpose possess inhomogeneous morphologies, and thus suffer from poor long-term durability and unstable electrical response. In this study, we developed a novel, flexible, and highly sensitive pressure sensor using an elastomeric dielectric layer with particularly high permittivity and homogeneity due to the addition of synthesized ionic liquid-grafted silicone oil (denoted LMS-EIL). LMS-EIL possesses both a very high relative dielectric permittivity (9.6 × 105 at 10−1 Hz) and excellent compatibility with silicone elastomers due to the covalently connected structure of conductive ionic liquid (IL) and chloropropyl silicone oil. A silicone elastomer with a relative permittivity of 22 at 10−1 Hz, Young’s modulus of 0.78 MPa, and excellent homogeneity was prepared by incorporating 10 phr (parts per hundreds rubber) of LMS-EIL into an elastomer matrix. The sensitivity of the pressure sensor produced using this optimized silicone elastomer was 0.51 kPa−1, which is 100 times higher than that of the pristine elastomer. In addition, a high durability illustrated by 100 loading–unloading cycles and a rapid response and recovery time of approximately 60 ms were achieved. The excellent performance of this novel pressure sensor suggests significant potential for use in human interfaces, soft robotics, and electronic skin applications.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.739023
This paper presents a framework to alleviate the Deep Reinforcement Learning (DRL) training data sparsity problem that is present in challenging domains by creating a DRL agent training and vehicle integration methodology. The methodology leverages accessible domains to train an agent to solve navigational problems such as obstacle avoidance and allows the agent to generalize to challenging and inaccessible domains such as those present in marine environments with minimal further training. This is done by integrating a DRL agent at a high level of vehicle control and leveraging existing path planning and proven low-level control methodologies that are utilized in multiple domains. An autonomy package with a tertiary multilevel controller is developed to enable the DRL agent to interface at the prescribed high control level and thus be separated from vehicle dynamics and environmental constraints. An example Deep Q Network (DQN) employing this methodology for obstacle avoidance is trained in a simulated ground environment, and then its ability to generalize across domains is experimentally validated. Experimental validation utilized a simulated water surface environment and real-world deployment of ground and water robotic platforms. This methodology, when used, shows that it is possible to leverage accessible and data rich domains, such as ground, to effectively develop marine DRL agents for use on Autonomous Surface Vehicle (ASV) navigation. This will allow rapid and iterative agent development without the risk of ASV loss, the cost and logistic overhead of marine deployment, and allow landlocked institutions to develop agents for marine applications.
Frontiers in Robotics and AI, Volume 8; https://doi.org/10.3389/frobt.2021.641338
Accumulating space debris edges the space domain ever closer to cascading Kessler syndrome, a chain reaction of debris generation that could dramatically inhibit the practical use of space. Meanwhile, a growing number of retired satellites, particularly in higher orbits like geostationary orbit, remain nearly functional except for minor but critical malfunctions or fuel depletion. Servicing these ailing satellites and cleaning up “high-value” space debris remains a formidable challenge, but active interception of these targets with autonomous repair and deorbit spacecraft is inching closer toward reality as shown through a variety of rendezvous demonstration missions. However, some practical challenges are still unsolved and undemonstrated. Devoid of station-keeping ability, space debris and fuel-depleted satellites often enter uncontrolled tumbles on-orbit. In order to perform on-orbit servicing or active debris removal, docking spacecraft (the “Chaser”) must account for the tumbling motion of these targets (the “Target”), which is oftentimes not known a priori. Accounting for the tumbling dynamics of the Target, the Chaser spacecraft must have an algorithmic approach to identifying the state of the Target’s tumble, then use this information to produce useful motion planning and control. Furthermore, careful consideration of the inherent uncertainty of any maneuvers must be accounted for in order to provide guarantees on system performance. This study proposes the complete pipeline of rendezvous with such a Target, starting from a standoff estimation point to a mating point fixed in the rotating Target’s body frame. A novel visual estimation algorithm is applied using a 3D time-of-flight camera to perform remote standoff estimation of the Target’s rotational state and its principal axes of rotation. A novel motion planning algorithm is employed, making use of offline simulation of potential Target tumble types to produce a look-up table that is parsed on-orbit using the estimation data. This nonlinear programming-based algorithm accounts for known Target geometry and important practical constraints such as field of view requirements, producing a motion plan in the Target’s rotating body frame. Meanwhile, an uncertainty characterization method is demonstrated which propagates uncertainty in the Target’s tumble uncertainty to provide disturbance bounds on the motion plan’s reference trajectory in the inertial frame. Finally, this uncertainty bound is provided to a robust tube model predictive controller, which provides tube-based robustness guarantees on the system’s ability to follow the reference trajectory translationally. The combination and interfaces of these methods are shown, and some of the practical implications of their use on a planned demonstration on NASA’s Astrobee free-flyer are additionally discussed. Simulation results of each of the components individually and in a complete case study example of the full pipeline are presented as the study prepares to move toward demonstration on the International Space Station.