Feedback Guidance Research Articles

Multi-constrained feedback guidance for mars pinpoint soft landing using time-varying sliding mode

Taking into account the thruster performance (total thrust magnitude constraint and control acceleration deviation) and glide-slope constraint, this paper presents two types of new feedback guidance strategies for Mars pinpoint soft landing, which are near-fuel-optimal and robust to disturbance. A novel and simple time-varying feedback guidance law is proposed at first. Next, this guidance law is extended to the incremental guidance to improve the robustness and ability to deal with the thruster performance and disturbance. An advantage of these two guidance schemes is that they can avoid subsurface flight owing to the monotonic convergence of position and velocity. Moreover, an exciting feature of these two guidance laws is that the trajectory of the vehicle can be set as a straight line by choosing the guidance laws parameters. Finally, an optimal problem is constructed and solved based on this feature to obtain the guidance laws parameters to satisfy the glide-slope constraint. Its robustness, fuel efficiency, and performance in dealing with acceleration deviation and thruster constraint are demonstrated for different simulation scenarios, especially in comparison with zero-effort-miss/zero-effort-velocity feedback guidance law.

Discrete-Variable-Thrust Guidance for Orbital Rendezvous Based on Feedback Linearization

This research is focused on the analysis, design, and numerical testing of a feedback guidance algorithm for autonomous (unmanned) close-range maneuvering of a chaser spacecraft, in the context of orbital rendezvous with a target vehicle. The relative dynamics of the two vehicles, placed in nearby low Earth orbits, is modeled using the nonlinear Battin–Giorgi equations of relative motion, with the inclusion of all the relevant perturbations, i.e. several harmonics of the geopotential, atmospheric drag, solar radiation pressure, and third body gravitational pull due to Moon and Sun. Unlike several former contributions in the scientific literature, this research considers the orbit perturbing actions on both vehicles, proving that this is crucial for a successful maneuver. Feedback linearization provides the theoretical foundation for the definition and development of a guidance algorithm that is capable of driving the chaser vehicle toward the target spacecraft. Moreover, discrete-variable thrust is considered, and an effective modulation scheme is proposed that includes adaptation of the control gains. Monte Carlo simulations demonstrate that the guidance technique at hand is effective and accurate in driving the chaser spacecraft toward the target vehicle, in the presence of orbit perturbations and unpredictable displacements from the nominal initial conditions.

Online Feedback Systems and Debate on Scientific Issues during COVID-19: A Case Study on Sookmyung Women’s University

As the COVID-19 pandemic continues, university education and feedback guidance have inevitably moved to online platforms, becoming a global trend. This study focuses on a case of Sookmyung Women’s University in South Korea, which has operated an online discussion clinic for university general education for more than a year as a case study. There are two main research methods. A frequency analysis was conducted to confirm what kind of counseling the students preferred at the discussion clinic based on the answers written in the students’ applications. The students whose applications were used for the analysis were divided into 57 teams, and there were two to six members per team. The results were as follows: In the survey results, students wanted help with the preparation process necessary for the discussion and the practical strategies for facilitating discussions. They wanted personalized counseling, demonstrating that discussion education provided in the foundational curriculum is insufficient. Second, the educational model of the discussion clinic and educational examples were examined. The findings confirmed that online discussion education is effective if the system is technically supplemented. Instructors and researchers are prepared to meet students’ demands for feedback and individual counseling, even if these are not provided through face-to-face discussions. Additionally, face-to-face guidance can be operated more effectively by taking advantage of online systems. The findings also demonstrate that further research on designing and operating online discussion centers is required. This study is a preceding study on developing online systems and educational guidelines for higher educational institutions to present new insights into smart learning. This paper also includes suggestions for educational and scientific discussions. The online discussion instructional model shown in this paper explores methods of scientific communication through a debate on scientific issues.

Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation

In procedural-based medicine, the technical ability can be a critical determinant of patient outcomes. Psychomotor performance occurs in real-time, hence a continuous assessment is necessary to provide action-oriented feedback and error avoidance guidance. We outline a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), to assess surgical bimanual performance at 0.2-s intervals. A long-short term memory network was built using neurosurgeon and student performance in 156 virtually simulated tumor resection tasks. Algorithm predictive ability was tested separately on 144 procedures by scoring the performance of neurosurgical trainees who are at different training stages. The ICEMS successfully differentiated between neurosurgeons, senior trainees, junior trainees, and students. Trainee average performance score correlated with the year of training in neurosurgery. Furthermore, coaching and risk assessment for critical metrics were demonstrated. This work presents a comprehensive technical skill monitoring system with predictive validation throughout surgical residency training, with the ability to detect errors.

Prescribed Performance-Based Powered Descent Guidance for Step-Shaped Hazardous Terrains

The planetary powered descent guidance problem for step-shaped hazardous terrains is investigated in this article based on prescribed performance function (PPF) methodology. Initially, the distances between the lander and step-shaped terrains around the landing site have been formulated in a new form boundaryfunction using PPF, in which a new step-shaped boundary PPF is specifically designed to constrain the lateral motion. Furthermore, a fixed-time convergent PPF is chosen to coordinate the vertical motion. Next, to avoid the collision with step-shaped terrains and planetary surface, a feedback guidance algorithm is proposed based on the backstepping method. Considering a large guidance gain is beneficial for the lander to move away from the boundary PPF, but excessive control acceleration will be generated when the landing error is large. To solve this problem, an adaptive guidance gain is designed using Gaussian function. Finally, the feasibility and effectiveness of the proposed algorithm have been verified through typical numerical simulations inspired by realistic Martian terrains. Moreover, this attempt using PPF methodology here can be easily reformulated to adjust the powered descent problem with collision avoidance for a flat surface or glide-slope constraint.

Cooperative trajectory optimization of UAVs in approaching stage using feedback guidance methods

This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles. Based on the optimal trajectory trends and feedback guidance methods, a new cooperative control algorithm is proposed to optimize trajectories of multi-UAVs for target tracking in approaching stage. Based on UAV kinematics and sensor performance models, optimal trajectory trends of UAVs are analyzed theoretically. Then, feedback guidance methods are proposed under the optimal observation trends of UAVs in the approaching target stage, producing trajectories with far less computational complexity and performance very close to the best-known trajectories. Next, the sufficient condition for the UAV to form the optimal observation configuration by the feedback guidance method is presented, which guarantees that the proposed method can optimize the observation trajectory of the UAV in approaching stage. Finally, the feedback guidance method is numerically simulated. Simulation results demonstrate that the estimation performance of the feedback guidance method is superior to the Lyapunov guidance vector field (LGVF) method and verify the effectiveness of the proposed method. Additionally, compared with the receding horizon optimization (RHO) method, the proposed method has the same optimization ability as the RHO method and better real-time performance.

The effect of learning analytics assisted recommendations and guidance feedback on students’ metacognitive awareness and academic achievements

The effect of learning analytics assisted recommendations and guidance feedback on students’ metacognitive awareness and academic achievements

Haptic Devices: Wearability-Based Taxonomy and Literature Review

In the last decade, several new haptic devices have been developed, contributing to the definition of more realistic virtual environments. An overview on this topic requires a description of the various technologies employed in building such devices, and of their application domains. This survey describes the current technology underlying haptic devices, based on the concept of “wearability level”. More than 90 devices, newly developed and described in scientific papers published in the period 2010-2021, are reviewed, which provide either haptic illusions or novel haptic feedback for teleoperation, entertainment, training, education, guidance and notification. As a result, the analyzed systems are divided into grounded, hand-held and wearable devices; the latter are further split into exoskeletons and gloves, finger-worn devices, and arm-worn devices. For the systems in each of these categories, descriptions and tables are provided that analyze their structure, including device mass and employed actuators, their applications, and other characteristics such as type of haptic feedback and tactile illusions. The paper also provides an overview of devices worn in parts of the human body other than arms and hands, and precisely haptic vests, jackets and belts, and haptic devices for head, legs and feet. Based on this analysis, the survey also provides a discussion on research gaps and challenges, and potential future directions.

Real-time posture feedback for effective motor learning in table tennis in virtual reality

Table tennis is a demanding sport in terms of motor skills, requiring precise time-critical postures and techniques for the player to excel. Therefore, it is a compelling case study for motor learning in Virtual Reality (VR). Though VR table tennis training applications have been used to study the learning process in VR, each has not fully explored the role of real-time feedback on motor learning, which is a fundamental component of real-life table tennis training. To address this, we have developed a training system that provides multi-modal training instructions and real-time posture guidance feedback to assist beginner players in improving their playing outcomes. This system synthesizes a table tennis training environment by providing realistic visual and audio stimuli that includes a custom highly-accurate physics implementation of ball flight. An experimental group (n = 9) trained to learn correct posture and paddle handling for forehand and backhand drives. The results show that the participants improved significantly in terms of technique and ball return quality. This work is the first VR table tennis system that provides real-time posture feedback using a low-cost depth camera. Additionally, the participants’ significant improvement in playing posture over a short period of time shows the integral role of feedback in learning and further validates VR as an effective motor learning tool.

Usability and applicability of research knowledge translation models in a national health service

Abstract Background The Health Service Executive in Ireland aims to facilitate the translation of research and innovation into policy and practice and increase dissemination of research. Previous HSE surveys showed that a high volume of research was undertaken but the impact on practice and policy was unclear. Researchers reported sharing their research in publications, conferences, and through education and training but identified problems in engaging with practice and policy stakeholders. Survey respondents requested help with dissemination and getting research into practice. An organisation-wide project to identify frameworks to support knowledge translation (KT), dissemination, and impact identified a recommended methodology, created guidance, and training for knowledge creators and users. Objectives Provide a series of six guides, tools and templates, to support knowledge creators and users across the organisation Provide online training to support translation and dissemination of research knowledge Underline the importance of planning KT and impact at research commencement and identify the planned outcomes Develop user-friendly training to explain the elements of KT and dissemination. Results Two pilot studies were undertaken to test the guidance which, although positively received, resulted in changes to the format, design, and language to increase useability. An explainer video and six online training modules were developed, based on the guidance and pilot feedback to be rolled out from July 2021. Learning was enhanced through transfer of complex information and models into short training modules to be accessed by researchers with a range of experience and understanding. Conclusions KT is a complex area and developing education and training for it requires understanding the different learning needs of knowledge creators and users. Education and training should focus on the need for KT and impact from the start of a project. Key messages Practical guidance to assist researchers in translation of their research findings into practice and improving dissemination enhances service user impact. Education and training should focus on the need to plan for KT from the start of a project.

New Look-Angle Tracking Guidance Strategy for Impact Time and Angle Control

This paper presents a new guidance strategy for a certain class of homing missiles subject to various terminal constraints and lateral acceleration limit. To achieve zero miss distance in the presence of various terminal impact constraints, a planar engagement scenario of missiles is considered to develop a feedback guidance law of tracking a time-varying look angle that is modeled as an inverse tangent function. The proposed strategy can be applied to both the impact time control and the impact time and angle control guidance problems. Furthermore, it does not require any forms of time-to-go estimation, numerical iterations, and linearization procedures. Numerical simulations are conducted to validate the effectiveness, robustness, and energy efficiency of the proposed guidance strategy in comparison with other existing guidance strategies.

Singular-Perturbation-Based Guidance of Pulse Motor Interceptors with Look Angle Constraints

This paper addresses the problem of midcourse guidance of dual-pulse rocket motor interceptors with seeker field-of-view constraints using singular perturbation technique. The guidance law considers a combination of terminal velocity and flight time as a performance measure. In addition to the seeker field-of-view limit, constraints in the optimization problem include minimum dynamic pressure limit arising due to aerodynamic controllability. Using the time scale separation between the state variables, the full-order problem is reduced into lower-order subproblems, and a closed-form solution for guidance law is derived. It is shown that the performance of the interceptor is not very sensitive to perturbation of pulse firing time, and an offline generated lookup table is used to time the second thrust pulse firing. The proposed guidance law is computationally efficient, and its performance is benchmarked with that of pseudospectral-based feedback guidance with much lower computational cost. Simulation studies with point mass and six-degree-of-freedom model are presented highlighting the accuracy and efficacy of the proposed guidance law.

Toward Accurate Pixelwise Object Tracking via Attention Retrieval.

Pixelwise single object tracking is challenging due to the competition of running speeds and segmentation accuracy. Current state-of-the-art real-time approaches seamlessly connect tracking and segmentation by sharing computation of the backbone network, e.g., SiamMask and D3S fork a light branch from the tracking model to predict segmentation mask. Although efficient, directly reusing features from tracking networks may harm the segmentation accuracy, since background clutter in the backbone feature tends to introduce false positives in segmentation. To mitigate this problem, we propose a unified tracking-retrieval-segmentation framework consisting of an attention retrieval network (ARN) and an iterative feedback network (IFN). Instead of segmenting the target inside the bounding box, the proposed framework performs soft spatial constraints on backbone features to obtain an accurate global segmentation map. Concretely, in ARN, a look-up-table (LUT) is first built by sufficiently using the information of the first frame. By retrieving it, a target-aware attention map is generated to suppress the negative influence of background clutter. To ulteriorly refine the contour of the segmentation, IFN iteratively enhances the features at different resolutions by taking the predicted mask as feedback guidance. Our framework sets a new state of the art on the recent pixelwise tracking benchmark VOT2020 and runs at 40 fps. Notably, the proposed model surpasses SiamMask by 11.7/4.2/5.5 points on VOT2020, DAVIS2016, and DAVIS2017, respectively. Code is available at https://github.com/JudasDie/SOTS.

Usability of cooperative surgical telemanipulation for bone milling tasks

PurposeCooperative surgical systems enable humans and machines to combine their individual strengths and collaborate to improve the surgical outcome. Cooperative telemanipulated systems offer the widest spectrum of cooperative functionalities, because motion scaling is possible. Haptic guidance can be used to assist surgeons and haptic feedback makes acting forces at the slave side transparent to the operator, however, overlapping and masking of forces needs to be avoided. This study evaluates the usability of a cooperative surgical telemanipulator in a laboratory setting.MethodsThree experiments were designed and conducted for characteristic surgical task scenarios derived from field studies in orthopedics and neurosurgery to address bone tissue differentiation, guided milling and depth sensitive milling. Interaction modes were designed to ensure that no overlapping or masking of haptic guidance and haptic feedback occurs when allocating information to the haptic channel. Twenty participants were recruited to compare teleoperated modes, direct manual execution and an exemplary automated milling with respect to usability.ResultsParticipants were able to differentiate compact and cancellous bone, both directly manually and teleoperatively. Both telemanipulated modes increased effectiveness measured by the mean absolute depth and contour error for guided and depth sensitive millings. Efficiency is decreased if solely a boundary constraint is used in hard material, while a trajectory guidance and manual milling perform similarly. With respect to subjective user satisfaction trajectory guidance is rated best for guided millings followed by boundary constraints and the direct manual interaction. Haptic feedback only improved subjective user satisfaction.ConclusionA cooperative surgical telemanipulator can improve effectiveness and efficiency close to an automated execution and enhance user satisfaction compared to direct manual interaction. At the same time, the surgeon remains part of the control loop and is able to adjust the surgical plan according to the intraoperative situation and his/her expertise at any time.

Two-Phase Zero-Effort-Miss/Zero-Effort-Velocity Guidance for Mars Landing

This paper presents a new two-phase zero-effort-miss/zero-effort-velocity (ZEM/ZEV) feedback guidance strategy for Mars precision landing, which directly accommodates a variety of constraints and requirements, such as retrorocket thrust magnitude limits, obstacles over the surface of Mars, and abnormal initial conditions. For its first phase, a virtual-terminal-velocity guidance law is augmented by an online parameter searching technique for collision avoidance. For its second phase, a signed curvature concept enhances the basic ZEM/ZEV guidance law for final soft touchdown. Its robustness, fuel efficiency, and computational simplicity are demonstrated for abnormal initial conditions, especially in comparison with three other feedback guidance laws recently published in the literature.

Autonomous image-based navigation using vector code correlation algorithm for distant small body exploration

This paper proposes an autonomous image-based navigation method for estimating the target-relative position of a spacecraft for distant small body exploration. The main focus is position estimation at high altitude where the outlines of a target body can be seen in images. The asteroid explorer Hayabusa2 touched down on the asteroid Ryugu with pin-point accuracy in February 2019. For this mission, the asteroid-relative position was estimated by ground operators from 20 km to 50 m above the surface of Ryugu. For the exploration of small bodies farther than the asteroid main belt, the delay of communication with Earth is unacceptably large for feedback guidance. This situation becomes worse for larger bodies because the time constant of the dynamics becomes smaller. Therefore, real-time autonomous navigation is required for distant small body exploration even at high altitude. To accomplish high-accuracy and real-time autonomous navigation, an autonomous position estimation method based on terrain-relative navigation (TRN) that estimates deviation by comparing nominal terrain information and actual terrain information is proposed. In addition to TRN, the vector code correlation (VCC) algorithm is used for the luminance comparison of terrain information. This algorithm is a type of correlation calculation method for template matching that finds the maximum correlated region in images. With the VCC algorithm, correlation can be calculated in real time via XOR operations suitable for FPGA. The estimation accuracy and processing time of the proposed method were evaluated with a comparison to those of other methods. The results show that a high estimation accuracy, similar to the image resolution, was accomplished in real time. Finally, an evaluation using flight data from Hayabusa2 shows that the estimation accuracy and processing time of the proposed method are suitable for a real mission environment. The proposed method will be a key technology for distant small body exploration.

Feedback-aided data acquisition improves myoelectric control of a prosthetic hand

Objective. Pattern-recognition-based myocontrol can be unreliable, which may limit its use in the clinical practice and everyday activities. One cause for this is the poor generalization of the underlying machine learning models to untrained conditions. Acquiring the training data and building the model more interactively can reduce this problem. For example, the user could be encouraged to target the model’s instabilities during the data acquisition supported by automatic feedback guidance. Interactivity is an emerging trend in myocontrol of upper-limb electric prostheses: the user should be actively involved throughout the training and usage of the device. Approach. In this study, 18 non-disabled participants tested two novel feedback-aided acquisition protocols against a standard one that did not provide any guidance. All the protocols acquired data dynamically in multiple arm positions to counteract the limb position effect. During feedback-aided acquisition, an acoustic signal urged the participant to hover with the arm in specific regions of her peri-personal space, de facto acquiring more data where needed. The three protocols were compared on everyday manipulation tasks performed with a prosthetic hand. Main results. Our results showed that feedback-aided data acquisition outperformed the acquisition routine without guidance, both objectively and subjectively. Significance. This indicates that the interaction with the user during the data acquisition is fundamental to improve myocontrol.

Runtime Assurance for Autonomous Aerospace Systems

This paper presents a runtime assurance (RTA) framework for an unmanned aerospace application. RTA systems hold the promise of protecting feedback systems that contain advanced or experimental elements that cannot be completely tested at design time to their required levels of assurance. This inability of fully trusting such advanced systems is due to their inherent complexity and that current analysis, verification, and validation protocols cannot address such complexity. The necessary structure of RTA frameworks for multilevel interacting feedback systems is investigated. A challenge problem is constructed for a fleet of unmanned aircraft systems performing complex missions. The complete study involves integrating RTA systems for the inner-loop control, outer-loop guidance, vehicle flight management, and runtime mission planning elements. The RTA interactions between these feedback levels are explored in this effort. General required RTA checks are presented and the critical reversionary switching conditions defined. More detailed system design is presented for the outer-loop guidance feedback level.

Student Opinions About Personalized Recommendation and Feedback Based on Learning Analytics

There is a growing interest in the use of learning analytics in higher education institutions. Learning analytics also appear to have the potential to be used to provide personalized feedback and support in online learning. However, when the literature is examined, the use of learning analytics for this purpose appears as a gap to be investigated. This research aims to examine the opinions of pre-service teachers about the personalized recommendation and guidance feedback based on learning analytics. The research was carried out on 40 pre-service teachers in the Computer I course, which was conducted according to the flipped learning model for 12 weeks. Throughout the research process, personalized feedback based on learning analytics was provided by researcher (the researcher is also the teacher of the Computer I course) to pre-service teachers at the end of each week. Accordingly, the students’ weekly learning management system (LMS) obtained learning analytics results from the log data related to their usage behavior. Then, the researcher prepared personalized recommendation and guidance messages based on learning analytics results. Learning analytics results and related recommendations and guidance messages were sent via LMS (from the messaging tool) as feedback. This process was done for each pre-service teacher by the researcher every week during the research process. The data of the research were obtained with a semi-structured opinion form and content analysis was made in the analysis of the data. As a result of the research, beneficial aspects and limitations of personalized recommendation and guidance feedback based on learning analytics from the perspective of pre-service teachers were revealed. In line with the results obtained from the research, various suggestions were made for the design and use of feedback messages based on learning analytics.

English speech sound improvement system based on deep learning from signal processing to semantic recognition

With the global integration and the increasing level of China's internationalization, the demand of Chinese people for English learning is growing rapidly. At the same time, English has gradually become one of the most frequently used languages in the world economic exchanges and cultural information exchanges. After the material life demands of the members of the domestic society are fully satisfied, the learning and explanation of the corresponding knowledge content of the English language are gradually highly valued by the staff in various production fields. After AI proposed deep learning models, researchers also began to improve these models and use them in speech recognition. Through multi-layer nonlinear structure, the essential features are preserved and more abstract advanced features are extracted, so the recognition rate is improved. Computer-aided language learning will change the existing language teaching mode and learning environment, so that learners can learn independently anytime and anywhere. Deep learning can give learners accurate, objective and timely pronunciation evaluation and feedback guidance. It can also help learners find out the differences between their pronunciation and standard pronunciation through repeated listening and comparison, correct their pronunciation errors, and improve the efficiency of language learning. This paper constructs an English language improvement system based on deep learning. The experimental results show that the proposed method can effectively analyze the input speech signals and make corresponding feedback.