Autonomous Research Articles

Expanding Ground Vehicle Autonomy into Unstructured, Off-Road Environments: Dataset Challenges

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University’s Center for Advanced Vehicular Systems.

Modeling biological memory network by an autonomous and adaptive multi-agent system

At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships description of complex relationships and structures, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a multi-agent system (MAS) model based on the graph theory, each agent equipped with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information memory, modeling and simulation of the brain’s memory process. This decentralized approach transforms memory storage into the management of MAS paths, with each agent utilizing localized information for the dynamic formation and modification of these paths, different path refers to different memory instance. The model’s unique memory algorithm avoids a global view, instead relying on neighborhood-based interactions to enhance resource utilization. Emulating neuron electrophysiology, each agent’s adaptive learning behavior is represented through a microcircuit centered around a variable resistor. Using principles of Ohm’s and Kirchhoff’s laws, we validated the model’s efficacy in memorizing and retrieving data through computer simulations. This approach offers a plausible neurobiological explanation for memory realization and validates the memory trace theory at a system level.

Integrating Eye Movement, Finger Pressure, and Foot Pressure Information to Build an Intelligent Driving Fatigue Detection System

Fatigued driving is a problem that every driver will face, and traffic accidents caused by drowsy driving often occur involuntarily. If there is a fatigue detection and warning system, it is generally believed that the occurrence of some incidents can be reduced. However, everyone’s driving habits and methods may differ, so it is not easy to establish a suitable general detection system. If a customized intelligent fatigue detection system can be established, it may reduce unfortunate accidents. With its potential to mitigate unfortunate accidents, this study offers hope for a safer driving environment. Thus, on the one hand, this research hopes to integrate the information obtained from three different sensing devices (eye movement, finger pressure, and plantar pressure), which are chosen for their ability to provide comprehensive and reliable data on a driver’s physical and mental state. On the other hand, it uses an autonomous learning architecture to integrate these three data types to build a customized fatigued driving detection system. This study used a system that simulated a car driving environment and then invited subjects to conduct tests on fixed driving routes. First, we demonstrated that the system established in this study could be used to learn and classify different driving clips. Then, we showed that it was possible to judge whether the driver was fatigued through a series of driving behaviors, such as lane drifting, sudden braking, and irregular acceleration, rather than a single momentary behavior. Finally, we tested the hypothesized situation in which drivers were experiencing three cases of different distractions. The results show that the entire system can establish a personal driving system through autonomous learning behavior and further detect whether fatigued driving abnormalities occur.

Fostering resilience and post-traumatic growth in overseas Chinese left-behind children: The role of autonomy, competence, and relatedness.

This study examines the impact of parental migration on the psychological well-being and development of left-behind children (LBCs) in Zhejiang, China, within the broader context of the country's rural transformations and urban migration. It investigates how intellectual and relational engagement (RE), autonomy (AUT), competence (COM), and relatedness (RES) contribute to resilience (REL) and post-traumatic growth (PTG) in these children, reflecting on the shift from viewing parental separation merely as a source of trauma to recognizing its potential to foster significant personal growth. Utilizing a cross-sectional design, the research was conducted in April and May 2023 with 1348 LBCs from a total sample of 4049 students inZhejiang. A two-step random, stratified, cluster-based sampling strategy was employed, and structural equation modeling was used to examine the hypothesized relationships among the constructs. The statistical analysis demonstrated significant positive effects of intellectual engagement (IE), AUT, COM, and RE on both REL and PTG (p<.05 for all). IE strongly correlated with AUT (r=.68, p<.001) and COM (r=.71, p<.001), enhancing REL and facilitating PTG. Additionally, the presence of secure and consistent relationships was identified as crucial for maintaining psychological well-being, with high correlation coefficients (r>.60) underscoring their importance. Notably, REL was found to moderate the relationships among RES, COM, and PTG, highlighting its critical role in the psychological adaptation of left-behind children. The study underscores the importance of nurturing intellectual and REs, AUT, and COM to enhance psychological REL and well-being among LBAs. These elements are crucial for supporting the mental health and developmental needs of children facing the challenges of parental migration. The findings advocate for targeted interventions that can address the unique needs of this vulnerable population, emphasizing the potential for growth and adaptation despite adversities.

The Role of Web Technologies in Promoting Learner Autonomy among EFL University Students

Technological advancements have brought about several environmental, economic and social challenges for the entire world. Nevertheless, no one can deny that these forces have offered infinite opportunities for progress and development for humanity. The digital revolution has dramatically affected many domains such as work and education. Perhaps one of the main aspects of this revolution in education is online learning which allows students to easily access resources and self-direct their learning. The current paper attempts to examine the role of using Web technologies in fostering learners’ autonomy. It also aims to explore students’ experiences with online learning and how they perceive its relevance to the enhancement of autonomous abilities. To attain these objectives, a mixed-method approach is adopted to collect data utilizing questionnaires and semi-structured interviews. The participants are Moroccan university students in the English departments, and data is analyzed using descriptive statistics and thematic analysis. The results show that the participants display high levels of autonomous behaviours as students value their experiences with online learning. The findings also indicate that most students are willing to be more responsible for their own learning when using online platforms that offer, flexibility, interaction, and interesting autonomous practices of language learning.

Artificial Punishment Signals for Guiding the Decision-Making Process of an Autonomous System

Somatic markers have been evidenced as determinant factors in human behavior. In particular, the concepts of somatic reward and punishment have been related to the decision-making process; both reward and somatic punishment represent bodily states with positive or negative sensations, respectively. In this research work, we have designed a mechanism to generate artificial somatic punishments in an autonomous system. An autonomous system is understood as a system capable of performing autonomous behavior and decision making. We incorporated this mechanism within a decision model oriented to support decision making on stock markets. Our model focuses on using artificial somatic punishments as a tool to guide the decisions of an autonomous system. To validate our proposal, we defined an experimental scenario using official data from Standard &amp; Poor’s 500 and the Dow Jones index, in which we evaluated the decisions made by the autonomous system based on artificial somatic punishments in a general investment process using 10,000 independent iterations. In the investment process, the autonomous system applied an active investment strategy combined with an artificial somatic index. The results show that this autonomous system presented a higher level of investment decision effectiveness, understood as the achievement of greater wealth over time, as measured by profitability, utility, and Sharpe Ratio indicators, relative to an industry benchmark.

The Effects of Educational Artificial Intelligence-Powered Applications on Teachers’ Perceived Autonomy, Professional Development for Online Teaching, and Digital Burnout

The transformative impact of advancements in educational technology, particularly those powered by artificial intelligence (AI), on the landscape of education and the teaching profession has been substantial. This study explores the repercussions of AI-powered technologies on teachers’ autonomous behavior, digital burnout, and professional development. The study involved a cohort of 320 high school teachers in China segregated into control and experimental groups. The experimental group received instructions on AI-integrated applications and how they might be used in education. However, the teachers assigned to the control group did not receive information on the use of AI educational applications. Three distinct questionnaires probing autonomous behaviors, digital burnout, and online professional development were administered, and the ensuing data were analyzed using independent sample t-tests. The findings elucidate a discernible positive impact of AI-integrated technology intervention on teachers’ professional development and autonomous behaviors. The incorporation of AI-enhanced tools facilitated an augmentation in teachers’ professional growth and bolstered their independent and self-directed instructional practices. Notably, using AI-integrated technology significantly reduced teachers’ susceptibility to digital burnout, signifying a potential alleviation of stressors associated with technology-mediated teaching. This research provides valuable insights into the multifaceted effects of AI-powered technologies on educators, shedding light on enhancing professional competencies and mitigating digital burnout. The implications extend beyond the confines of this study, resonating with the broader discourse on leveraging technology to augment the teaching profession and optimize the learning environment.

Assessment of Balance During a Single-Limb Stance Task in Healthy Adults: A Cross-Sectional Study.

Single-limb stance (SLS) is a demanding postural task, widely used for balance assessment in both research and clinical practice. Despite extensive data on elderly and clinical populations, less is known about younger and healthier adults. Our aim in this study was to assess balance during a SLS task among a cohort of healthy adults to determine whether there are age or sex group or testing condition differences in performances. In this cross-sectional study, we involved 120 participants aged 30-65 years and divided them into four age sub-groups with equal numbers of males and females in each. We assessed balance during a 45-s SLS task on a] the Delos Postural Proprioceptive System for both lower limbs in two conditions - open eyes (OE) and closed eyes (CE). We calculated stability (SI) and autonomy (AU) indices and used analysis of variance to determine that there was no significant effect of limb dominance or sex on balance parameters. However, there was a significant interaction effect between age group and testing condition for both SI and AU (p < .001 for both), with balance worsening as age increased only in the CE condition. These results highlight a pattern of balance decline with age when vision is eliminated from balance performance, underscoring the critical relationship between sensory input and postural control as people age.

A Bio-Inspired Dopamine Model for Robots with Autonomous Decision-Making.

Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating humans can bring robots with skills that users can understand more easily. Human decisions highly depend on dopamine, a brain substance that regulates motivation and reward, acknowledging positive and negative situations. Considering recent neuroscience studies about the dopamine role in the human brain and its influence on decision-making and motivated behaviour, this paper proposes a model based on how dopamine drives human motivation and decision-making. The model allows robots to behave autonomously in dynamic environments, learning the best action selection strategy and anticipating future rewards. The results show the model's performance in five scenarios, emphasising how dopamine levels vary depending on the robot's situation and stimuli perception. Moreover, we show the model's integration into the Mini social robot to provide insights into how dopamine levels drive motivated autonomous behaviour regulating biologically inspired internal processes emulated in the robot.

Encountering robots: Customers’ autonomous behaviors in tourism services

Service providers shape customers’ desires to increase their profits, potentially limiting customer choices and expression. This is prominent in tourism because of the unfamiliarity and uncertainty of destinations. Drawing on social impact theory, we posit that customers feel more comfortable and behave more autonomously when they encounter a service provider with less social impact. Through five scenario-based experiments and one field experiment, our findings illustrate that customers are more willing to develop autonomous behaviors under robotic service than under human service. Service robots are perceived to possess less social influence than human employees, allowing customers to feel more comfortable and behave more autonomously. We also discuss the boundary conditions of self-presentation concern and self-construal on this effect. This study enriches the research on social impact theory and consumer autonomy and provides actionable insights into robotic adoption in tourism services.

Agency as an Inherent Property of Living Organisms

AbstractA central characteristic of living organisms is their agency, that is, their intrinsic activity, both in terms of their basic life processes and their behavior in the environment. This aspect is currently a subject of debate and this article provides an overview of some of the relevant publications on this topic. We develop the argument that agency is immanent in living organisms. There is no life without agency. Even the basic life processes are an intrinsic activity, which we call the organismic level of agency. In addition to this we describe several further levels. These capture different qualities that occur or transform during evolution. In addition to the organismic level, we propose an ontogenetic level, a level of directed agency, directed agency with extended flexibility, and a level that includes the capacities to follow preconceived goals. A further property of organisms is their autonomy. It has been shown that the capacity for autonomy changed during evolution. Here we propose that the two organismic properties autonomy and agency are closely related. Enhanced physiological and behavioral autonomy extends the scope of self-generated, flexible actions and reactions. The increase in autonomy through the evolution of a widened scope of behavioral possibilities and versatility in organisms coincides with extended levels of agency. Especially the human organization, including the sophisticated brain, is the basis for an extended level of agency referring to the capacities to follow preconceived goals. However, it is important for the understanding of the phenomenon of agency not only to assume this latter form, but also to look at the different levels of agency.

Engineered nascent living human tissues with unit programmability.

Leveraging human cells as materials precursors is a promising approach for fabricating living materials with tissue-like functionalities and cellular programmability. Here we describe a set of cellular units with metabolically engineered glycoproteins that allow cells to tether together to function as macrotissue building blocks and bioeffectors. The generated human living materials, termed as Cellgels, can be rapidly assembled in a wide variety of programmable three-dimensional configurations with physiologically relevant cell densities (up to 108 cells per cm3), tunable mechanical properties and handleability. Cellgels inherit the ability of living cells to sense and respond to their environment, showing autonomous tissue-integrative behaviour, mechanical maturation, biological self-healing, biospecific adhesion and capacity to promote wound healing. These living features also enable the modular bottom-up assembly of multiscale constructs, which are reminiscent of human tissue interfaces with heterogeneous composition. This technology can potentially be extended to any human cell type, unlocking the possibility for fabricating living materials that harness the intrinsic biofunctionalities of biological systems.

Shape Memory Polymers with Patternable Recovery Onset Regulated by Light.

Shape memory polymers (SMPs) show attractive prospects in emerging fields such as soft robots and biomedical devices. Although their typical trigger-responsive character offers the essential shape-changing controllability, having to access external stimulation is a major bottleneck toward many applications. Recently emerged autonomous SMPs exhibit unique stimuli-free shape-shifting behavior with its controllability achieved via a delayed and programmable recovery onset. Achieving multi-shape morphing in an arbitrary fashion, however, is infeasible. In this work, a molecular design that allows to spatio-temporally define the recovery onset of an autonomous shape memory hydrogel (SMH) is reported. By introducing nitrocinnamate groups onto an SMH, its crosslinking density can be adjusted by light. This affects greatly the phase separation kinetics, which is the basis for the autonomous shape memory behavior. Consequently, the recovery onset can be regulated between 0 to 85min. With masked light, multiple recovery onsets in an arbitrarily defined pattern which correspondingly enable multi-shape morphing can be realized. This ability to achieve highly sophisticated morphing without relying on any external stimulation greatly extends the versatility of SMPs.

Muutuvad arusaamad lastest ja laste uuenduslik energia

Understanding the child and childhood depends on the content attached to these concepts. Different images have prevailed in different times and they reflect in modern understandings of children. Drawing on Philippe Ariès’ seminal work, Centuries of Childhood, the paper offers a glimpse of images of children through time. History has portrayed the vulnerable, fragile child who needs protection and the immature child who needs to be educated and disciplined. Both images are represented in adults’ and children’s perceptions of today’s children. These perceptions in turn influence the way adults treat children. For example, a parent who sees the child as vulnerable and fragile may be overprotective and limit the child’s autonomous behaviour; another parent may not be able to negotiate with the child and may be easily punitive in situations where the child makes mistakes. According to children, they do not misbehave deliberately, but because they are immature and lack knowledge and experience. Understandably, they also agree with the image of a vulnerable child – in some situations they need to feel safe and protected. With the adoption of the Convention on the Rights of the Child by the UN General Assembly in 1989 and its ratification by state governments, the image of the socially competent child began to develop vigorously. The current article focuses on the competent image of the child, according to which a child is an active social agent and socially competent according to age to evaluate his or her lived life. The paper recalls keynotes of Ivar Frønes and Asher Ben-Arieh at international children’s well-being conference which was held in Tartu in 2019. According to them, while an adult thinks about life lived today, and thinking about tomorrow is the quest to “think out of the box”, a child has to live life forward each day, i.e., find solutions here and now, harnessing their innovative energy; for example, when confronted with new situations that require new solutions, or when interpreting and reproducing knowledge individually and collectively, shaping the world in which they live and, last but not least, bringing about change around them, e.g., contributing to the parent’s growth into parenthood. The challenge of modern childhood is to understand social processes in a forward-looking way, focusing not on future adulthood but on the here-and-now of childhood. Children use their innovative energy in everyday interactions and play, which is an important part of their lives. In a broad sense, play is action, it is practice of living, including the development of new skills and self-socialisation. There is plenty of innovative energy in children in their free play. In conclusion, it has to be said that, as society develops more rapidly, innovative energy and thinking are increasingly becoming a challenge for adults too. The article is based on the introductory presentation of the winter conference of folklorists, “Playing and Playfulness in Folklore”, held in Kuremaa Castle on 2–3 March 2023.

DNA Dissipative System for Controlled Release of Immunostimulatory CpG Oligodeoxynucleotides

Herein, a dissipative system tailored for the controlled loading and release of CpG oligodeoxynucleotides (CpG ODNs), known for their pharmacological immunostimulatory properties, is reported. The approach involves multiple cycles of deactivation and activation of the CpG ODNs via its hybridization with a complementary fuel strand, followed by its selective release mediated by the enzymatic activity of T7 exonuclease. The autonomous and temporal behavior of this dissipative system can be tuned by three factors: the design of the fuel strand and its concentration that governs the kinetics of the forward hybridization reaction, as well as the concentration of T7 exonuclease, which regulates the backward energy dissipation reaction. Furthermore, the enzyme's tolerance toward waste accumulation is demonstrated, and the system's robust performance when utilizing various fuel strands in alternating fashion is showcased. The findings underscore the potential of this approach for precise and programmable delivery of therapeutic nucleic acids in multiple cycles, with implications for enhancing immunotherapeutic strategies in which controlled kinetics of the nucleic acid is highly desired.

A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations.

Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.

Human-robot interactions in autonomous hospital transports

The integration of robotics in nursing is a significant shift in healthcare, driven by the aging global population and the increasing demand for care. Robots in nursing can handle less technical tasks such as patient transport and rehabilitation activities. This support allows caregivers to focus on less strenuous nursing duties and more direct patient care. Human-Robot Interaction (HRI) plays an important role in this challenging context. In this research, we present an autonomous hospital transport system based on the ROS 2 framework, focusing on enhancing HRI in the healthcare environment. It encompasses the development of a control architecture for autonomous robot behavior, the implementation of machine learning for emergency detection, and the creation of a user-friendly interface for both patients and staff. The proposed concepts were validated in real-world scenarios in three different hospitals in Germany. This not only demonstrates the practical application of this system but also shares insights and methods, encouraging further advancement in the field of healthcare robotics.

Unlocking the Potential in a Gamification-Based MOOC: Assessing Autonomous Learning and Self-Directed Learning Behaviors

Massive open online courses (MOOCs) have significantly advanced online education. Yet, MOOC instructors still grapple with the ongoing challenge of low learner engagement while striving to foster autonomous and self-directed learning (SDL). Integrating gamification into these courses could be a promising solution. However, the extent to which gamification shapes autonomous and SDL behaviors within MOOCs remains underexplored. This mixed-methods sequential explanatory study investigates the interplay between MOOC, gamification, autonomous learning, and SDL behaviors to harness the potential of online education. We address this research void by delving into the intricate relationship between gamification and learners’ engagement in autonomous learning and SDL behaviors. Drawing upon self-determination theory (SDT) as a theoretical framework, we scrutinize how gamified MOOCs influence intrinsic motivation and SDL behaviors. Our study utilizes questionnaires and structured interviews to comprehensively examine the users’ experiences within a gamified MOOC. The findings suggest that thoughtfully integrated gamification elements not only enhance learner engagement but also stimulate autonomous learning and SDL behaviors. However, it is crucial to approach the integration of gamification thoughtfully, ensuring a balance that promotes intrinsic motivation without undue reliance on extrinsic motivators. These findings hold significant implications for educators, course designers, and policymakers leveraging gamification to enrich online learning experiences and cultivate self-directed educational pathways. They offer valuable insights for educators, course designers, and policymakers aiming to leverage gamification for enriching online learning experiences and nurturing self-directed educational pathways in MOOCs.

Fostering Learner Autonomy and Best Approaches to Implement the Practice

As per the definition, “The ability to take charge of one’s own learning”, Learner Autonomy (LA) has been a significant theme of interest in second and foreign language learning over the last five decades. Yet this concept is not widely practiced among local learners in the English as a Second Language (ESL) pedagogy. Implementing autonomous learning behaviours benefits the education system, of a country like Sri Lanka, to minimize most obstacles the ESL learners encounter. For a successful implementation of the concept, the contribution of both teachers and students is essential. Therefore, this research aims to identify sixty ESL teachers’ understanding of how much they support in fostering LA, and the best approaches to implement the practice. Using the simple random sampling method, 10% out of 600 English teachers from the Galle zonal division were given a researcher-made questionnaire that included four sections. A 5-point Likert scale was used to measure the collected data, and they were quantitatively analyzed using descriptive statistics. The SPSS version 21 was used, while Cronbach’s alpha determined the reliability of the Likert scale. The questions were composed of identifying the teachers’ understanding upon the LA concept, the teachers’ roles and responsibilities in planning, implementing, monitoring, and evaluating the class; and how the teachers view their learners’ abilities to take responsibility in planning, implementing, monitoring, and assessing their learning tasks, while suggestions were forwarded to get the teachers’ preferences as the best approaches to foster autonomy among ESL learners. Significantly, 98.3% believe that implementing LA is essential, and the study concluded with the understanding that teachers are willing to cultivate LA if a suitable environment is created.

Artificial Intelligence Applied to Drone Control: A State of the Art

The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape.