JournalTOCs

Artificial Life and Robotics
Journal Prestige (SJR): 0.226

Number of Followers: 17

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1614-7456 - ISSN (Online) 1433-5298
Published by Springer-Verlag

[2468 journals]

A cognitive strategy for service robots in recognizing emotional attribute
of objects
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  Abstract: Abstract With the advancement of service robots, discovering the emotional needs of users is becoming increasingly important. Unlike research focusing solely on human facial expression recognition or image sentiment recognition, our work proposes that the objects in the environment also impact human emotions, such as candy, which can make people happy. Therefore, studying the impact of objects on human emotions is crucial for service robots to regulate human emotions and provide more satisfactory services. In this work, we first propose the emotional attribute of objects: the ability to improve people’s moods. And we propose a strategy for recognizing this attribute. To achieve this, we first construct the H–S object emotional attribute image dataset, which contains different objects with pleasant or unpleasant emotion labels for people. We then propose the YOLOv3-SESA object detection model. By incorporating YOLOv3 with the SESA attention module, the model focuses more on the target objects, achieving higher recognition accuracy for small objects in the environment. We gain the correlation frequency between objects and emotion labels and convert it into emotional attribute probability values. Objects with the value exceeding a predefined threshold are defined as having an emotional attribute. Our experiments validate the effectiveness of our approach, yielding a list of common objects that can please users. By leveraging the knowledge of object emotional attributes, service robots can proactively provide emotionally appealing objects to humans, offering psychological comfort when they are depressed.
  PubDate: 2024-08-27
Effects of invisible body and optic flow on experience of users
voluntarily walking in a VR environment
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  Abstract: Abstract Studies have demonstrated that a multi-modal virtual reality (VR) system can enhance the realism of virtual walking. However, a few studies explore the body awareness altered by visual presentation of virtual body and optic flow during locomotion in VR. This study investigated the impact of invisible body and optic flow on experience of users voluntarily walking in a camera-image VR environment. Participants wearing a head-mounted display performed six-step walking at their own timing. Three experimental conditions providing visible body and optic flow as a baseline, invisible body and optic flow, and invisible body and no flow, were conducted on three different days. We found that losing visual body per se decreased the feeling of being-there-now. However, providing continuous optic flow maintained virtual presence equivalent to the baseline in terms of immersion and natural walking, as opposed to providing discontinuous flow. We discussed these results in association with body awareness.
  PubDate: 2024-08-14
Revealing inputs causing web API performance latency using
response-time-guided genetic algorithm fuzzing
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  Abstract: Abstract Web APIs are integral to modern web development, enabling service integration and automation. Ensuring their performance and functionality is critical, yet performance testing is less explored due to the difficulty in detecting performance bugs. This paper presents a response time-guided genetic algorithm (GA) fuzzing approach to uncover web API performance latency in a black-box setting. Unlike traditional random input generation, our method uses GA to refine inputs through crossover and mutation, guided by response time-based fitness. We propose two seed generation methods: pairwise combinatorial testing using Mircosoft’s Pairwise Independent Combinatorial Testing (PICT) and randomly paired combinations. We compared our method with classic random fuzzing. Experiments on five real-world web APIs show that our approach significantly outperforms classic random fuzzing, identifying inputs with response times 1.5 to 26.3 times longer. Additionally, PICT-generated seeds demonstrated superior performance compared to randomly-paired combinations in 2 out of 5 APIs. Our findings highlight the potential of GA-based fuzzing to reveal web API performance latency, advocating for further research in this area.
  PubDate: 2024-08-02
Inhibition of convergence of mimicry rings by low learning abilities of
predators
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  Abstract: Abstract Since Müllerian mimicry is more effective, the greater the number of species involved, multiple mimicry rings tend to gradually converge into one large mimicry ring, which then tends to expand. However, in nature, mimicry rings often do not converge into a single ring. It is believed that various factors lead to the diversification of mimicry rings. In this study, we conducted an evolutionary simulation experiment using a multi-agent system (MAS) based on the hypothesis that “predators with low learning ability cannot learn the patterns of toxic prey and continue to prey on species that exhibit Müllerian mimicry." Our aim was to investigate the inhibitory factor of convergence of mimicry rings. We use two types of agent models that make up the MAS: PREY-agent and PREDATOR-agent. Each PREDATOR-agent encounters a PREY-agent randomly at each step, decides whether to prey on the PREY-agent or not based on the pattern of the PREY-agent using its own feed-forward neural network (FFNN); it also uses its FFNN to learn the relationship between the PREY-agent’s pattern and the presence or absence of venom. The PREY-agent determines its own fitness based on the results of whether or not it was preyed upon by PREDATOR-agent, performs genetic evolution based on this fitness, and decodes its own genes to generate patterns. This pattern is generated using a modified L-system. Evolutionary simulation experiments using the MAS showed that the convergence of the mimicry ring is inhibited when the number of neurons in the hidden layer in the PREDATOR-agent’s FFNN is small, i.e., when the learning ability of the PREDATOR-agent is low.
  PubDate: 2024-07-20
A conceptual examination of an additive manufactured high-ratio coaxial
gearbox
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  Abstract: Abstract This research introduces the novel “Kraken-Gear” mechanism, emphasizing the advantages of additive polymer 3D printing in high-ratio gearbox systems for lightweight robotic applications, such as surgical instruments. The innovative kinematic solution provides high torsional system stiffness, substantial gear ratios, and backlash-free transmission. Leveraging the “hot lithography” additive manufacturing method ensures precise and warp-free gearbox components. Targeting medical technology, the gearbox meets stringent requirements: backlash-free, minimal vibration, high precision, and torque, with minimized weight for ergonomic comfort and fatigue mitigation. Computational simulations assess forces and stresses, highlighting the potential of additive manufacturing for cost-effective and functionally efficient gearbox fabrication. Nevertheless, careful material selection remains imperative for optimal functionality, especially in demanding medical applications. In summary, this research underscores a promising approach to gearbox fabrication, emphasizing the critical role of material selection and simulation-based assessments for optimal performance.
  PubDate: 2024-06-10
  DOI: 10.1007/s10015-024-00955-6
Effect of subjective health conditions on facial skin temperature
distribution: a 1-year statistical analysis among four participants
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  Abstract: Abstract Thermal skin images are used to evaluate the physiological and psychological states of patients. To implement remote daily health monitoring, we attempted to assess subjective health conditions using thermal face images. In our previous study, we constructed an anomaly detection model to detect poor health conditions; the area under the receiver operating characteristic curve of the anomaly-detection model was 0.70. However, how the spatial distribution of facial skin temperature changes in response to subjective health conditions remains unclear. In this study, we statistically analyzed the acquired thermal face images to investigate the effect of subjective health conditions on facial skin temperature distribution. As a result of the comparison between health conditions, we confirmed that typically the average temperatures and left-right asymmetry in some regions of the face were significantly higher in poor health conditions than in good health conditions.
  PubDate: 2024-06-07
  DOI: 10.1007/s10015-024-00953-8
Inferring source of learning by chimpanzees in cognitive tasks using
reinforcement learning theory
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  Abstract: Abstract Reinforcement learning is a mathematical framework for learning better choices through trial-and-error. Recent studies revealed that reinforcement learning is applicable to animal behavior and cognition. However, applying reinforcement learning to animal behavior sometimes encounters difficulties because the information sources utilized by animals to make choices are often unknown, whereas this is identified as the “state” in the reinforcement learning framework. We sought to identify possible state settings including non-standard formulations suitable for explaining data from past chimpanzee studies. Although chimpanzees’ performance in a serial learning task was inconsistent with standard reinforcement learning formulations, we found that the combination of state-independent choice making and state-dependent evaluation produced consistent results. Exploration of state settings in reinforcement learning may shed new light on animal learning processes.
  PubDate: 2024-06-05
  DOI: 10.1007/s10015-024-00954-7
Research on automatic error-checking in English short text translation by
a neural network algorithm
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  Abstract: Abstract With the growing population of English learners, how to improve the efficiency of English learning has become a focus of research. This article focuses on automatic error-checking in English short text translation. The Transformer model was enhanced by combining with the bidirectional gated recurrent unit (BiGRU) algorithm to create a dual-encoder model that better captures information within input sequences. Experiments were then conducted on different corpora. The improved Transformer model obtained a \({\text{F}}_{0.5}\) of 59.09 on CoNLL-2014 and 61.05 Google-bilingual evaluation understudy (GLEU) on JFLEG, both of which were better than the other methods compared. The case analysis showed that the improved Transformer model accurately found errors in short text translation. The findings indicate that the proposed approach is reliable in the automatic error-checking of English short text translation and can be applied in practice.
  PubDate: 2024-05-31
  DOI: 10.1007/s10015-024-00952-9
Real-time translation of English speech through speech feature extraction
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  Abstract: Abstract Real-time English speech translation is useful in numerous situations, including business and travel. The goal of this research is to improve real-time English speech translation efficacy. Initially, filter bank (FBank) features were extracted from English speech. Subsequently, an enhanced Transformer model was introduced, incorporating a causal convolution module in the front end of the encoder to capture English speech features with location information. The performance of the optimized model in translating English speech to different target languages was tested using the MuST-C dataset. The results revealed differences in translation results for different target languages using the improved Transformer. The highest bilingual evaluation understudy (BLEU) score was observed for Spanish text at 20.84, while Russian text obtained the lowest score of 10.56. The average BLEU score was 18.51, with an average lag time delay of 1202.33 ms. Compared to the conventional Transformer model, the improved model exhibited higher BLEU scores, lower time delay, and optimal performance when utilizing a convolutional kernel size of 3 × 3. The results demonstrate the dependability of the improved Transformer model in real-time English speech translation, highlighting its practical usefulness.
  PubDate: 2024-05-27
  DOI: 10.1007/s10015-024-00951-w
Human gait-type recognition without pre-training: an adaptive fuzzy-based
approach for locomotion-assistance devices
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  Abstract: Abstract Gait-type recognition is important for robotic exoskeletons and walking-assistance devices to adjust their output according to the users’ needs. However, the growing trend of using machine learning (ML) models is both labor- and data-intensive, which makes it practically less attractive for application in exoskeletons and wearable-assistive devices. This research aims to devise a fuzzy-based gait recognition algorithm that requires minimum training data (only 40 cycles for each of the 5 gait types) and adapts to new users without having the need of pre-training for each of them. The proposed algorithm uses the fuzzy logic system (FLS) and Welford’s (variance computation) method to enhance the adaptability by adjusting the rules for gait-type recognition and fine-tuning them in real time for every new user without requiring a specific prior training. Simulation-based evaluation of the proposed algorithm shows a gait-type recognition accuracy of 63.0%, an improvement of 36.8% over the non-adaptive fuzzy-based recognition algorithm. Moreover, the results show that the proposed algorithm outperforms the popular ML methods (support vector machine, Naive Bayes classifier, and logistic regression) when subjected to limited gait-cycles data and no prior training is provided.
  PubDate: 2024-05-24
  DOI: 10.1007/s10015-024-00950-x
Robust active suspension control of vehicles with varying vehicle mass
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  Abstract: Abstract In vehicles with active suspensions, if handling stability is improved strongly by using a LQ controller for a road surface such as large bumps, ride comfort may deteriorate even for a road surface such as not so large bumps. Recently, to avoid the problem, a nonlinear active suspension control scheme has been proposed. However, in the proposed nonlinear controller, it is required that vehicle mass does not vary. In practice, vehicle mass varies greatly. If vehicle mass varies, the controller has to be redesigned. In this paper, to address the problem, based on the proposed nonlinear control scheme, we develop a new robust active suspension control scheme.
  PubDate: 2024-05-01
  DOI: 10.1007/s10015-023-00931-6
Interchange flow control with dynamic obstacles optimized using genetic
algorithms—a concept of virtual walls
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  Abstract: Abstract In the near future, autonomous vehicles will be able to share information with other vehicles via communication, enabling appropriate traffic control approaches. A new approach to traffic control using fully autonomous driving involves the realization of a flat interchange. Unlike conventional approaches, this study focused on roads and interchanges that do not assume lanes. Specifically, we propose an interchange flow control approach to traffic control using a virtual wall (VW), which acquires and shares the initial position, destination, and speed of all vehicles entering an interchange in a two-dimensional space where vehicles can move freely, and then realizes appropriate control based on this information. Each vehicle individually calculates the shortest path to avoid the VW, thereby realizing a safe and rational path selection. In this study, a genetic algorithm was used to determine the location of the VW. The effectiveness of the proposed method was evaluated using simulations, and the results showed that compared to manual deployment in the roundabout form, the proposed method using VWs reduced the total path length and the number of collisions to zero. In addition, when comparing the case where VWs were deployed in common for all vehicles and the case where VWs were deployed individually for each vehicle, it was shown that the total path length was shorter when individual VWs were deployed.
  PubDate: 2024-04-24
  DOI: 10.1007/s10015-024-00946-7
Performance evaluation of character-level CNNs using tweet data and
analysis for weight perturbations
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  Abstract: Abstract Character-level convolutional neural networks (CLCNNs) are commonly used to classify textual data. CLCNN is used as a more versatile tool. For natural language recognition, after decomposing a sentence into character units, each unit is converted into a corresponding character code (e.g., Unicode values) and the code is input into the CLCNN network. Thus, sentences can be treated like images. We have previously applied a CLCNN to verify whether a university’s diploma and/or curriculum policies are well written. In this study, we experimentally confirm the effectiveness of CLCNN using tweet data. In particular, we focus on the effect of the number of units on performance using the following two types of data; one is a real and public tweet dataset on the reputation of a cell phone, and the other is the NTCIR-13 MedWeb task, which consists of pseudo-tweet data and is a well-known collection of tests for multi-label problems. Results of experiments conducted by varying the number of units in the all-coupled layer confirmed the agreement of the results with the theorem introduced in the Amari’s book (Amari in Mathematical Science New Development of Information Geometry, For Senior & Graduate Courses. SAIENSU-SHA Co., 2014). Furthermore, in the NTCIR-13 MedWeb task, we analyze two kinds of experiments, the effects of kernel size and weight perturbation. The results of the difference in the kernel size suggest the existence of an optimal kernel size for sentence comprehension. The results of perturbations to the convolutional layer and pooling layer indicate the possibility of relationship between the numbers of degrees of freedom and network parameters.
  PubDate: 2024-04-05
  DOI: 10.1007/s10015-024-00944-9
Estimated camera trajectory-based integration among local 3D models
sequentially generated from image sequences by SfM–MVS
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  Abstract: Abstract This paper describes a three-dimensional (3D) modeling method for sequentially and spatially understanding situations in unknown environments from an image sequence acquired from a camera. The proposed method chronologically divides the image sequence into sub-image sequences by the number of images, generates local 3D models from the sub-image sequences by the Structure from Motion and Multi-View Stereo (SfM–MVS), and integrates the models. Images in each sub-image sequence partially overlap with previous and subsequent sub-image sequences. The local 3D models are integrated into a 3D model using transformation parameters computed from camera trajectories estimated by the SfM–MVS. In our experiment, we quantitatively compared the quality of integrated models with a 3D model generated from all images in a batch and the computational time to obtain these models using three real data sets acquired from a camera. Consequently, the proposed method can generate a quality integrated model that is compared with a 3D model using all images in a batch by the SfM–MVS and reduce the computational time.
  PubDate: 2024-04-01
  DOI: 10.1007/s10015-024-00949-4
A CPG-based gait planning method for bipedal robots
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  Abstract: Abstract Gait planning is one of the main focuses in the research field of bipedal robotics. To enhance the stability and simplicity of gait planning for bipedal robots using central pattern generator (CPG) methods, this paper first refines the existing Kimura oscillator model. Subsequently, an improved oscillator model is employed to propose a novel configuration of CPG network for flat walking gait planning in bipedal robots. A particle swarm algorithm with variable structural parameters is utilized to optimize the parameters of the CPG network, with the optimization objective being the maximization of stability margin at zero moment points (ZMP) during the walking process of the bipedal robot. Finally, an ADAMS simulation experiment platform is established to validate the feasibility of this method through simulation experiments. The experimental results indicate that this approach enables bipedal robots to achieve stable walking motion on a flat surface.
  PubDate: 2024-03-30
  DOI: 10.1007/s10015-024-00947-6
Analysis of spatial distribution characteristics of facial skin
temperature on stress coping
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  Abstract: Abstract Individuals exhibit two types of responses when exposed to external stimuli. These are called stress-coping responses, or active and passive coping responses, respectively. These stress-coping responses are discriminated by differences in the fluctuations of hemodynamic parameters, such as cardiac output (CO), total peripheral resistance (TPR), and mean blood pressure (MBP), and others. However, the existing method for measuring hemodynamic parameters is contact measurement, which involves wearing a continuous blood pressure cuff; thus, a remote measurement method is required. Therefore, we focused on facial thermal imaging, remotely measurable indicator of the cardiovascular system. We constructed a model to estimate stress-coping responses from the spatial characteristics of facial thermal images using a CNN and sparse coding. However, the standard spatial distribution of facial thermal images of stress-coping response states has not yet been examined. Therefore, in this study, we analyzed the standard spatial distribution of facial thermal images of stress-coping response states. To elicit each stress-coping response, a cold pressure task and a game task were performed. Facial thermal images and hemodynamic parameters were recorded during the experiments. The measured hemodynamic parameters confirmed the elicitation of a stress-coping response. Additionally, using the measured facial thermal images, we evaluated the deviation of the stress-coping response states from a person’s normal state and the standard spatial distribution of each stress-coping response. The results showed that the stress-coping response states deviated from a person’s normal state. In addition, the standard spatial distribution differed for each stress-coping response.
  PubDate: 2024-03-25
  DOI: 10.1007/s10015-024-00942-x
Urban scale pedestrian simulation in Kobe City center
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  Abstract: Abstract We attempted to construct a digital twin of Kobe City center, from the aspect of pedestrian traffic simulation. Policy evaluation was conducted using traffic demand based on mobile phone population statistics provided by NTT DOCOMO, INC., CrowdWalk, an agent-based pedestrian simulator, and manually edited map obtained from Open Street Map to implement pedestrian signals. Background pedestrian population was estimated to be 6509, and 10,000 evacuees are assumed in the Shinko area. All of them are assumed to evacuate into three stations, JR Sannomiya, Hankyu Sannomiya, and Motomachi. The evacuation time was originally simulated to be 25,685 s without any aided policies. As a policy, splitting the evacuation route reduced the evacuation time into 17,780 s which is 69% of the original. In addition, removing signals reduced the time furthermore into 9550 s, 37% in total. Only removing the signal resulted 12,475 s, which is a reduction to 52% of the original.
  PubDate: 2024-03-20
  DOI: 10.1007/s10015-024-00941-y
Online simultaneous localization and mapping with parallelization for
dynamic line segments based on moving horizon estimation
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  Abstract: Abstract In this paper, to render SLAM robust in dynamic environments, we propose a novel LiDAR SLAM algorithm that estimates the velocity of all objects in the scene while suppressing speed of static objects by moving horizon estimation (MHE). We approximate environment features as dynamic line segments having velocity. To deal with static objects as well, MHE is employed, so that its objective function allows the addition of velocity suppression terms that treat stationary objects. By considering association probability, the SLAM algorithm can track the endpoints of line segments to estimate the velocity along the line segments. Even if it is temporarily occluded, the estimation is accurate, because MHE considers a finite length of past measurements. Parallelization of the robot’s localization with the map’s estimation and careful mathematical elimination of decision variables allows online implementations. Post-process modifications remove possible spurious estimates by considering the piercing of LiDAR lasers and integrating maps. Simulation and experiment results of the proposed method prove that the presented algorithm can robustly perform online SLAM even with moving objects present.
  PubDate: 2024-03-01
  DOI: 10.1007/s10015-024-00937-8
Development of a hardware CPG model for controlling both legs of a
musculoskeletal humanoid robot with gait and gait cycle change by higher
center and sensory information
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  Abstract: Abstract Most conventional biped robots process leg movements and information from each sensor by numerical calculation using a CPU. However, to cope with diverse environments, the numerical calculations are enormous, so they must be processed at high speed using a high-performance CPU and high power consumption. On the other hand, focusing on human motor control, it is believed that basic motor patterns such as walking and running are generated by a neural network called the central pattern generator (CPG), which is localized in the spinal cord and is independent of calculation. We previously focused on pulse-type hardware neural networks (P-HNNs), in which the neural network was composed of analog electronic circuits, and developed a hardware CPG model for controlling a single leg of a musculoskeletal humanoid robot. However, to actually move a biped robot, a CPG model that takes into account both legs and sensory information is required. Therefore, this study aims to develop a hardware CPG model for controlling both legs of a musculoskeletal humanoid robot whose gait changes according to the higher center and sensory information. We report on a hardware CPG model configured by circuit simulation confirmed the generation of walking and running patterns.
  PubDate: 2024-02-24
  DOI: 10.1007/s10015-024-00939-6
Investigating the effects of material ratio scenarios on soft robot design
based on morphology–material–control coevolution
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  Abstract: Abstract This study explores the influence of material selection scenarios on the evolutionary design of soft robots. Soft robots have vast potential applications that rigid robots cannot accomplish due to their lightweight and flexible nature. Despite rapid development of soft robotics in recent years, its effective automatic design method remains undeveloped. To combat this, evolutionary computation has been proposed for automatic soft robot design. Material selection has been identified as crucial in influencing the outcomes of autonomously designed soft robots, yet understanding of its impacts remains limited, particularly in changing material selection scenarios across generations. In this study, we hypothesized that these changing scenarios could influence the evolved robots. Initial experiments, based on travel distance and energy efficiency, suggested material selection could greatly impact the final robot. Further experiments, adjusting material ratio and rewarding specific material use, identified how certain material selection scenarios led to changes in the structure and performance of the evolved soft robots. These findings contribute to automatic design methodologies in soft robotics.
  PubDate: 2024-02-10
  DOI: 10.1007/s10015-023-00934-3

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