Artificial Life and Robotics最新文献

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.

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.

In this study, we developed a rimless wheel robot with elastic telescopic legs that can overcome steps. Numerical studies have shown that a rimless wheel robot with elastic telescopic legs has high adaptability to steps. However, rimless wheel robots that can walk in environments with steps have not yet been developed. To develop a rimless wheel robot that could overcome the steps, we initially developed a three-dimensional model of the robot through Unity software and simulated its walking on level ground and surfaces with steps. Then, we determined the optimal elasticity of the elastic telescopic legs through numerical simulation and constructed a model with optimized parameters. Finally, we conducted the walking experiments employing the developed robot. The robot can walk on level ground and surfaces with steps.

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.

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.

In this study, we analysed the novel coronavirus disease (COVID-19) cases data to investigate the regional infection trends in Japan. There had been seven outbreaks by October 2022 in Japan. In each outbreak, the number of COVID-19 cases has increased at different rates in different regions. The prefectural infection ratio is defined using COVID-19 cases data. We calculate the prefectural infection ratio and study the characteristic of each pandemic wave. The prefectural order of infection progression is estimated in each past wave of the COVID-19 pandemic. This study shows that the infection spread from the Kanto region in the fourth pandemic wave and the infection spread simultaneously from four regions in the sixth wave. It is also found that the infection situation trend in Okinawa differs from that in the other regions.

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.

Proportional–integral–derivative (PID) control systems are typically used in power plants owing to their simple structure and ease of implementation. During industrial power generation, binary power plants using low-grade thermal energy sources experience fluctuations in characteristic values due to the presence of impurities and corrosive components in the hot water used as a heat source. Moreover, the temperature of the hot water depends on environmental conditions. However, fine-tuning the PID controller parameters during the operation of binary power plants is challenging, with unmodeled dynamics and uncertainties in parameters arising from changes in the characteristic values. In this study, a novel neural network-based self-adjusting PID control system is proposed, establishing a design strategy for effective control of binary power plants. A comparative analysis of simulation results from control systems with fixed conventional PID parameters, a back-propagation neural network, and the proposed method demonstrates that the proposed self-adjusting PID control approach effectively operates the investigated binary power plant.

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.

Capillary refill time (CRT) is an internationally accepted indicator of peripheral circulation. The CRT is measured by applying compression to the fingernail for a few seconds, releasing it, and observing the process of refilling of blood at the fingertip. The international guidelines for the management of sepsis and septic shock 2021 contains an additional new recommendation for CRT measurement to determine the peripheral circulation statuses of adult patients with septic shock. However, the current CRT measurement method lacks objectivity. Previous studies have reported the development of measurement devices and video analysis systems, which involve complex measurement environment construction and assume only in-hospital measurements. In addition, since medical workers are limited in the number of carrying medical devices, devices that can be used to obtain multiple biometric indicators in a single measurement are needed. Hence, a prototype wearable CRT measurement device was developed in this study, and its feasibility was evaluated by comparing the agreement, intra-class correlation coefficient, and coefficient of variation with those of a CRT measurement device developed in the past. The results indicated that mean CRTs between measurement methods were agreement, with moderate or better intra-rater reliability and no difference in coefficient of variation. Therefore, the results indicate the feasibility of the proposed wearable CRT device.