HUMAN–ROBOT COLLISION AVOIDANCE USING A MODIFIED SOCIAL FORCE MODEL WITH BODY POSE AND FACE ORIENTATION

Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing RHRs with high degrees of human-likeness provides a life-like vessel for humans to physically and naturally interact with technology in a manner insurmountable to any other form of non-biological human emulation. This study outlines a human–robot interaction (HRI) experiment employing two automated RHRs with a contrasting appearance and personality. The selective sample group employed in this study is composed of 20 individuals, categorised by age and gender for a diverse statistical analysis. Galvanic skin response, facial expression analysis, and AI analytics permitted cross-analysis of biometric and AI data with participant testimonies to reify the results. This study concludes that younger test subjects preferred HRI with a younger-looking RHR and the more senior age group with an older looking RHR. Moreover, the female test group preferred HRI with an RHR with a younger appearance and male subjects with an older looking RHR. This research is useful for modelling the appearance and personality of RHRs with EAI for specific jobs such as care for the elderly and social companions for the young, isolated, and vulnerable.

Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population.

In this paper, we formulate human motion tracking as a high dimensional constrained dynamic optimization problem. A novel generative method, called Sequential Immune Genetic Algorithm, is proposed for human motion tracking. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of human motion. As the latent space is low-dimensional and contains the prior knowledge of human motion, it makes pose analysis more efficient and accurate. We apply IGA for pose optimization. Compared with GA and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. As motion tracking is a dynamic optimization problem, we incorporate the temporal continuity information into the traditional IGA and propose a sequential IGA (S-IGA) algorithm. We demonstrate our methods on different videos of different motion types. Experimental results show that the S-IGA motion tracking method can achieve accurate and stable tracking of 3D human motion.

Human motion models are finding an increasing number of novel applications in many different fields, such as building design, computer graphics and robot motion planning. The Social Force Model is one of the most popular alternatives to describe the motion of pedestrians. By resorting to a physical analogy, individuals are assimilated to point-wise particles subject to social forces which drive their dynamics. Such a model implicitly assumes that humans move isotropically. On the contrary, empirical evidence shows that people do have a preferred direction of motion, walking forward most of the time. Lateral motions are observed only in specific circumstances, such as when navigating in overcrowded environments or avoiding unexpected obstacles. In this paper, the Headed Social Force Model is introduced in order to improve the realism of the trajectories generated by the classical Social Force Model. The key feature of the proposed approach is the inclusion of the pedestrians’ heading into the dynamic model used to describe the motion of each individual. The force and torque representing the model inputs are computed as suitable functions of the force terms resulting from the traditional Social Force Model. Moreover, a new force contribution is introduced in order to model the behavior of people walking together as a single group. The proposed model features high versatility, being able to reproduce both the unicycle-like trajectories typical of people moving in open spaces and the point-wise motion patterns occurring in high density scenarios. Extensive numerical simulations show an increased regularity of the resulting trajectories and confirm a general improvement of the model realism.

Human movement tracking is useful in a variety of areas, such as search-and-rescue activities. CCTV and IP cameras are popular as front-end sensors for tracking human motion; however, they are stationary and have limited applicability in hard-to-reach places, such as those where disasters have occurred. Using a drone to discover a person is challenging and requires an innovative approach. In this paper, we aim to present the design and implementation of a human motion tracking method using a Tello EDU drone. The design methodology is carried out in four steps: (1) control panel design; (2) human motion tracking algorithm; (3) notification systems; and (4) communication and distance extension. Intensive experimental results show that the drone implemented by the proposed algorithm performs well in tracking a human at a distance of 2–10 m moving at a speed of 2 m/s. In an experimental field of the size , the drone tracked human motion throughout a whole day, with the best tracking results observed in the morning. The drone was controlled from a laptop using a Wi-Fi router with a maximum horizontal tracking distance of 84.30 m and maximum vertical distance of 13.40 m. The experiment showed an accuracy rate for human movement detection between 96.67 and 100%.

Human motion tracking is a fundamental building block for various applications including computer animation, human-computer interaction, healthcare, and so on. To reduce the burden of wearing multiple sensors, human motion prediction from sparse sensor inputs has become a hot topic in human motion tracking. However, such predictions are non-trivial as (i) the widely adopted data-driven approaches can easily collapse to average poses, and (ii) the predicted motions contain unnatural jitters. In this work, we address the aforementioned issues by proposing a novel framework which can accurately predict the human joint moving angles from the signals of only four flexible sensors, thereby achieving the tracking of human joints in multi-degrees of freedom. Specifically, we mitigate the collapse to average poses by implementing the model with a Bi-LSTM neural network that makes full use of short-time sequence information; we reduce jitters by adding a median pooling layer to the network, which smooths consecutive motions. Although being bio-compatible and ideal for improving the wearing experience, the flexible sensors are prone to aging which increases prediction errors. Observing that the aging of flexible sensors usually results in drifts of their resistance ranges, we further propose a novel dynamic calibration technique to rescale sensor ranges, which further improves the prediction accuracy. Experimental results show that our method achieves a low and stable tracking error of 4.51 degrees across different motion types with only four sensors.

We study the pedestrian motion along a corridor in a nonpanic regime, as usually happens in evacuation scenarios in, e.g., schools, hospitals, or airports, by means of Monte Carlo simulations. We present a model, a combination of the well-known social force model (SFM) and Vicsek model (VM), that takes into account both model interactions, based on the relative position (SFM) and based on the velocity of the particles with some randomness (modulated by an external control parameter, the noise η, VM), respectively. To clarify the influence of the model ingredients we have compared simulations using (a) the pure Vicsek model (VM) with two boundary conditions (periodic and bouncing back) and with or without desired direction of motion, (b) the social force model (SFM), and (c) the model (SFM+VM). The study of steady-state particle configurations in the VM with confined geometry shows the expected bands perpendicular to the motion direction, while in the SFM and SFM+VM particles order in stripes of a given width w along the direction of motion. The results in the SFM+VM case show that w(t)≃t^{α} has a diffusivelike behavior at low noise η (dynamic exponent α≈1/2), while it is subdiffusive at high values of external noise (α<1/2). We observe the well-known order-disorder transition in the VM with both boundary conditions, but the application of a desired direction condition inhibits the existence of disorder as expected. Similar behavior is observed in the SFM case. For the SFM+VM case we find a susceptibility maximum which slowly increases with system size as a function of noise strength. This might be indicative of a order-disorder transition in the range of densities (ρε[1/12,1/9]) and speeds (v_{0}ε[0.5,2]) studied.

In the present paper, we investigate arch-shaped equilibrium solutions in the social force model proposed by Helbing and Molnar (Phys Rev E 51:4282, 1995) and Helbing et al. (Nature 407:487, 2000). The social force model is a system of ordinary differential equations, which describe the motion of the pedestrians under a panic situation. In the simulation of the social force model, we observe an intermittent appearance of arch-shaped structures (i.e. the “Blocking clusters” Parisi and Dorso, Physica A 354:606, 2005; Physica A 385:343, 2007; Frank and Dorso, Physica A 390:2135, 2011) around an exit which block up the flow of pedestrians. To understand such a dynamic behavior, we study arch-shaped equilibrium solutions around an exit under the simplest configuration.

Pedestrian movement modeling and simulation is an important way to formulate an emergency evacuation plan. The performance of the pedestrian movement model directly determines the accuracy of the simulation experiment results. Pedestrian movement in the real world is a very complicated process, and this process is susceptible to a variety of external factors (e.g. road conditions, visibility, etc.) and internal factors (e.g. psychological states, etc.) which making it difficult to model pedestrian movement. The social force model draws on the principles of mechanics and proposes a modeling scheme from the perspective of social force which greatly simplifies the process of pedestrian movement modeling. However, the traditional social force model considers the general situation of pedestrian movement and does not target the emergency evacuation scene, which has certain limitations. In this paper we start with the social force model and introduces the concepts of external guides and internal panic emotions to improves the model. In the scene, the influence of the attraction of the guide and the psychological state of the pedestrian on the movement process of the pedestrian are considered. So that the model is more suitable for the emergency evacuation scene with limited field of vision. It also provides a reference for emergency evacuation plans in public places. We take the subway station as the research object and uses Anylogic software to build simulation experiments. The experimental results show that the improved model we proposed in this paper is less than the traditional model in terms of evacuation time by 8.4%, and is better than the traditional model in terms of exit pedestrian density by 21.5%.

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines view-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classify individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on parameter optimization. We convert the human motion tracking problem into a parameter optimization problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

This paper provides a concise tutorial on the Microsoft Kinect technology and the state of art research on human motion tracking and recognition with Microsoft Kinect. A pre-requisite for human motion recognition is feature extraction. There are two types of feature extraction methods: skeleton joint based, and depth/color image based. Given a set of feature vectors, a motion could be recognized using machine learning, direct comparison, or rule-based methods. We also outline future research directions on the Kinect technology.

Real-time tracking of human body motion has applications in tele-operation, synthetic reality and others. A motion tracking system based on use of the MARG sensors has been under development at Naval Postgraduate School and Miami University. The magnetic, angular rate, and gravity (MARG) sensor modules use a combination of three orthogonal magnetometers, three orthogonal angular rate sensors, and three orthogonal accelerometers to measure 3-D orientation of individual limb segments in order to determine posture. This paper presents the latest results of the MARG human body motion tracking system. The design and implementation of a control interface unit (CIU), a real-time 3-D human avatar called Andy, and a concurrent client-server program are discussed. Experimental testing and evaluation of the overall MARG system is also presented. The system is able to track multiple human limbs in real time. The captured human motion data can be visualized over the Internet by multiple clients using the 3-D avatar.

A 3D Time-of-flight (ToF) image is very useful to accurately track the human body motion due to its precision. However, the ToF image can not provide occluded 3D data because it also has a limitation of camera viewpoint. This paper proposes a self-occlusion handling scheme for human body motion tracking from 3D ToF image sequence. The proposed self-occlusion handling scheme consists of two steps: detect whether the body part is occluded or not and then estimate its motion from estimating the motion of non-occluded its adjacent body parts. Occlusion can be easily detected by using the eigenvalue analysis of 3D ToF data gathered from the joint point of each body part, and their motions can be estimated by calculating the rotation of the occluded body part. To apply it to the human body motion tracking, we use the Iterative closest point (ICP) algorithm and particle filter to track even the motion of fast moving body parts. Experimental results show that the human body motion tracking with the proposed self-occlusion handling scheme can correctly estimate even the motion of the self-occluded body part by comparing the estimated joint points with the manually marked joint points.

Personal space between two human beings is very important. If humans and humanoid robots are to coexist in the same environment, the personal space between them has to be taken into consideration, too. We have evaluated the personal space between a person and a humanoid robot through an experiment using a real robot and a VR robot. If the results about a VR robot and a real robot are not different, the personal space can be evaluated in the future by VR only. As a result of our experiment, we have found that the personal space between a person and a humanoid robot matches to personal distance which is seen between two persons who are in a relationship of friendship/acquaintanceship. Also, no differences were seen in the shapes of personal space for a real robot and a VR robot, respectively.

Stereo vision in detecting human motion is an emerging research for automation, robotics, and sports science field due to the advancement of imaging sensors and information technology. The difficulty of human motion detection and tracking is relatively complex when it is applied to uncontrolled environment. In this paper, a hybrid filter approach is proposed to detect human motion in the stereo vision. The hybrid filter approach integrates Gaussian filter and median filter to reduce the coverage of shadow and sudden change of illumination. In addition, sequential thinning and thickening morphological method is used to construct the skeleton model. The proposed hybrid approach is compared with the normalized filter. As a result, the proposed approach produces better skeleton model with less influential effect on shadow and illumination. The output results of the proposed approach can show up to 86% of average accuracy matched with skeleton model. In addition, obtains approximately 94% of sensitivity measurement in the stereo vision. The proposed approach using hybrid filter and sequential morphology could improve the performance of the detection in the uncontrolled environment.