Adaptive control for robot navigation in human environments based on social force model

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.

This paper presents a new design for formation control on a group of autonomous humanoid robots with obstacle and collision avoidance ability that inspired by human walking behavior in a pedestrian crowd. This human walking behavior model is known as Social Force Model (SFM) and has been widely used to predict individual movement in a crowd in many variety of situation. We combine consensus algorithm with virtual structure approach for formation control and SFM for obstacle and collision avoidance. In this research, we induced some factors in SFM into consensus equation and then implemented into a group of humanoid robots. The aim of the integration is to create a group of robots that are capable of carrying out its collective tasks while still able to maintain its safety. The use of perception effect, distinct obstacle and collision avoidance function on the algorithm will differentiate algorithm with the others. The attractive feature of this new design is the various behaviors of robots to go back to their initial formation after they avoid obstacles. In the case of robot is trapped in a crowded situation (singularity condition), robot will still trying to look a new position, until it find a condition that allow him to move forward. We verify the proposed algorithm by some simulations and experiments.

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.

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.

Social navigation framework for assistive robots in human inhabited unknown environments

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%.

Pushing and overtaking others in a spatial game of exit congestion

Today, many models for describing vehicle movementhave been developed. Most of the available methods for modelingthe vehicle movement in traffic assume that vehicles move on lanesdisciplines. Meanwhile, in developing countries the traffic washeterogeneous, which means there are many types of vehicles. Indeveloping countries, each vehicles usually did not move based onlane disciplines, they move irregularly with unpredictable behavior.The vehicles used to move based on the empty space around themin order to make their travel time faster. Lately, research onmodeling the irregular movement of vehicle using Social ForceModel approached have been developed, especially for mixedtraffic condition in developing countries. This paper will explainabout the basic concept of Social Force Model theories and also willgive a review and comparison of existing the vehicle movementmodel using Social Force Model in mixed traffic condition. Basedupon the review and comparison, some practical issues such asadding some new parameters (driver’s psychology, crowd situationfactor, environmental, driver distraction, etc.) to improve theaccuracy and reliability of vehicle movement model will bepresented as a contribution from this paper.

Simulating Interactions between Pedestrians, Segway Riders and Cyclists in Shared Spaces Using Social Force Model

Robot navigation in large-scale social maps: An action recognition approach

As there are many deficiencies in the methods used for bottleneck identification of transfer pedestrian route, a new method based on social force model is proposed. By taking the transfer pedestrian route as the research object, applying the basic principle of social-force model, the area where bottleneck exists is identified from the view of social force received by pedestrians. By considering both the force received by pedestrians from micro point of view and the velocity from macro point of view, this paper analyzes the social force of pedestrians in the area of bottleneck in the route. Applying the method of observation and example, the effectiveness and advancement of this bottleneck identification method is proposed.

This paper considers the problem of multiple human target tracking in a sequence of video data. A solution is proposed which is able to deal with the challenges of a varying number of targets, interactions, and when every target gives rise to multiple measurements. The developed novel algorithm comprises variational Bayesian clustering combined with a social force model, integrated within a particle filter with an enhanced prediction step. It performs measurement-to-target association by automatically detecting the measurement relevance. The performance of the developed algorithm is evaluated over several sequences from publicly available data sets: AV16.3, CAVIAR, and PETS2006, which demonstrates that the proposed algorithm successfully initializes and tracks a variable number of targets in the presence of complex occlusions. A comparison with state-of-the-art techniques due to Khan , Laet , and Czyz shows improved tracking performance.

Bridging the gap between micro-economics and micro-mobility: A two-dimensional risk-based microscopic model of pedestrians’ and bicyclists’ operational behaviors

In this study, we proposed a new method for the detection of abnormal event based on the social force model (SFM), combined with the local density information of the crowd. The method extracts the local density of the people based on the feature point clustering algorithm. The Latent Dirichlet Allocation (LDA) model is established based on the bag of words method combined with the temporal and spatial features of visual words, then identify the abnormal event using the maximum likelihood function.