How simple rules determine pedestrian behavior and crowd disasters

With the increasing size and frequency of mass events, the study of crowd disasters and the simulation of pedestrian flows have become important research areas. Yet, even successful modeling approaches such as those inspired by Newtonian force models are still not fully consistent with empirical observations and are sometimes hard to calibrate. Here, a novel cognitive science approach is proposed, which is based on behavioral heuristics. We suggest that, guided by visual information, namely the distance of obstructions in candidate lines of sight, pedestrians apply two simple cognitive procedures to adapt their walking speeds and directions. While simpler than previous approaches, this model predicts individual trajectories and collective patterns of motion in good quantitative agreement with a large variety of empirical and experimental data. This includes the emergence of self-organization phenomena, such as the spontaneous formation of unidirectional lanes or stop-and-go waves. Moreover, the combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities-a phenomenon that has been observed during recent crowd disasters. By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms. Furthermore, our behavioral heuristics may serve to improve the navigation of autonomous robots.Comment: Article accepted for publication in PNA

Investigating pedestrians’ obstacle avoidance behaviour

Modelling and simulating pedestrian motions are standard ways to investigate crowd dynamics aimed to enhance pedestrians’ safety. Movement of people is affected by interactions with one another and with the physical environment that it may be a worthy line of research. This paper studies the impact of speed on how pedestrians respond to the obstacles (i.e. Obstacles avoidance behaviour). A field experiment was performed in which a group of people were instructed to perform some obstacles avoidance tasks at two levels of normal and high speeds. Trajectories of the participants are extracted from the video recordings for the subsequent intentions:(i) to seek out the impact of total speed, x and yaxis (ii) to observe the impact of the speed on the movement direction, x-axis, (iii) to find out the impact of speed on the lateral direction, y-axis. The results of the experiments could be used to enhance the current pedestrian simulation models

SANTO: Social Aerial NavigaTion in Outdoors

In recent years, the advances in remote connectivity, miniaturization of electronic components and computing power has led to the integration of these technologies in daily devices like cars or aerial vehicles. From these, a consumer-grade option that has gained popularity are the drones or unmanned aerial vehicles, namely quadrotors. Although until recently they have not been used for commercial applications, their inherent potential for a number of tasks where small and intelligent devices are needed is huge. However, although the integrated hardware has advanced exponentially, the refinement of software used for these applications has not beet yet exploited enough. Recently, this shift is visible in the improvement of common tasks in the field of robotics, such as object tracking or autonomous navigation. Moreover, these challenges can become bigger when taking into account the dynamic nature of the real world, where the insight about the current environment is constantly changing. These settings are considered in the improvement of robot-human interaction, where the potential use of these devices is clear, and algorithms are being developed to improve this situation. By the use of the latest advances in artificial intelligence, the human brain behavior is simulated by the so-called neural networks, in such a way that computing system performs as similar as possible as the human behavior. To this end, the system does learn by error which, in an akin way to the human learning, requires a set of previous experiences quite considerable, in order for the algorithm to retain the manners. Applying these technologies to robot-human interaction do narrow the gap. Even so, from a bird's eye, a noticeable time slot used for the application of these technologies is required for the curation of a high-quality dataset, in order to ensure that the learning process is optimal and no wrong actions are retained. Therefore, it is essential to have a development platform in place to ensure these principles are enforced throughout the whole process of creation and optimization of the algorithm. In this work, multiple already-existing handicaps found in pipelines of this computational gauge are exposed, approaching each of them in a independent and simple manner, in such a way that the solutions proposed can be leveraged by the maximum number of workflows. On one side, this project concentrates on reducing the number of bugs introduced by flawed data, as to help the researchers to focus on developing more sophisticated models. On the other side, the shortage of integrated development systems for this kind of pipelines is envisaged, and with special care those using simulated or controlled environments, with the goal of easing the continuous iteration of these pipelines.Thanks to the increasing popularity of drones, the research and development of autonomous capibilities has become easier. However, due to the challenge of integrating multiple technologies, the available software stack to engage this task is restricted. In this thesis, we accent the divergencies among unmanned-aerial-vehicle simulators and propose a platform to allow faster and in-depth prototyping of machine learning algorithms for this drones

Social-aware drone navigation using social force model

Robot’s navigation is one of the hardest challenges to deal with, because real environments imply highly dynamic objects moving in all directions. The main ideal goal is to conduct a safe navigation within the environment, avoiding obstacles and reaching the final proposed goal. Nowadays, with the last advances in technology, we are able to see robots almost everywhere, and this can lead us to think about the robot’s role in the future, and where we would find them, and it is no exaggerated to say, that practically, flying and land-based robots are going to live together with people, interacting in our houses, streets and shopping centers. Moreover, we will notice their presence, gradually inserted in our human societies, every time doing more human tasks, which in the past years were unthinkable. Therefore, if we think about robots moving or flying around us, we must consider safety, the distance the robot should take to make the human feel comfortable, and the different reactions people would have. The main goal of this work is to accompany people making use of a flying robot. The term social navigation gives us the path to follow when we talk about a social environment. Robots must be able to navigate between humans, giving sense of security to those who are walking close to them. In this work, we present a model called Social Force Model, which states that the human social interaction between persons and objects is inspired in the fluid dynamics de- fined by Newton’s equations, and also, we introduce the extended version which complements the initial method with the human-robot interaction force. In the robotics field, the use of tools for helping the development and the implementation part are crucial. The fast advances in technology allows the international community to have access to cheaper and more compact hardware and software than a decade ago. It is becoming more and more usual to have access to more powerful technology which helps us to run complex algorithms, and because of that, we can run bigger systems in reduced space, making robots more intelligent, more compact and more robust against failures. Our case was not an exception, in the next chapters we will present the procedure we followed to implement the approaches, supported by different simulation tools and software. Because of the nature of the problem we were facing, we made use of Robotic Operating System along with Gazebo, which help us to have a good outlook of how the code will work in real-life experiments. In this work, both real and simulated experiments are presented, in which we expose the interaction conducted by the 3D Aerial Social Force Model, between humans, objects and in this case the AR.Drone, a flying drone property of the Instituto de Robótica e Informática Industrial. We focus on making the drone navigation more socially acceptable by the humans around; the main purpose of the drone is to accompany a person, which we will call the "main" person in this work, who is going to try to navigate side-by-side, with a behavior being dictated with some forces exerted by the environment, and also is going to try to be the more socially close acceptable possible to the remaining humans around. Also, it is presented a comparison between the 3D Aerial Social Force Model and the Artificial Potential Fields method, a well-known method and widely used in robot navigation. We present both methods and the description of the forces each one involves. Along with these two models, there is also another important topic to introduce. As we said, the robot must be able to accompany a pedestrian in his way, and for that reason, the forecasting capacity is an important feature since the robot does not know the final destination of the human to accompany. It is essential to give it the ability to predict the human movements. In this work, we used the differential values between the past position values to know how much is changing through time. This gives us an accurate idea of how the human would behave or which direction he/she would take next. Furthermore, we present a description of the human motion prediction model based on linear regression. The motivation behind the idea of building a Regression Model was the simplicity of the implementation, the robustness and the very accurate results of the approach. The previous main human positions are taken, in order to forecast the new position of the human, the next seconds. This is done with the main purpose of letting the drone know about the direction the human is taking, to move forward beside the human, as if the drone was accompanying him. The optimization for the linear regression model, to find the right weights for our model, was carried out by gradient descent, implementing also de RMSprop variant in order to reach convergence in a faster way. The strategy that was followed to build the prediction model is explained with detail later in this work. The presence of social robots has grown during the past years, many researchers have contributed and many techniques are being used to give them the capacity of interacting safely and effectively with the people, and it is a hot topic which has matured a lot, but still there is many research to be investigated