This letter presents a model to address the collaborative effects in multi-agent systems from the perspective of microscopic mechanism. The model utilizes distributed control for robot swarms in traversal applications. Inspired by pedestrian planning dynamics, the model employs three types of forces to regulate the behavior of agents: intrinsic propulsion, interaction among agents, and repulsion from obstacles. These forces are able to balance the convergence, divergence and avoidance effects among agents. Additionally, we present a planning and decision method based on resultant forces to enable real-world deployment of the model. Experimental results demonstrate the effectiveness on system path optimization in unknown cluttered environments. The sensor data is swiftly digital filtered and the data transmitted is significantly compressed. Consequently, the model has low computation costs and minimal communication loads, thereby promoting environmental adaptability and system scalability.

Multi-agent systems (MAS) need to adaptively cope with dynamic environments, changing agent populations, and diverse tasks. However, most of the multi-agent systems cannot easily handle them, due to the complexity of the state and task space. The social impact theory regards the complex influencing factors as forces acting on an agent, emanating from the environment, other agents, and the agent's intrinsic motivation, referring to the social force. Inspired by this concept, we propose a novel gradient-based state representation for multi-agent reinforcement learning. To non-trivially model the social forces, we further introduce a data-driven method, where we employ denoising score matching to learn the social gradient fields (SocialGFs) from offline samples, e.g., the attractive or repulsive outcomes of each force. During interactions, the agents take actions based on the multi-dimensional gradients to maximize their own rewards. In practice, we integrate SocialGFs into the widely used multi-agent reinforcement learning algorithms, e.g., MAPPO. The empirical results reveal that SocialGFs offer four advantages for multi-agent systems: 1) they can be learned without requiring online interaction, 2) they demonstrate transferability across diverse tasks, 3) they facilitate credit assignment in challenging reward settings, and 4) they are scalable with the increasing number of agents.

Navigating mobile robots in social environments remains a challenging task due to the intricacies of human-robot interactions. Most of the motion planners designed for crowded and dynamic environments focus on choosing the best velocity to reach the goal while avoiding collisions, but do not explicitly consider the high-level navigation behavior (avoiding through the left or right side, letting others pass or passing before others, etc.). In this work, we present a novel motion planner that incorporates topology distinct paths representing diverse navigation strategies around humans. The planner selects the topology class that imitates human behavior the best using a deep neural network model trained on real-world human motion data, ensuring socially intelligent and contextually aware navigation. Our system refines the chosen path through an optimization-based local planner in real time, ensuring seamless adherence to desired social behaviors. In this way, we decouple perception and local planning from the decision-making process. We evaluate the prediction accuracy of the network with real-world data. In addition, we assess the navigation capabilities in both simulation and a real-world platform, comparing it with other state-of-the-art planners. We demonstrate that our planner exhibits socially desirable behaviors and shows a smooth and remarkable performance.

To well understand crowd behavior, microscopic models have been developed in recent decades, in which an individual's behavioral/psychological status can be modeled and simulated. A well-known model is the social-force model innovated by physical scientists (Helbing and Molnar, 1995; Helbing, Farkas and Vicsek, 2000; Helbing et al., 2002). This model has been widely accepted and mainly used in simulation of crowd evacuation in the past decade. A problem, however, is that the testing results of the model were not explained in consistency with the psychological findings, resulting in misunderstanding of the model by psychologists. This paper will bridge the gap between psychological studies and physical explanation about this model. We reinterpret this physics-based model from a psychological perspective, clarifying that the model is consistent with psychological theories on stress, including time-related stress and interpersonal stress. Based on the conception of stress, we renew the model at both micro-and-macro level, referring to multi-agent simulation in a microscopic sense and fluid-based analysis in a macroscopic sense. The cognition and behavior of individual agents are critically modeled as response to environmental stimuli. Existing simulation results such as faster-is-slower effect will be reinterpreted by Yerkes-Dodson law, and herding and grouping effect as well as oscillation phenomenon are further discussed for pedestrian crowd. In brief the social-force model exhibits a bridge between the physics laws and psychological principles regarding crowd motion, and this paper will renew and reinterpret the model on the foundation of psychological studies.

This article introduces a modeling framework to characterize evacuee response to environmental stimuli during emergency egress. The model is developed in consistency with stress theory, which explains how an organism reacts to environmental stressors. We integrate the theory into the well-known social-force model, and develop a framework to simulate crowd evacuation behavior in multi-compartment buildings. Our method serves as a theoretical basis to study crowd movement at bottlenecks, and simulate their herding and way-finding behavior in normal and hazardous conditions. The pre-movement behavior is also briefly investigated by using opinion dynamics. The algorithms have been partly tested in FDS+EVAC as well as our simulation platform crowdEgress.

Emergency evacuation describes a complex situation involving time-critical decision-making by evacuees. Mobile robots are being actively explored as a potential solution to provide timely guidance. In this work, we study a robot-guided crowd evacuation problem where a small group of robots is used to guide a large human crowd to safe locations. The challenge lies in how to utilize micro-level human-robot interactions to indirectly influence a population that significantly outnumbers the robots to achieve the collective evacuation objective. To address the challenge, we follow a two-scale modeling strategy and explore mean-field hydrodynamic models which consist of a family of microscopic social-force models that explicitly describe how human movements are locally affected by other humans, the environment, and the robots, and associated macroscopic equations for the temporal and spatial evolution of the crowd density and flow velocity. We design controllers for the robots such that they not only automatically explore the environment (with unknown dynamic obstacles) to cover it as much as possible but also dynamically adjust the directions of their local navigation force fields based on the real-time macro-states of the crowd to guide the crowd to a safe location. We prove the stability of the proposed evacuation algorithm and conduct a series of simulations (involving unknown dynamic obstacles) to validate the performance of the algorithm.

Predicting trajectories of pedestrians based on goal information in highly interactive scenes is a crucial step toward Intelligent Transportation Systems and Autonomous Driving. The challenges of this task come from two key sources: (1) complex social interactions in high pedestrian density scenarios and (2) limited utilization of goal information to effectively associate with past motion information. To address these difficulties, we integrate social forces into a Transformer-based stochastic generative model backbone and propose a new goal-based trajectory predictor called ForceFormer. Differentiating from most prior works that simply use the destination position as an input feature, we leverage the driving force from the destination to efficiently simulate the guidance of a target on a pedestrian. Additionally, repulsive forces are used as another input feature to describe the avoidance action among neighboring pedestrians. Extensive experiments show that our proposed method achieves on-par performance measured by distance errors with the state-of-the-art models but evidently decreases collisions, especially in dense pedestrian scenarios on widely used pedestrian datasets.

London, Dec 24 (IANS) In a bid a to teach robots how to respect personal space, scientists are now giving mobile robots a crash course in avoiding collisions with humans. Using "impedance" control, the researchers at the Institute of Automatics of the National University of San Juan in Argentina aimed to regulate the social dynamics between the robot's movements and the interactions of the robot's environment. The team did this by first analysing how a human leader and a human follower interact on a set track with well-defined borders. The feedback humans use to adjust their behaviours - letting someone know they're following too closely, for example - was marked as social forces and treated as defined physical fields. When a robot follows a human as part of a formation, it is supposed that it must also respect these social zones to improve its social acceptance," wrote Daniel Herrera, and an author on the study.

WASHINGTON: Scientists are teaching mobile robots how to respect the personal space of humans beings and avoid collisions with them. "When a robot follows a human as part of a formation, it is supposed that it must also respect these social zones to improve its social acceptance," said Herrera. Using impedance control, researchers aimed to regulate the social dynamics between the robot's movements and the interactions of the robot's environment. They did this by first analysing how a human leader and a human follower interact on a set track with well-defined borders. The feedback humans use to adjust their behaviors - letting someone know they're following too closely, for example - was marked as social forces and treated as defined physical fields.