Agent-based pedestrian modelling

When the focus of interest in geographical systems is at the very fine scale, at the level of streets and buildings for example, movement becomes central to simulations of how spatial activities are used and develop. Recent advances in computing power and the acquisition of fine scale digital data now mean that we are able to attempt to understand and predict such phenomena with the focus in spatial modelling changing to dynamic simulations of the individual and collective behaviour of individual decision-making at such scales. In this Chapter, we develop ideas about how such phenomena can be modelled showing first how randomness and geometry are all important to local movement and how ordered spatial structures emerge from such actions. We focus on developing these ideas for pedestrians showing how random walks constrained by geometry but aided by what agents can see, determine how individuals respond to locational patterns. We illustrate these ideas with three types of example: first for local scale street scenes where congestion and flocking is all important, second for coarser scale shopping centres such as malls where economic preference interferes much more with local geometry, and finally for semi-organised street festivals where management and control by police and related authorities is integral to the way crowds move

Path Planning for Robot and Pedestrian Simulations

The thesis is divided into two parts. The first part presents a new proposed method for solving the path planning problem to find an optimal collision-free path between the starting and the goal points in a static environment. Initially, the grid model of the robot's working environment is constructed. Next, each grid cell's potential value in the working environment is calculated based on the proposed potential function. This function guides the robot to move toward the desired goal location, it has the lowest value at the goal location, and the value increase as the robot moves further away. Next, a new method, called Boundary Node Method (BNM), is proposed to find the initial feasible path. In this method, the robot is simulated by a nine-node quadrilateral element, where the centroid node represents the robot's position. The robot moves in the working environment toward the goal point with eight-boundary nodes based on the boundary nodes' characteristics. In the BNM method, the initial feasible path is generated from the sequence of the waypoints that the robot has to traverse as it moves toward the goal point without colliding with obstacles. The BNM method can generate the path safely and efficiently. However, the path is not optimal in terms of the total path length. An additional method, called Path Enhancement Method (PEM), is proposed to construct an optimal or near-optimal collision-free path. The generated path obtained by BNM and PEM may contain sharp turns. Therefore, the cubic spline interpolation is used to create a continuous smooth path that connects the starting point to the goal point. The performance of the proposed method is compared with the other path planning methods in terms of path length and computational time. Moreover, the multi-goal path planning problem is investigated to find the shortest collision-free path connecting a given set of goal points in the robot working environment. Furthermore, to verify the performance of the proposed method, several experimental tests have been performed on the e-puck robot with different obstacle configurations and various positions of goal points. The experimental results showed that the proposed method could construct the shortest collision-free path and direct the real physical robot to the final destination point. At the end of the first part of the thesis, we investigate the multi-goal path planning problem for the multi-robot system such that several robots reach each goal. In the second part of this thesis, we proposed a new method for simulating pedestrian crowd movement in a virtual environment. The first part of this thesis concerning the generation of the shortest collision-free path is used. In this method, we assumed that the crowd consists of multiple groups with a different number and various types of pedestrians. In this scenario, each group's intention is different for visiting several goal points with varying sequences of the visit. The proposed method uses the multi-group microscopic model to generate a real-time trajectory for each pedestrian navigating in the pedestrianized area of the virtual environment. Additionally, an agent-based model is introduced to simulate pedestrian' behaviours. Based on the proposed method, every single pedestrian in each group can continuously adjust their attributes, such as position, velocity, etc. Moreover, pedestrians optimize their path independently toward the desired goal points while avoiding obstacles and other pedestrians in the scene. At the end of this part of the thesis, a statistical analysis is carried out to evaluate the performance of the proposed method for simulating the crowd movement in the virtual environment. The proposed method implemented for several simulation scenarios under a variety of conditions for a wide range of different parameters. The results showed that the proposed method is capable of describing pedestrian' behaviours in the virtual environment

Navigating Through Virtual Worlds: From Single Characters to Large Crowds

With the rise and success of digital games over the past few decades, path planning algorithms have become an important aspect in modern game development for all types of genres. Indirectly-controlled playable characters as well as non-player characters have to find their way through the game's environment to reach their goal destinations. Modern gaming hardware and new algorithms enable the simulation of large crowds with thousands of individual characters. Still, the task of generating feasible and believable paths in a time- and storage-efficient way is a big challenge in this emerging and exciting research field. In this chapter, the authors describe classical algorithms and data structures, as well as recent approaches that enable the simulation of new and immersive features related to path planning and crowd simulation in modern games. The authors discuss the pros and cons of such algorithms, give an overview of current research questions and show why graph-based methods will soon be replaced by novel approaches that work on a surface-based representation of the environment

SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces

We present SocialGym 2, a multi-agent navigation simulator for social robot research. Our simulator models multiple autonomous agents, replicating real-world dynamics in complex environments, including doorways, hallways, intersections, and roundabouts. Unlike traditional simulators that concentrate on single robots with basic kinematic constraints in open spaces, SocialGym 2 employs multi-agent reinforcement learning (MARL) to develop optimal navigation policies for multiple robots with diverse, dynamic constraints in complex environments. Built on the PettingZoo MARL library and Stable Baselines3 API, SocialGym 2 offers an accessible python interface that integrates with a navigation stack through ROS messaging. SocialGym 2 can be easily installed and is packaged in a docker container, and it provides the capability to swap and evaluate different MARL algorithms, as well as customize observation and reward functions. We also provide scripts to allow users to create their own environments and have conducted benchmarks using various social navigation algorithms, reporting a broad range of social navigation metrics. Projected hosted at: https://amrl.cs.utexas.edu/social_gym/index.htmlComment: Submitted to RSS 202

Optimal Acceleration-Velocity-Bounded Trajectory Planning in Dynamic Crowd Simulation

Creating complex and realistic crowd behaviors, such as pedestrian navigation behavior with dynamic obstacles, is a difficult and time consuming task. In this paper, we study one special type of crowd which is composed of urgent individuals, normal individuals, and normal groups. We use three steps to construct the crowd simulation in dynamic environment. The first one is that the urgent individuals move forward along a given path around dynamic obstacles and other crowd members. An optimal acceleration-velocity-bounded trajectory planning method is utilized to model their behaviors, which ensures that the durations of the generated trajectories are minimal and the urgent individuals are collision-free with dynamic obstacles (e.g., dynamic vehicles). In the second step, a pushing model is adopted to simulate the interactions between urgent members and normal ones, which ensures that the computational cost of the optimal trajectory planning is acceptable. The third step is obligated to imitate the interactions among normal members using collision avoidance behavior and flocking behavior. Various simulation results demonstrate that these three steps give realistic crowd phenomenon just like the real world